Fusion Simulations, data visualization and future requirements for the interactive grid infraestructure F.
Download ReportTranscript Fusion Simulations, data visualization and future requirements for the interactive grid infraestructure F.
Slide 1
Fusion Simulations, data visualization
and future requirements for the
interactive grid infraestructure
F. Castejón1, D. López Bruna1, J.M. Reynolds2, A. Tarancón2,
R. Vallés2, J.L. Velasco2
1.
2.
Instituto de Biocomputación y Física de Sistemas Complejos
Laboratorio nacional de Fusión-Asociación Euratom/Ciemat
28040 Madrid, Spain.
WWW on Fusion
What is Fusion?
• Increasing the temperature of a gas, we get a plasma state
• At this temperature, the union of light atom nuclei is possible
through an exothermal process:
Mass after fusion process less than before it
Exceeding mass -> energy
GAS
High
Temperatures
PLASMA
Why Fusion?
• Long-term safe and enviromentally friendly
• Delocalized fuel: water and lithium
When Fusion?
• ITER will produce about 400MW of power excess, and it is the
previous step to DEMO, a demostration reactor, the last stage
(50 years)
THEORETICAL MODELS
PLASMA SIMULATION
Why?
• Reach a better understanding of plasma physics is
necessary to obtain good models
• Not expensive, compared with the experiment
• Very Flexible
What?
• Under several non-restrictive assumptions, the
plasma is modeled by the equations:
• Guiding center aproximation
• Evolution of the distribution function for every specie:
Convection terms
Collision terms
Solving GC plasma equation
The parameters are:
• Magnetic field (supose fixed)
• Electric field (for the moment supose fixed)
The only aditional non linearity are the collision
terms
Even assuming all previous simplifications, it is
not possible to obtain a general analytical
solution in the 5D space.
It is neccesary to obtain numerical solutions. Two
possible ways are:
• Langevin approach
• Direct approach
Langevin Approach: ISDEP
Linealizing the collision term:
• Equivalent to applying a thermal
bath
• Then, equation has a FockerPlanck equivalent:
• This is, the evolution of the distribution function is equivalent
to solve many independent test particles trajectories
collisioning with a fixed background caracterized through noise
terms.
Relatively easy to gridify (no communication between
particles)
Direct aproach
Solve directly the equation to get the distribution
function:
• Get a magnetic geometry adapted meshing in 3D spatial
dimensions.
• Get a truncated spectral expansion of the DF in space and
velocity:
If mesh is shared between computation nodes, the
parallelization is not trivial, not as easy to gridify
IMPLEMENTATION
Adaptation of IVISDEP TO INT.EU.GRID
Use case:Opengl+GLUT+MPI+i2glogin+
Gvid+Migrating Desktop
IVISDEP running on int2grid.
Different instances of IVISDEP running on
the grid and visualized using MD.
Direct Approach
Not implemented, only proposed.
Viability:
Computation: O(Nv2 Np2)
Transmision (Nv1/3 +1)2 Np
Direct Approach: An example
100 modes * 16 data * 8 bytes/data =
12.8 kbytes/surface
100 Hex (100x27 modes)
100 Hex (100x27 modes)
(1 sec/iteration)
(1 sec/iteration)
Node A
12.8kbytes *100 Hex=1.2 MB/s
Node B
Total bandwidth/node=2.4MB/s full duplex
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6
Node 7
Data management and
visualization on the grid
Analyze and visualize the big amount of data
generated (more than 100GB per simulation):
• If looking for something it is not easy what to look at.
• So, ideally, the more you can see the more you can find.
Data distributed around the SE on the grid.
Could be possible for the user to:
• Interact with the remotelly stored data
• Analyze data in real time on the grid
• Visualize a complex graphical representation generated on
the grid
Proposed model
Conclusions
Grid has demostrated to be useful for the
Fusion community through developed
INT.EU.GRID applications (IVISDEP)
Next applications seem to be more parallel
demanding codes. Here we propose one
We propose too an interactive data
visualization and management application
for scientific results analysis
Thank you!
Questions?
Slide 2
Fusion Simulations, data visualization
and future requirements for the
interactive grid infraestructure
F. Castejón1, D. López Bruna1, J.M. Reynolds2, A. Tarancón2,
R. Vallés2, J.L. Velasco2
1.
2.
Instituto de Biocomputación y Física de Sistemas Complejos
Laboratorio nacional de Fusión-Asociación Euratom/Ciemat
28040 Madrid, Spain.
WWW on Fusion
What is Fusion?
• Increasing the temperature of a gas, we get a plasma state
• At this temperature, the union of light atom nuclei is possible
through an exothermal process:
Mass after fusion process less than before it
Exceeding mass -> energy
GAS
High
Temperatures
PLASMA
Why Fusion?
• Long-term safe and enviromentally friendly
• Delocalized fuel: water and lithium
When Fusion?
• ITER will produce about 400MW of power excess, and it is the
previous step to DEMO, a demostration reactor, the last stage
(50 years)
THEORETICAL MODELS
PLASMA SIMULATION
Why?
• Reach a better understanding of plasma physics is
necessary to obtain good models
• Not expensive, compared with the experiment
• Very Flexible
What?
• Under several non-restrictive assumptions, the
plasma is modeled by the equations:
• Guiding center aproximation
• Evolution of the distribution function for every specie:
Convection terms
Collision terms
Solving GC plasma equation
The parameters are:
• Magnetic field (supose fixed)
• Electric field (for the moment supose fixed)
The only aditional non linearity are the collision
terms
Even assuming all previous simplifications, it is
not possible to obtain a general analytical
solution in the 5D space.
It is neccesary to obtain numerical solutions. Two
possible ways are:
• Langevin approach
• Direct approach
Langevin Approach: ISDEP
Linealizing the collision term:
• Equivalent to applying a thermal
bath
• Then, equation has a FockerPlanck equivalent:
• This is, the evolution of the distribution function is equivalent
to solve many independent test particles trajectories
collisioning with a fixed background caracterized through noise
terms.
Relatively easy to gridify (no communication between
particles)
Direct aproach
Solve directly the equation to get the distribution
function:
• Get a magnetic geometry adapted meshing in 3D spatial
dimensions.
• Get a truncated spectral expansion of the DF in space and
velocity:
If mesh is shared between computation nodes, the
parallelization is not trivial, not as easy to gridify
IMPLEMENTATION
Adaptation of IVISDEP TO INT.EU.GRID
Use case:Opengl+GLUT+MPI+i2glogin+
Gvid+Migrating Desktop
IVISDEP running on int2grid.
Different instances of IVISDEP running on
the grid and visualized using MD.
Direct Approach
Not implemented, only proposed.
Viability:
Computation: O(Nv2 Np2)
Transmision (Nv1/3 +1)2 Np
Direct Approach: An example
100 modes * 16 data * 8 bytes/data =
12.8 kbytes/surface
100 Hex (100x27 modes)
100 Hex (100x27 modes)
(1 sec/iteration)
(1 sec/iteration)
Node A
12.8kbytes *100 Hex=1.2 MB/s
Node B
Total bandwidth/node=2.4MB/s full duplex
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6
Node 7
Data management and
visualization on the grid
Analyze and visualize the big amount of data
generated (more than 100GB per simulation):
• If looking for something it is not easy what to look at.
• So, ideally, the more you can see the more you can find.
Data distributed around the SE on the grid.
Could be possible for the user to:
• Interact with the remotelly stored data
• Analyze data in real time on the grid
• Visualize a complex graphical representation generated on
the grid
Proposed model
Conclusions
Grid has demostrated to be useful for the
Fusion community through developed
INT.EU.GRID applications (IVISDEP)
Next applications seem to be more parallel
demanding codes. Here we propose one
We propose too an interactive data
visualization and management application
for scientific results analysis
Thank you!
Questions?
Slide 3
Fusion Simulations, data visualization
and future requirements for the
interactive grid infraestructure
F. Castejón1, D. López Bruna1, J.M. Reynolds2, A. Tarancón2,
R. Vallés2, J.L. Velasco2
1.
2.
Instituto de Biocomputación y Física de Sistemas Complejos
Laboratorio nacional de Fusión-Asociación Euratom/Ciemat
28040 Madrid, Spain.
WWW on Fusion
What is Fusion?
• Increasing the temperature of a gas, we get a plasma state
• At this temperature, the union of light atom nuclei is possible
through an exothermal process:
Mass after fusion process less than before it
Exceeding mass -> energy
GAS
High
Temperatures
PLASMA
Why Fusion?
• Long-term safe and enviromentally friendly
• Delocalized fuel: water and lithium
When Fusion?
• ITER will produce about 400MW of power excess, and it is the
previous step to DEMO, a demostration reactor, the last stage
(50 years)
THEORETICAL MODELS
PLASMA SIMULATION
Why?
• Reach a better understanding of plasma physics is
necessary to obtain good models
• Not expensive, compared with the experiment
• Very Flexible
What?
• Under several non-restrictive assumptions, the
plasma is modeled by the equations:
• Guiding center aproximation
• Evolution of the distribution function for every specie:
Convection terms
Collision terms
Solving GC plasma equation
The parameters are:
• Magnetic field (supose fixed)
• Electric field (for the moment supose fixed)
The only aditional non linearity are the collision
terms
Even assuming all previous simplifications, it is
not possible to obtain a general analytical
solution in the 5D space.
It is neccesary to obtain numerical solutions. Two
possible ways are:
• Langevin approach
• Direct approach
Langevin Approach: ISDEP
Linealizing the collision term:
• Equivalent to applying a thermal
bath
• Then, equation has a FockerPlanck equivalent:
• This is, the evolution of the distribution function is equivalent
to solve many independent test particles trajectories
collisioning with a fixed background caracterized through noise
terms.
Relatively easy to gridify (no communication between
particles)
Direct aproach
Solve directly the equation to get the distribution
function:
• Get a magnetic geometry adapted meshing in 3D spatial
dimensions.
• Get a truncated spectral expansion of the DF in space and
velocity:
If mesh is shared between computation nodes, the
parallelization is not trivial, not as easy to gridify
IMPLEMENTATION
Adaptation of IVISDEP TO INT.EU.GRID
Use case:Opengl+GLUT+MPI+i2glogin+
Gvid+Migrating Desktop
IVISDEP running on int2grid.
Different instances of IVISDEP running on
the grid and visualized using MD.
Direct Approach
Not implemented, only proposed.
Viability:
Computation: O(Nv2 Np2)
Transmision (Nv1/3 +1)2 Np
Direct Approach: An example
100 modes * 16 data * 8 bytes/data =
12.8 kbytes/surface
100 Hex (100x27 modes)
100 Hex (100x27 modes)
(1 sec/iteration)
(1 sec/iteration)
Node A
12.8kbytes *100 Hex=1.2 MB/s
Node B
Total bandwidth/node=2.4MB/s full duplex
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6
Node 7
Data management and
visualization on the grid
Analyze and visualize the big amount of data
generated (more than 100GB per simulation):
• If looking for something it is not easy what to look at.
• So, ideally, the more you can see the more you can find.
Data distributed around the SE on the grid.
Could be possible for the user to:
• Interact with the remotelly stored data
• Analyze data in real time on the grid
• Visualize a complex graphical representation generated on
the grid
Proposed model
Conclusions
Grid has demostrated to be useful for the
Fusion community through developed
INT.EU.GRID applications (IVISDEP)
Next applications seem to be more parallel
demanding codes. Here we propose one
We propose too an interactive data
visualization and management application
for scientific results analysis
Thank you!
Questions?
Slide 4
Fusion Simulations, data visualization
and future requirements for the
interactive grid infraestructure
F. Castejón1, D. López Bruna1, J.M. Reynolds2, A. Tarancón2,
R. Vallés2, J.L. Velasco2
1.
2.
Instituto de Biocomputación y Física de Sistemas Complejos
Laboratorio nacional de Fusión-Asociación Euratom/Ciemat
28040 Madrid, Spain.
WWW on Fusion
What is Fusion?
• Increasing the temperature of a gas, we get a plasma state
• At this temperature, the union of light atom nuclei is possible
through an exothermal process:
Mass after fusion process less than before it
Exceeding mass -> energy
GAS
High
Temperatures
PLASMA
Why Fusion?
• Long-term safe and enviromentally friendly
• Delocalized fuel: water and lithium
When Fusion?
• ITER will produce about 400MW of power excess, and it is the
previous step to DEMO, a demostration reactor, the last stage
(50 years)
THEORETICAL MODELS
PLASMA SIMULATION
Why?
• Reach a better understanding of plasma physics is
necessary to obtain good models
• Not expensive, compared with the experiment
• Very Flexible
What?
• Under several non-restrictive assumptions, the
plasma is modeled by the equations:
• Guiding center aproximation
• Evolution of the distribution function for every specie:
Convection terms
Collision terms
Solving GC plasma equation
The parameters are:
• Magnetic field (supose fixed)
• Electric field (for the moment supose fixed)
The only aditional non linearity are the collision
terms
Even assuming all previous simplifications, it is
not possible to obtain a general analytical
solution in the 5D space.
It is neccesary to obtain numerical solutions. Two
possible ways are:
• Langevin approach
• Direct approach
Langevin Approach: ISDEP
Linealizing the collision term:
• Equivalent to applying a thermal
bath
• Then, equation has a FockerPlanck equivalent:
• This is, the evolution of the distribution function is equivalent
to solve many independent test particles trajectories
collisioning with a fixed background caracterized through noise
terms.
Relatively easy to gridify (no communication between
particles)
Direct aproach
Solve directly the equation to get the distribution
function:
• Get a magnetic geometry adapted meshing in 3D spatial
dimensions.
• Get a truncated spectral expansion of the DF in space and
velocity:
If mesh is shared between computation nodes, the
parallelization is not trivial, not as easy to gridify
IMPLEMENTATION
Adaptation of IVISDEP TO INT.EU.GRID
Use case:Opengl+GLUT+MPI+i2glogin+
Gvid+Migrating Desktop
IVISDEP running on int2grid.
Different instances of IVISDEP running on
the grid and visualized using MD.
Direct Approach
Not implemented, only proposed.
Viability:
Computation: O(Nv2 Np2)
Transmision (Nv1/3 +1)2 Np
Direct Approach: An example
100 modes * 16 data * 8 bytes/data =
12.8 kbytes/surface
100 Hex (100x27 modes)
100 Hex (100x27 modes)
(1 sec/iteration)
(1 sec/iteration)
Node A
12.8kbytes *100 Hex=1.2 MB/s
Node B
Total bandwidth/node=2.4MB/s full duplex
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6
Node 7
Data management and
visualization on the grid
Analyze and visualize the big amount of data
generated (more than 100GB per simulation):
• If looking for something it is not easy what to look at.
• So, ideally, the more you can see the more you can find.
Data distributed around the SE on the grid.
Could be possible for the user to:
• Interact with the remotelly stored data
• Analyze data in real time on the grid
• Visualize a complex graphical representation generated on
the grid
Proposed model
Conclusions
Grid has demostrated to be useful for the
Fusion community through developed
INT.EU.GRID applications (IVISDEP)
Next applications seem to be more parallel
demanding codes. Here we propose one
We propose too an interactive data
visualization and management application
for scientific results analysis
Thank you!
Questions?
Slide 5
Fusion Simulations, data visualization
and future requirements for the
interactive grid infraestructure
F. Castejón1, D. López Bruna1, J.M. Reynolds2, A. Tarancón2,
R. Vallés2, J.L. Velasco2
1.
2.
Instituto de Biocomputación y Física de Sistemas Complejos
Laboratorio nacional de Fusión-Asociación Euratom/Ciemat
28040 Madrid, Spain.
WWW on Fusion
What is Fusion?
• Increasing the temperature of a gas, we get a plasma state
• At this temperature, the union of light atom nuclei is possible
through an exothermal process:
Mass after fusion process less than before it
Exceeding mass -> energy
GAS
High
Temperatures
PLASMA
Why Fusion?
• Long-term safe and enviromentally friendly
• Delocalized fuel: water and lithium
When Fusion?
• ITER will produce about 400MW of power excess, and it is the
previous step to DEMO, a demostration reactor, the last stage
(50 years)
THEORETICAL MODELS
PLASMA SIMULATION
Why?
• Reach a better understanding of plasma physics is
necessary to obtain good models
• Not expensive, compared with the experiment
• Very Flexible
What?
• Under several non-restrictive assumptions, the
plasma is modeled by the equations:
• Guiding center aproximation
• Evolution of the distribution function for every specie:
Convection terms
Collision terms
Solving GC plasma equation
The parameters are:
• Magnetic field (supose fixed)
• Electric field (for the moment supose fixed)
The only aditional non linearity are the collision
terms
Even assuming all previous simplifications, it is
not possible to obtain a general analytical
solution in the 5D space.
It is neccesary to obtain numerical solutions. Two
possible ways are:
• Langevin approach
• Direct approach
Langevin Approach: ISDEP
Linealizing the collision term:
• Equivalent to applying a thermal
bath
• Then, equation has a FockerPlanck equivalent:
• This is, the evolution of the distribution function is equivalent
to solve many independent test particles trajectories
collisioning with a fixed background caracterized through noise
terms.
Relatively easy to gridify (no communication between
particles)
Direct aproach
Solve directly the equation to get the distribution
function:
• Get a magnetic geometry adapted meshing in 3D spatial
dimensions.
• Get a truncated spectral expansion of the DF in space and
velocity:
If mesh is shared between computation nodes, the
parallelization is not trivial, not as easy to gridify
IMPLEMENTATION
Adaptation of IVISDEP TO INT.EU.GRID
Use case:Opengl+GLUT+MPI+i2glogin+
Gvid+Migrating Desktop
IVISDEP running on int2grid.
Different instances of IVISDEP running on
the grid and visualized using MD.
Direct Approach
Not implemented, only proposed.
Viability:
Computation: O(Nv2 Np2)
Transmision (Nv1/3 +1)2 Np
Direct Approach: An example
100 modes * 16 data * 8 bytes/data =
12.8 kbytes/surface
100 Hex (100x27 modes)
100 Hex (100x27 modes)
(1 sec/iteration)
(1 sec/iteration)
Node A
12.8kbytes *100 Hex=1.2 MB/s
Node B
Total bandwidth/node=2.4MB/s full duplex
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6
Node 7
Data management and
visualization on the grid
Analyze and visualize the big amount of data
generated (more than 100GB per simulation):
• If looking for something it is not easy what to look at.
• So, ideally, the more you can see the more you can find.
Data distributed around the SE on the grid.
Could be possible for the user to:
• Interact with the remotelly stored data
• Analyze data in real time on the grid
• Visualize a complex graphical representation generated on
the grid
Proposed model
Conclusions
Grid has demostrated to be useful for the
Fusion community through developed
INT.EU.GRID applications (IVISDEP)
Next applications seem to be more parallel
demanding codes. Here we propose one
We propose too an interactive data
visualization and management application
for scientific results analysis
Thank you!
Questions?
Slide 6
Fusion Simulations, data visualization
and future requirements for the
interactive grid infraestructure
F. Castejón1, D. López Bruna1, J.M. Reynolds2, A. Tarancón2,
R. Vallés2, J.L. Velasco2
1.
2.
Instituto de Biocomputación y Física de Sistemas Complejos
Laboratorio nacional de Fusión-Asociación Euratom/Ciemat
28040 Madrid, Spain.
WWW on Fusion
What is Fusion?
• Increasing the temperature of a gas, we get a plasma state
• At this temperature, the union of light atom nuclei is possible
through an exothermal process:
Mass after fusion process less than before it
Exceeding mass -> energy
GAS
High
Temperatures
PLASMA
Why Fusion?
• Long-term safe and enviromentally friendly
• Delocalized fuel: water and lithium
When Fusion?
• ITER will produce about 400MW of power excess, and it is the
previous step to DEMO, a demostration reactor, the last stage
(50 years)
THEORETICAL MODELS
PLASMA SIMULATION
Why?
• Reach a better understanding of plasma physics is
necessary to obtain good models
• Not expensive, compared with the experiment
• Very Flexible
What?
• Under several non-restrictive assumptions, the
plasma is modeled by the equations:
• Guiding center aproximation
• Evolution of the distribution function for every specie:
Convection terms
Collision terms
Solving GC plasma equation
The parameters are:
• Magnetic field (supose fixed)
• Electric field (for the moment supose fixed)
The only aditional non linearity are the collision
terms
Even assuming all previous simplifications, it is
not possible to obtain a general analytical
solution in the 5D space.
It is neccesary to obtain numerical solutions. Two
possible ways are:
• Langevin approach
• Direct approach
Langevin Approach: ISDEP
Linealizing the collision term:
• Equivalent to applying a thermal
bath
• Then, equation has a FockerPlanck equivalent:
• This is, the evolution of the distribution function is equivalent
to solve many independent test particles trajectories
collisioning with a fixed background caracterized through noise
terms.
Relatively easy to gridify (no communication between
particles)
Direct aproach
Solve directly the equation to get the distribution
function:
• Get a magnetic geometry adapted meshing in 3D spatial
dimensions.
• Get a truncated spectral expansion of the DF in space and
velocity:
If mesh is shared between computation nodes, the
parallelization is not trivial, not as easy to gridify
IMPLEMENTATION
Adaptation of IVISDEP TO INT.EU.GRID
Use case:Opengl+GLUT+MPI+i2glogin+
Gvid+Migrating Desktop
IVISDEP running on int2grid.
Different instances of IVISDEP running on
the grid and visualized using MD.
Direct Approach
Not implemented, only proposed.
Viability:
Computation: O(Nv2 Np2)
Transmision (Nv1/3 +1)2 Np
Direct Approach: An example
100 modes * 16 data * 8 bytes/data =
12.8 kbytes/surface
100 Hex (100x27 modes)
100 Hex (100x27 modes)
(1 sec/iteration)
(1 sec/iteration)
Node A
12.8kbytes *100 Hex=1.2 MB/s
Node B
Total bandwidth/node=2.4MB/s full duplex
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6
Node 7
Data management and
visualization on the grid
Analyze and visualize the big amount of data
generated (more than 100GB per simulation):
• If looking for something it is not easy what to look at.
• So, ideally, the more you can see the more you can find.
Data distributed around the SE on the grid.
Could be possible for the user to:
• Interact with the remotelly stored data
• Analyze data in real time on the grid
• Visualize a complex graphical representation generated on
the grid
Proposed model
Conclusions
Grid has demostrated to be useful for the
Fusion community through developed
INT.EU.GRID applications (IVISDEP)
Next applications seem to be more parallel
demanding codes. Here we propose one
We propose too an interactive data
visualization and management application
for scientific results analysis
Thank you!
Questions?
Slide 7
Fusion Simulations, data visualization
and future requirements for the
interactive grid infraestructure
F. Castejón1, D. López Bruna1, J.M. Reynolds2, A. Tarancón2,
R. Vallés2, J.L. Velasco2
1.
2.
Instituto de Biocomputación y Física de Sistemas Complejos
Laboratorio nacional de Fusión-Asociación Euratom/Ciemat
28040 Madrid, Spain.
WWW on Fusion
What is Fusion?
• Increasing the temperature of a gas, we get a plasma state
• At this temperature, the union of light atom nuclei is possible
through an exothermal process:
Mass after fusion process less than before it
Exceeding mass -> energy
GAS
High
Temperatures
PLASMA
Why Fusion?
• Long-term safe and enviromentally friendly
• Delocalized fuel: water and lithium
When Fusion?
• ITER will produce about 400MW of power excess, and it is the
previous step to DEMO, a demostration reactor, the last stage
(50 years)
THEORETICAL MODELS
PLASMA SIMULATION
Why?
• Reach a better understanding of plasma physics is
necessary to obtain good models
• Not expensive, compared with the experiment
• Very Flexible
What?
• Under several non-restrictive assumptions, the
plasma is modeled by the equations:
• Guiding center aproximation
• Evolution of the distribution function for every specie:
Convection terms
Collision terms
Solving GC plasma equation
The parameters are:
• Magnetic field (supose fixed)
• Electric field (for the moment supose fixed)
The only aditional non linearity are the collision
terms
Even assuming all previous simplifications, it is
not possible to obtain a general analytical
solution in the 5D space.
It is neccesary to obtain numerical solutions. Two
possible ways are:
• Langevin approach
• Direct approach
Langevin Approach: ISDEP
Linealizing the collision term:
• Equivalent to applying a thermal
bath
• Then, equation has a FockerPlanck equivalent:
• This is, the evolution of the distribution function is equivalent
to solve many independent test particles trajectories
collisioning with a fixed background caracterized through noise
terms.
Relatively easy to gridify (no communication between
particles)
Direct aproach
Solve directly the equation to get the distribution
function:
• Get a magnetic geometry adapted meshing in 3D spatial
dimensions.
• Get a truncated spectral expansion of the DF in space and
velocity:
If mesh is shared between computation nodes, the
parallelization is not trivial, not as easy to gridify
IMPLEMENTATION
Adaptation of IVISDEP TO INT.EU.GRID
Use case:Opengl+GLUT+MPI+i2glogin+
Gvid+Migrating Desktop
IVISDEP running on int2grid.
Different instances of IVISDEP running on
the grid and visualized using MD.
Direct Approach
Not implemented, only proposed.
Viability:
Computation: O(Nv2 Np2)
Transmision (Nv1/3 +1)2 Np
Direct Approach: An example
100 modes * 16 data * 8 bytes/data =
12.8 kbytes/surface
100 Hex (100x27 modes)
100 Hex (100x27 modes)
(1 sec/iteration)
(1 sec/iteration)
Node A
12.8kbytes *100 Hex=1.2 MB/s
Node B
Total bandwidth/node=2.4MB/s full duplex
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6
Node 7
Data management and
visualization on the grid
Analyze and visualize the big amount of data
generated (more than 100GB per simulation):
• If looking for something it is not easy what to look at.
• So, ideally, the more you can see the more you can find.
Data distributed around the SE on the grid.
Could be possible for the user to:
• Interact with the remotelly stored data
• Analyze data in real time on the grid
• Visualize a complex graphical representation generated on
the grid
Proposed model
Conclusions
Grid has demostrated to be useful for the
Fusion community through developed
INT.EU.GRID applications (IVISDEP)
Next applications seem to be more parallel
demanding codes. Here we propose one
We propose too an interactive data
visualization and management application
for scientific results analysis
Thank you!
Questions?
Slide 8
Fusion Simulations, data visualization
and future requirements for the
interactive grid infraestructure
F. Castejón1, D. López Bruna1, J.M. Reynolds2, A. Tarancón2,
R. Vallés2, J.L. Velasco2
1.
2.
Instituto de Biocomputación y Física de Sistemas Complejos
Laboratorio nacional de Fusión-Asociación Euratom/Ciemat
28040 Madrid, Spain.
WWW on Fusion
What is Fusion?
• Increasing the temperature of a gas, we get a plasma state
• At this temperature, the union of light atom nuclei is possible
through an exothermal process:
Mass after fusion process less than before it
Exceeding mass -> energy
GAS
High
Temperatures
PLASMA
Why Fusion?
• Long-term safe and enviromentally friendly
• Delocalized fuel: water and lithium
When Fusion?
• ITER will produce about 400MW of power excess, and it is the
previous step to DEMO, a demostration reactor, the last stage
(50 years)
THEORETICAL MODELS
PLASMA SIMULATION
Why?
• Reach a better understanding of plasma physics is
necessary to obtain good models
• Not expensive, compared with the experiment
• Very Flexible
What?
• Under several non-restrictive assumptions, the
plasma is modeled by the equations:
• Guiding center aproximation
• Evolution of the distribution function for every specie:
Convection terms
Collision terms
Solving GC plasma equation
The parameters are:
• Magnetic field (supose fixed)
• Electric field (for the moment supose fixed)
The only aditional non linearity are the collision
terms
Even assuming all previous simplifications, it is
not possible to obtain a general analytical
solution in the 5D space.
It is neccesary to obtain numerical solutions. Two
possible ways are:
• Langevin approach
• Direct approach
Langevin Approach: ISDEP
Linealizing the collision term:
• Equivalent to applying a thermal
bath
• Then, equation has a FockerPlanck equivalent:
• This is, the evolution of the distribution function is equivalent
to solve many independent test particles trajectories
collisioning with a fixed background caracterized through noise
terms.
Relatively easy to gridify (no communication between
particles)
Direct aproach
Solve directly the equation to get the distribution
function:
• Get a magnetic geometry adapted meshing in 3D spatial
dimensions.
• Get a truncated spectral expansion of the DF in space and
velocity:
If mesh is shared between computation nodes, the
parallelization is not trivial, not as easy to gridify
IMPLEMENTATION
Adaptation of IVISDEP TO INT.EU.GRID
Use case:Opengl+GLUT+MPI+i2glogin+
Gvid+Migrating Desktop
IVISDEP running on int2grid.
Different instances of IVISDEP running on
the grid and visualized using MD.
Direct Approach
Not implemented, only proposed.
Viability:
Computation: O(Nv2 Np2)
Transmision (Nv1/3 +1)2 Np
Direct Approach: An example
100 modes * 16 data * 8 bytes/data =
12.8 kbytes/surface
100 Hex (100x27 modes)
100 Hex (100x27 modes)
(1 sec/iteration)
(1 sec/iteration)
Node A
12.8kbytes *100 Hex=1.2 MB/s
Node B
Total bandwidth/node=2.4MB/s full duplex
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6
Node 7
Data management and
visualization on the grid
Analyze and visualize the big amount of data
generated (more than 100GB per simulation):
• If looking for something it is not easy what to look at.
• So, ideally, the more you can see the more you can find.
Data distributed around the SE on the grid.
Could be possible for the user to:
• Interact with the remotelly stored data
• Analyze data in real time on the grid
• Visualize a complex graphical representation generated on
the grid
Proposed model
Conclusions
Grid has demostrated to be useful for the
Fusion community through developed
INT.EU.GRID applications (IVISDEP)
Next applications seem to be more parallel
demanding codes. Here we propose one
We propose too an interactive data
visualization and management application
for scientific results analysis
Thank you!
Questions?
Slide 9
Fusion Simulations, data visualization
and future requirements for the
interactive grid infraestructure
F. Castejón1, D. López Bruna1, J.M. Reynolds2, A. Tarancón2,
R. Vallés2, J.L. Velasco2
1.
2.
Instituto de Biocomputación y Física de Sistemas Complejos
Laboratorio nacional de Fusión-Asociación Euratom/Ciemat
28040 Madrid, Spain.
WWW on Fusion
What is Fusion?
• Increasing the temperature of a gas, we get a plasma state
• At this temperature, the union of light atom nuclei is possible
through an exothermal process:
Mass after fusion process less than before it
Exceeding mass -> energy
GAS
High
Temperatures
PLASMA
Why Fusion?
• Long-term safe and enviromentally friendly
• Delocalized fuel: water and lithium
When Fusion?
• ITER will produce about 400MW of power excess, and it is the
previous step to DEMO, a demostration reactor, the last stage
(50 years)
THEORETICAL MODELS
PLASMA SIMULATION
Why?
• Reach a better understanding of plasma physics is
necessary to obtain good models
• Not expensive, compared with the experiment
• Very Flexible
What?
• Under several non-restrictive assumptions, the
plasma is modeled by the equations:
• Guiding center aproximation
• Evolution of the distribution function for every specie:
Convection terms
Collision terms
Solving GC plasma equation
The parameters are:
• Magnetic field (supose fixed)
• Electric field (for the moment supose fixed)
The only aditional non linearity are the collision
terms
Even assuming all previous simplifications, it is
not possible to obtain a general analytical
solution in the 5D space.
It is neccesary to obtain numerical solutions. Two
possible ways are:
• Langevin approach
• Direct approach
Langevin Approach: ISDEP
Linealizing the collision term:
• Equivalent to applying a thermal
bath
• Then, equation has a FockerPlanck equivalent:
• This is, the evolution of the distribution function is equivalent
to solve many independent test particles trajectories
collisioning with a fixed background caracterized through noise
terms.
Relatively easy to gridify (no communication between
particles)
Direct aproach
Solve directly the equation to get the distribution
function:
• Get a magnetic geometry adapted meshing in 3D spatial
dimensions.
• Get a truncated spectral expansion of the DF in space and
velocity:
If mesh is shared between computation nodes, the
parallelization is not trivial, not as easy to gridify
IMPLEMENTATION
Adaptation of IVISDEP TO INT.EU.GRID
Use case:Opengl+GLUT+MPI+i2glogin+
Gvid+Migrating Desktop
IVISDEP running on int2grid.
Different instances of IVISDEP running on
the grid and visualized using MD.
Direct Approach
Not implemented, only proposed.
Viability:
Computation: O(Nv2 Np2)
Transmision (Nv1/3 +1)2 Np
Direct Approach: An example
100 modes * 16 data * 8 bytes/data =
12.8 kbytes/surface
100 Hex (100x27 modes)
100 Hex (100x27 modes)
(1 sec/iteration)
(1 sec/iteration)
Node A
12.8kbytes *100 Hex=1.2 MB/s
Node B
Total bandwidth/node=2.4MB/s full duplex
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6
Node 7
Data management and
visualization on the grid
Analyze and visualize the big amount of data
generated (more than 100GB per simulation):
• If looking for something it is not easy what to look at.
• So, ideally, the more you can see the more you can find.
Data distributed around the SE on the grid.
Could be possible for the user to:
• Interact with the remotelly stored data
• Analyze data in real time on the grid
• Visualize a complex graphical representation generated on
the grid
Proposed model
Conclusions
Grid has demostrated to be useful for the
Fusion community through developed
INT.EU.GRID applications (IVISDEP)
Next applications seem to be more parallel
demanding codes. Here we propose one
We propose too an interactive data
visualization and management application
for scientific results analysis
Thank you!
Questions?
Slide 10
Fusion Simulations, data visualization
and future requirements for the
interactive grid infraestructure
F. Castejón1, D. López Bruna1, J.M. Reynolds2, A. Tarancón2,
R. Vallés2, J.L. Velasco2
1.
2.
Instituto de Biocomputación y Física de Sistemas Complejos
Laboratorio nacional de Fusión-Asociación Euratom/Ciemat
28040 Madrid, Spain.
WWW on Fusion
What is Fusion?
• Increasing the temperature of a gas, we get a plasma state
• At this temperature, the union of light atom nuclei is possible
through an exothermal process:
Mass after fusion process less than before it
Exceeding mass -> energy
GAS
High
Temperatures
PLASMA
Why Fusion?
• Long-term safe and enviromentally friendly
• Delocalized fuel: water and lithium
When Fusion?
• ITER will produce about 400MW of power excess, and it is the
previous step to DEMO, a demostration reactor, the last stage
(50 years)
THEORETICAL MODELS
PLASMA SIMULATION
Why?
• Reach a better understanding of plasma physics is
necessary to obtain good models
• Not expensive, compared with the experiment
• Very Flexible
What?
• Under several non-restrictive assumptions, the
plasma is modeled by the equations:
• Guiding center aproximation
• Evolution of the distribution function for every specie:
Convection terms
Collision terms
Solving GC plasma equation
The parameters are:
• Magnetic field (supose fixed)
• Electric field (for the moment supose fixed)
The only aditional non linearity are the collision
terms
Even assuming all previous simplifications, it is
not possible to obtain a general analytical
solution in the 5D space.
It is neccesary to obtain numerical solutions. Two
possible ways are:
• Langevin approach
• Direct approach
Langevin Approach: ISDEP
Linealizing the collision term:
• Equivalent to applying a thermal
bath
• Then, equation has a FockerPlanck equivalent:
• This is, the evolution of the distribution function is equivalent
to solve many independent test particles trajectories
collisioning with a fixed background caracterized through noise
terms.
Relatively easy to gridify (no communication between
particles)
Direct aproach
Solve directly the equation to get the distribution
function:
• Get a magnetic geometry adapted meshing in 3D spatial
dimensions.
• Get a truncated spectral expansion of the DF in space and
velocity:
If mesh is shared between computation nodes, the
parallelization is not trivial, not as easy to gridify
IMPLEMENTATION
Adaptation of IVISDEP TO INT.EU.GRID
Use case:Opengl+GLUT+MPI+i2glogin+
Gvid+Migrating Desktop
IVISDEP running on int2grid.
Different instances of IVISDEP running on
the grid and visualized using MD.
Direct Approach
Not implemented, only proposed.
Viability:
Computation: O(Nv2 Np2)
Transmision (Nv1/3 +1)2 Np
Direct Approach: An example
100 modes * 16 data * 8 bytes/data =
12.8 kbytes/surface
100 Hex (100x27 modes)
100 Hex (100x27 modes)
(1 sec/iteration)
(1 sec/iteration)
Node A
12.8kbytes *100 Hex=1.2 MB/s
Node B
Total bandwidth/node=2.4MB/s full duplex
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6
Node 7
Data management and
visualization on the grid
Analyze and visualize the big amount of data
generated (more than 100GB per simulation):
• If looking for something it is not easy what to look at.
• So, ideally, the more you can see the more you can find.
Data distributed around the SE on the grid.
Could be possible for the user to:
• Interact with the remotelly stored data
• Analyze data in real time on the grid
• Visualize a complex graphical representation generated on
the grid
Proposed model
Conclusions
Grid has demostrated to be useful for the
Fusion community through developed
INT.EU.GRID applications (IVISDEP)
Next applications seem to be more parallel
demanding codes. Here we propose one
We propose too an interactive data
visualization and management application
for scientific results analysis
Thank you!
Questions?
Slide 11
Fusion Simulations, data visualization
and future requirements for the
interactive grid infraestructure
F. Castejón1, D. López Bruna1, J.M. Reynolds2, A. Tarancón2,
R. Vallés2, J.L. Velasco2
1.
2.
Instituto de Biocomputación y Física de Sistemas Complejos
Laboratorio nacional de Fusión-Asociación Euratom/Ciemat
28040 Madrid, Spain.
WWW on Fusion
What is Fusion?
• Increasing the temperature of a gas, we get a plasma state
• At this temperature, the union of light atom nuclei is possible
through an exothermal process:
Mass after fusion process less than before it
Exceeding mass -> energy
GAS
High
Temperatures
PLASMA
Why Fusion?
• Long-term safe and enviromentally friendly
• Delocalized fuel: water and lithium
When Fusion?
• ITER will produce about 400MW of power excess, and it is the
previous step to DEMO, a demostration reactor, the last stage
(50 years)
THEORETICAL MODELS
PLASMA SIMULATION
Why?
• Reach a better understanding of plasma physics is
necessary to obtain good models
• Not expensive, compared with the experiment
• Very Flexible
What?
• Under several non-restrictive assumptions, the
plasma is modeled by the equations:
• Guiding center aproximation
• Evolution of the distribution function for every specie:
Convection terms
Collision terms
Solving GC plasma equation
The parameters are:
• Magnetic field (supose fixed)
• Electric field (for the moment supose fixed)
The only aditional non linearity are the collision
terms
Even assuming all previous simplifications, it is
not possible to obtain a general analytical
solution in the 5D space.
It is neccesary to obtain numerical solutions. Two
possible ways are:
• Langevin approach
• Direct approach
Langevin Approach: ISDEP
Linealizing the collision term:
• Equivalent to applying a thermal
bath
• Then, equation has a FockerPlanck equivalent:
• This is, the evolution of the distribution function is equivalent
to solve many independent test particles trajectories
collisioning with a fixed background caracterized through noise
terms.
Relatively easy to gridify (no communication between
particles)
Direct aproach
Solve directly the equation to get the distribution
function:
• Get a magnetic geometry adapted meshing in 3D spatial
dimensions.
• Get a truncated spectral expansion of the DF in space and
velocity:
If mesh is shared between computation nodes, the
parallelization is not trivial, not as easy to gridify
IMPLEMENTATION
Adaptation of IVISDEP TO INT.EU.GRID
Use case:Opengl+GLUT+MPI+i2glogin+
Gvid+Migrating Desktop
IVISDEP running on int2grid.
Different instances of IVISDEP running on
the grid and visualized using MD.
Direct Approach
Not implemented, only proposed.
Viability:
Computation: O(Nv2 Np2)
Transmision (Nv1/3 +1)2 Np
Direct Approach: An example
100 modes * 16 data * 8 bytes/data =
12.8 kbytes/surface
100 Hex (100x27 modes)
100 Hex (100x27 modes)
(1 sec/iteration)
(1 sec/iteration)
Node A
12.8kbytes *100 Hex=1.2 MB/s
Node B
Total bandwidth/node=2.4MB/s full duplex
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6
Node 7
Data management and
visualization on the grid
Analyze and visualize the big amount of data
generated (more than 100GB per simulation):
• If looking for something it is not easy what to look at.
• So, ideally, the more you can see the more you can find.
Data distributed around the SE on the grid.
Could be possible for the user to:
• Interact with the remotelly stored data
• Analyze data in real time on the grid
• Visualize a complex graphical representation generated on
the grid
Proposed model
Conclusions
Grid has demostrated to be useful for the
Fusion community through developed
INT.EU.GRID applications (IVISDEP)
Next applications seem to be more parallel
demanding codes. Here we propose one
We propose too an interactive data
visualization and management application
for scientific results analysis
Thank you!
Questions?
Slide 12
Fusion Simulations, data visualization
and future requirements for the
interactive grid infraestructure
F. Castejón1, D. López Bruna1, J.M. Reynolds2, A. Tarancón2,
R. Vallés2, J.L. Velasco2
1.
2.
Instituto de Biocomputación y Física de Sistemas Complejos
Laboratorio nacional de Fusión-Asociación Euratom/Ciemat
28040 Madrid, Spain.
WWW on Fusion
What is Fusion?
• Increasing the temperature of a gas, we get a plasma state
• At this temperature, the union of light atom nuclei is possible
through an exothermal process:
Mass after fusion process less than before it
Exceeding mass -> energy
GAS
High
Temperatures
PLASMA
Why Fusion?
• Long-term safe and enviromentally friendly
• Delocalized fuel: water and lithium
When Fusion?
• ITER will produce about 400MW of power excess, and it is the
previous step to DEMO, a demostration reactor, the last stage
(50 years)
THEORETICAL MODELS
PLASMA SIMULATION
Why?
• Reach a better understanding of plasma physics is
necessary to obtain good models
• Not expensive, compared with the experiment
• Very Flexible
What?
• Under several non-restrictive assumptions, the
plasma is modeled by the equations:
• Guiding center aproximation
• Evolution of the distribution function for every specie:
Convection terms
Collision terms
Solving GC plasma equation
The parameters are:
• Magnetic field (supose fixed)
• Electric field (for the moment supose fixed)
The only aditional non linearity are the collision
terms
Even assuming all previous simplifications, it is
not possible to obtain a general analytical
solution in the 5D space.
It is neccesary to obtain numerical solutions. Two
possible ways are:
• Langevin approach
• Direct approach
Langevin Approach: ISDEP
Linealizing the collision term:
• Equivalent to applying a thermal
bath
• Then, equation has a FockerPlanck equivalent:
• This is, the evolution of the distribution function is equivalent
to solve many independent test particles trajectories
collisioning with a fixed background caracterized through noise
terms.
Relatively easy to gridify (no communication between
particles)
Direct aproach
Solve directly the equation to get the distribution
function:
• Get a magnetic geometry adapted meshing in 3D spatial
dimensions.
• Get a truncated spectral expansion of the DF in space and
velocity:
If mesh is shared between computation nodes, the
parallelization is not trivial, not as easy to gridify
IMPLEMENTATION
Adaptation of IVISDEP TO INT.EU.GRID
Use case:Opengl+GLUT+MPI+i2glogin+
Gvid+Migrating Desktop
IVISDEP running on int2grid.
Different instances of IVISDEP running on
the grid and visualized using MD.
Direct Approach
Not implemented, only proposed.
Viability:
Computation: O(Nv2 Np2)
Transmision (Nv1/3 +1)2 Np
Direct Approach: An example
100 modes * 16 data * 8 bytes/data =
12.8 kbytes/surface
100 Hex (100x27 modes)
100 Hex (100x27 modes)
(1 sec/iteration)
(1 sec/iteration)
Node A
12.8kbytes *100 Hex=1.2 MB/s
Node B
Total bandwidth/node=2.4MB/s full duplex
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6
Node 7
Data management and
visualization on the grid
Analyze and visualize the big amount of data
generated (more than 100GB per simulation):
• If looking for something it is not easy what to look at.
• So, ideally, the more you can see the more you can find.
Data distributed around the SE on the grid.
Could be possible for the user to:
• Interact with the remotelly stored data
• Analyze data in real time on the grid
• Visualize a complex graphical representation generated on
the grid
Proposed model
Conclusions
Grid has demostrated to be useful for the
Fusion community through developed
INT.EU.GRID applications (IVISDEP)
Next applications seem to be more parallel
demanding codes. Here we propose one
We propose too an interactive data
visualization and management application
for scientific results analysis
Thank you!
Questions?
Slide 13
Fusion Simulations, data visualization
and future requirements for the
interactive grid infraestructure
F. Castejón1, D. López Bruna1, J.M. Reynolds2, A. Tarancón2,
R. Vallés2, J.L. Velasco2
1.
2.
Instituto de Biocomputación y Física de Sistemas Complejos
Laboratorio nacional de Fusión-Asociación Euratom/Ciemat
28040 Madrid, Spain.
WWW on Fusion
What is Fusion?
• Increasing the temperature of a gas, we get a plasma state
• At this temperature, the union of light atom nuclei is possible
through an exothermal process:
Mass after fusion process less than before it
Exceeding mass -> energy
GAS
High
Temperatures
PLASMA
Why Fusion?
• Long-term safe and enviromentally friendly
• Delocalized fuel: water and lithium
When Fusion?
• ITER will produce about 400MW of power excess, and it is the
previous step to DEMO, a demostration reactor, the last stage
(50 years)
THEORETICAL MODELS
PLASMA SIMULATION
Why?
• Reach a better understanding of plasma physics is
necessary to obtain good models
• Not expensive, compared with the experiment
• Very Flexible
What?
• Under several non-restrictive assumptions, the
plasma is modeled by the equations:
• Guiding center aproximation
• Evolution of the distribution function for every specie:
Convection terms
Collision terms
Solving GC plasma equation
The parameters are:
• Magnetic field (supose fixed)
• Electric field (for the moment supose fixed)
The only aditional non linearity are the collision
terms
Even assuming all previous simplifications, it is
not possible to obtain a general analytical
solution in the 5D space.
It is neccesary to obtain numerical solutions. Two
possible ways are:
• Langevin approach
• Direct approach
Langevin Approach: ISDEP
Linealizing the collision term:
• Equivalent to applying a thermal
bath
• Then, equation has a FockerPlanck equivalent:
• This is, the evolution of the distribution function is equivalent
to solve many independent test particles trajectories
collisioning with a fixed background caracterized through noise
terms.
Relatively easy to gridify (no communication between
particles)
Direct aproach
Solve directly the equation to get the distribution
function:
• Get a magnetic geometry adapted meshing in 3D spatial
dimensions.
• Get a truncated spectral expansion of the DF in space and
velocity:
If mesh is shared between computation nodes, the
parallelization is not trivial, not as easy to gridify
IMPLEMENTATION
Adaptation of IVISDEP TO INT.EU.GRID
Use case:Opengl+GLUT+MPI+i2glogin+
Gvid+Migrating Desktop
IVISDEP running on int2grid.
Different instances of IVISDEP running on
the grid and visualized using MD.
Direct Approach
Not implemented, only proposed.
Viability:
Computation: O(Nv2 Np2)
Transmision (Nv1/3 +1)2 Np
Direct Approach: An example
100 modes * 16 data * 8 bytes/data =
12.8 kbytes/surface
100 Hex (100x27 modes)
100 Hex (100x27 modes)
(1 sec/iteration)
(1 sec/iteration)
Node A
12.8kbytes *100 Hex=1.2 MB/s
Node B
Total bandwidth/node=2.4MB/s full duplex
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6
Node 7
Data management and
visualization on the grid
Analyze and visualize the big amount of data
generated (more than 100GB per simulation):
• If looking for something it is not easy what to look at.
• So, ideally, the more you can see the more you can find.
Data distributed around the SE on the grid.
Could be possible for the user to:
• Interact with the remotelly stored data
• Analyze data in real time on the grid
• Visualize a complex graphical representation generated on
the grid
Proposed model
Conclusions
Grid has demostrated to be useful for the
Fusion community through developed
INT.EU.GRID applications (IVISDEP)
Next applications seem to be more parallel
demanding codes. Here we propose one
We propose too an interactive data
visualization and management application
for scientific results analysis
Thank you!
Questions?
Slide 14
Fusion Simulations, data visualization
and future requirements for the
interactive grid infraestructure
F. Castejón1, D. López Bruna1, J.M. Reynolds2, A. Tarancón2,
R. Vallés2, J.L. Velasco2
1.
2.
Instituto de Biocomputación y Física de Sistemas Complejos
Laboratorio nacional de Fusión-Asociación Euratom/Ciemat
28040 Madrid, Spain.
WWW on Fusion
What is Fusion?
• Increasing the temperature of a gas, we get a plasma state
• At this temperature, the union of light atom nuclei is possible
through an exothermal process:
Mass after fusion process less than before it
Exceeding mass -> energy
GAS
High
Temperatures
PLASMA
Why Fusion?
• Long-term safe and enviromentally friendly
• Delocalized fuel: water and lithium
When Fusion?
• ITER will produce about 400MW of power excess, and it is the
previous step to DEMO, a demostration reactor, the last stage
(50 years)
THEORETICAL MODELS
PLASMA SIMULATION
Why?
• Reach a better understanding of plasma physics is
necessary to obtain good models
• Not expensive, compared with the experiment
• Very Flexible
What?
• Under several non-restrictive assumptions, the
plasma is modeled by the equations:
• Guiding center aproximation
• Evolution of the distribution function for every specie:
Convection terms
Collision terms
Solving GC plasma equation
The parameters are:
• Magnetic field (supose fixed)
• Electric field (for the moment supose fixed)
The only aditional non linearity are the collision
terms
Even assuming all previous simplifications, it is
not possible to obtain a general analytical
solution in the 5D space.
It is neccesary to obtain numerical solutions. Two
possible ways are:
• Langevin approach
• Direct approach
Langevin Approach: ISDEP
Linealizing the collision term:
• Equivalent to applying a thermal
bath
• Then, equation has a FockerPlanck equivalent:
• This is, the evolution of the distribution function is equivalent
to solve many independent test particles trajectories
collisioning with a fixed background caracterized through noise
terms.
Relatively easy to gridify (no communication between
particles)
Direct aproach
Solve directly the equation to get the distribution
function:
• Get a magnetic geometry adapted meshing in 3D spatial
dimensions.
• Get a truncated spectral expansion of the DF in space and
velocity:
If mesh is shared between computation nodes, the
parallelization is not trivial, not as easy to gridify
IMPLEMENTATION
Adaptation of IVISDEP TO INT.EU.GRID
Use case:Opengl+GLUT+MPI+i2glogin+
Gvid+Migrating Desktop
IVISDEP running on int2grid.
Different instances of IVISDEP running on
the grid and visualized using MD.
Direct Approach
Not implemented, only proposed.
Viability:
Computation: O(Nv2 Np2)
Transmision (Nv1/3 +1)2 Np
Direct Approach: An example
100 modes * 16 data * 8 bytes/data =
12.8 kbytes/surface
100 Hex (100x27 modes)
100 Hex (100x27 modes)
(1 sec/iteration)
(1 sec/iteration)
Node A
12.8kbytes *100 Hex=1.2 MB/s
Node B
Total bandwidth/node=2.4MB/s full duplex
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6
Node 7
Data management and
visualization on the grid
Analyze and visualize the big amount of data
generated (more than 100GB per simulation):
• If looking for something it is not easy what to look at.
• So, ideally, the more you can see the more you can find.
Data distributed around the SE on the grid.
Could be possible for the user to:
• Interact with the remotelly stored data
• Analyze data in real time on the grid
• Visualize a complex graphical representation generated on
the grid
Proposed model
Conclusions
Grid has demostrated to be useful for the
Fusion community through developed
INT.EU.GRID applications (IVISDEP)
Next applications seem to be more parallel
demanding codes. Here we propose one
We propose too an interactive data
visualization and management application
for scientific results analysis
Thank you!
Questions?
Slide 15
Fusion Simulations, data visualization
and future requirements for the
interactive grid infraestructure
F. Castejón1, D. López Bruna1, J.M. Reynolds2, A. Tarancón2,
R. Vallés2, J.L. Velasco2
1.
2.
Instituto de Biocomputación y Física de Sistemas Complejos
Laboratorio nacional de Fusión-Asociación Euratom/Ciemat
28040 Madrid, Spain.
WWW on Fusion
What is Fusion?
• Increasing the temperature of a gas, we get a plasma state
• At this temperature, the union of light atom nuclei is possible
through an exothermal process:
Mass after fusion process less than before it
Exceeding mass -> energy
GAS
High
Temperatures
PLASMA
Why Fusion?
• Long-term safe and enviromentally friendly
• Delocalized fuel: water and lithium
When Fusion?
• ITER will produce about 400MW of power excess, and it is the
previous step to DEMO, a demostration reactor, the last stage
(50 years)
THEORETICAL MODELS
PLASMA SIMULATION
Why?
• Reach a better understanding of plasma physics is
necessary to obtain good models
• Not expensive, compared with the experiment
• Very Flexible
What?
• Under several non-restrictive assumptions, the
plasma is modeled by the equations:
• Guiding center aproximation
• Evolution of the distribution function for every specie:
Convection terms
Collision terms
Solving GC plasma equation
The parameters are:
• Magnetic field (supose fixed)
• Electric field (for the moment supose fixed)
The only aditional non linearity are the collision
terms
Even assuming all previous simplifications, it is
not possible to obtain a general analytical
solution in the 5D space.
It is neccesary to obtain numerical solutions. Two
possible ways are:
• Langevin approach
• Direct approach
Langevin Approach: ISDEP
Linealizing the collision term:
• Equivalent to applying a thermal
bath
• Then, equation has a FockerPlanck equivalent:
• This is, the evolution of the distribution function is equivalent
to solve many independent test particles trajectories
collisioning with a fixed background caracterized through noise
terms.
Relatively easy to gridify (no communication between
particles)
Direct aproach
Solve directly the equation to get the distribution
function:
• Get a magnetic geometry adapted meshing in 3D spatial
dimensions.
• Get a truncated spectral expansion of the DF in space and
velocity:
If mesh is shared between computation nodes, the
parallelization is not trivial, not as easy to gridify
IMPLEMENTATION
Adaptation of IVISDEP TO INT.EU.GRID
Use case:Opengl+GLUT+MPI+i2glogin+
Gvid+Migrating Desktop
IVISDEP running on int2grid.
Different instances of IVISDEP running on
the grid and visualized using MD.
Direct Approach
Not implemented, only proposed.
Viability:
Computation: O(Nv2 Np2)
Transmision (Nv1/3 +1)2 Np
Direct Approach: An example
100 modes * 16 data * 8 bytes/data =
12.8 kbytes/surface
100 Hex (100x27 modes)
100 Hex (100x27 modes)
(1 sec/iteration)
(1 sec/iteration)
Node A
12.8kbytes *100 Hex=1.2 MB/s
Node B
Total bandwidth/node=2.4MB/s full duplex
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6
Node 7
Data management and
visualization on the grid
Analyze and visualize the big amount of data
generated (more than 100GB per simulation):
• If looking for something it is not easy what to look at.
• So, ideally, the more you can see the more you can find.
Data distributed around the SE on the grid.
Could be possible for the user to:
• Interact with the remotelly stored data
• Analyze data in real time on the grid
• Visualize a complex graphical representation generated on
the grid
Proposed model
Conclusions
Grid has demostrated to be useful for the
Fusion community through developed
INT.EU.GRID applications (IVISDEP)
Next applications seem to be more parallel
demanding codes. Here we propose one
We propose too an interactive data
visualization and management application
for scientific results analysis
Thank you!
Questions?
Slide 16
Fusion Simulations, data visualization
and future requirements for the
interactive grid infraestructure
F. Castejón1, D. López Bruna1, J.M. Reynolds2, A. Tarancón2,
R. Vallés2, J.L. Velasco2
1.
2.
Instituto de Biocomputación y Física de Sistemas Complejos
Laboratorio nacional de Fusión-Asociación Euratom/Ciemat
28040 Madrid, Spain.
WWW on Fusion
What is Fusion?
• Increasing the temperature of a gas, we get a plasma state
• At this temperature, the union of light atom nuclei is possible
through an exothermal process:
Mass after fusion process less than before it
Exceeding mass -> energy
GAS
High
Temperatures
PLASMA
Why Fusion?
• Long-term safe and enviromentally friendly
• Delocalized fuel: water and lithium
When Fusion?
• ITER will produce about 400MW of power excess, and it is the
previous step to DEMO, a demostration reactor, the last stage
(50 years)
THEORETICAL MODELS
PLASMA SIMULATION
Why?
• Reach a better understanding of plasma physics is
necessary to obtain good models
• Not expensive, compared with the experiment
• Very Flexible
What?
• Under several non-restrictive assumptions, the
plasma is modeled by the equations:
• Guiding center aproximation
• Evolution of the distribution function for every specie:
Convection terms
Collision terms
Solving GC plasma equation
The parameters are:
• Magnetic field (supose fixed)
• Electric field (for the moment supose fixed)
The only aditional non linearity are the collision
terms
Even assuming all previous simplifications, it is
not possible to obtain a general analytical
solution in the 5D space.
It is neccesary to obtain numerical solutions. Two
possible ways are:
• Langevin approach
• Direct approach
Langevin Approach: ISDEP
Linealizing the collision term:
• Equivalent to applying a thermal
bath
• Then, equation has a FockerPlanck equivalent:
• This is, the evolution of the distribution function is equivalent
to solve many independent test particles trajectories
collisioning with a fixed background caracterized through noise
terms.
Relatively easy to gridify (no communication between
particles)
Direct aproach
Solve directly the equation to get the distribution
function:
• Get a magnetic geometry adapted meshing in 3D spatial
dimensions.
• Get a truncated spectral expansion of the DF in space and
velocity:
If mesh is shared between computation nodes, the
parallelization is not trivial, not as easy to gridify
IMPLEMENTATION
Adaptation of IVISDEP TO INT.EU.GRID
Use case:Opengl+GLUT+MPI+i2glogin+
Gvid+Migrating Desktop
IVISDEP running on int2grid.
Different instances of IVISDEP running on
the grid and visualized using MD.
Direct Approach
Not implemented, only proposed.
Viability:
Computation: O(Nv2 Np2)
Transmision (Nv1/3 +1)2 Np
Direct Approach: An example
100 modes * 16 data * 8 bytes/data =
12.8 kbytes/surface
100 Hex (100x27 modes)
100 Hex (100x27 modes)
(1 sec/iteration)
(1 sec/iteration)
Node A
12.8kbytes *100 Hex=1.2 MB/s
Node B
Total bandwidth/node=2.4MB/s full duplex
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6
Node 7
Data management and
visualization on the grid
Analyze and visualize the big amount of data
generated (more than 100GB per simulation):
• If looking for something it is not easy what to look at.
• So, ideally, the more you can see the more you can find.
Data distributed around the SE on the grid.
Could be possible for the user to:
• Interact with the remotelly stored data
• Analyze data in real time on the grid
• Visualize a complex graphical representation generated on
the grid
Proposed model
Conclusions
Grid has demostrated to be useful for the
Fusion community through developed
INT.EU.GRID applications (IVISDEP)
Next applications seem to be more parallel
demanding codes. Here we propose one
We propose too an interactive data
visualization and management application
for scientific results analysis
Thank you!
Questions?
Fusion Simulations, data visualization
and future requirements for the
interactive grid infraestructure
F. Castejón1, D. López Bruna1, J.M. Reynolds2, A. Tarancón2,
R. Vallés2, J.L. Velasco2
1.
2.
Instituto de Biocomputación y Física de Sistemas Complejos
Laboratorio nacional de Fusión-Asociación Euratom/Ciemat
28040 Madrid, Spain.
WWW on Fusion
What is Fusion?
• Increasing the temperature of a gas, we get a plasma state
• At this temperature, the union of light atom nuclei is possible
through an exothermal process:
Mass after fusion process less than before it
Exceeding mass -> energy
GAS
High
Temperatures
PLASMA
Why Fusion?
• Long-term safe and enviromentally friendly
• Delocalized fuel: water and lithium
When Fusion?
• ITER will produce about 400MW of power excess, and it is the
previous step to DEMO, a demostration reactor, the last stage
(50 years)
THEORETICAL MODELS
PLASMA SIMULATION
Why?
• Reach a better understanding of plasma physics is
necessary to obtain good models
• Not expensive, compared with the experiment
• Very Flexible
What?
• Under several non-restrictive assumptions, the
plasma is modeled by the equations:
• Guiding center aproximation
• Evolution of the distribution function for every specie:
Convection terms
Collision terms
Solving GC plasma equation
The parameters are:
• Magnetic field (supose fixed)
• Electric field (for the moment supose fixed)
The only aditional non linearity are the collision
terms
Even assuming all previous simplifications, it is
not possible to obtain a general analytical
solution in the 5D space.
It is neccesary to obtain numerical solutions. Two
possible ways are:
• Langevin approach
• Direct approach
Langevin Approach: ISDEP
Linealizing the collision term:
• Equivalent to applying a thermal
bath
• Then, equation has a FockerPlanck equivalent:
• This is, the evolution of the distribution function is equivalent
to solve many independent test particles trajectories
collisioning with a fixed background caracterized through noise
terms.
Relatively easy to gridify (no communication between
particles)
Direct aproach
Solve directly the equation to get the distribution
function:
• Get a magnetic geometry adapted meshing in 3D spatial
dimensions.
• Get a truncated spectral expansion of the DF in space and
velocity:
If mesh is shared between computation nodes, the
parallelization is not trivial, not as easy to gridify
IMPLEMENTATION
Adaptation of IVISDEP TO INT.EU.GRID
Use case:Opengl+GLUT+MPI+i2glogin+
Gvid+Migrating Desktop
IVISDEP running on int2grid.
Different instances of IVISDEP running on
the grid and visualized using MD.
Direct Approach
Not implemented, only proposed.
Viability:
Computation: O(Nv2 Np2)
Transmision (Nv1/3 +1)2 Np
Direct Approach: An example
100 modes * 16 data * 8 bytes/data =
12.8 kbytes/surface
100 Hex (100x27 modes)
100 Hex (100x27 modes)
(1 sec/iteration)
(1 sec/iteration)
Node A
12.8kbytes *100 Hex=1.2 MB/s
Node B
Total bandwidth/node=2.4MB/s full duplex
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6
Node 7
Data management and
visualization on the grid
Analyze and visualize the big amount of data
generated (more than 100GB per simulation):
• If looking for something it is not easy what to look at.
• So, ideally, the more you can see the more you can find.
Data distributed around the SE on the grid.
Could be possible for the user to:
• Interact with the remotelly stored data
• Analyze data in real time on the grid
• Visualize a complex graphical representation generated on
the grid
Proposed model
Conclusions
Grid has demostrated to be useful for the
Fusion community through developed
INT.EU.GRID applications (IVISDEP)
Next applications seem to be more parallel
demanding codes. Here we propose one
We propose too an interactive data
visualization and management application
for scientific results analysis
Thank you!
Questions?
Slide 2
Fusion Simulations, data visualization
and future requirements for the
interactive grid infraestructure
F. Castejón1, D. López Bruna1, J.M. Reynolds2, A. Tarancón2,
R. Vallés2, J.L. Velasco2
1.
2.
Instituto de Biocomputación y Física de Sistemas Complejos
Laboratorio nacional de Fusión-Asociación Euratom/Ciemat
28040 Madrid, Spain.
WWW on Fusion
What is Fusion?
• Increasing the temperature of a gas, we get a plasma state
• At this temperature, the union of light atom nuclei is possible
through an exothermal process:
Mass after fusion process less than before it
Exceeding mass -> energy
GAS
High
Temperatures
PLASMA
Why Fusion?
• Long-term safe and enviromentally friendly
• Delocalized fuel: water and lithium
When Fusion?
• ITER will produce about 400MW of power excess, and it is the
previous step to DEMO, a demostration reactor, the last stage
(50 years)
THEORETICAL MODELS
PLASMA SIMULATION
Why?
• Reach a better understanding of plasma physics is
necessary to obtain good models
• Not expensive, compared with the experiment
• Very Flexible
What?
• Under several non-restrictive assumptions, the
plasma is modeled by the equations:
• Guiding center aproximation
• Evolution of the distribution function for every specie:
Convection terms
Collision terms
Solving GC plasma equation
The parameters are:
• Magnetic field (supose fixed)
• Electric field (for the moment supose fixed)
The only aditional non linearity are the collision
terms
Even assuming all previous simplifications, it is
not possible to obtain a general analytical
solution in the 5D space.
It is neccesary to obtain numerical solutions. Two
possible ways are:
• Langevin approach
• Direct approach
Langevin Approach: ISDEP
Linealizing the collision term:
• Equivalent to applying a thermal
bath
• Then, equation has a FockerPlanck equivalent:
• This is, the evolution of the distribution function is equivalent
to solve many independent test particles trajectories
collisioning with a fixed background caracterized through noise
terms.
Relatively easy to gridify (no communication between
particles)
Direct aproach
Solve directly the equation to get the distribution
function:
• Get a magnetic geometry adapted meshing in 3D spatial
dimensions.
• Get a truncated spectral expansion of the DF in space and
velocity:
If mesh is shared between computation nodes, the
parallelization is not trivial, not as easy to gridify
IMPLEMENTATION
Adaptation of IVISDEP TO INT.EU.GRID
Use case:Opengl+GLUT+MPI+i2glogin+
Gvid+Migrating Desktop
IVISDEP running on int2grid.
Different instances of IVISDEP running on
the grid and visualized using MD.
Direct Approach
Not implemented, only proposed.
Viability:
Computation: O(Nv2 Np2)
Transmision (Nv1/3 +1)2 Np
Direct Approach: An example
100 modes * 16 data * 8 bytes/data =
12.8 kbytes/surface
100 Hex (100x27 modes)
100 Hex (100x27 modes)
(1 sec/iteration)
(1 sec/iteration)
Node A
12.8kbytes *100 Hex=1.2 MB/s
Node B
Total bandwidth/node=2.4MB/s full duplex
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6
Node 7
Data management and
visualization on the grid
Analyze and visualize the big amount of data
generated (more than 100GB per simulation):
• If looking for something it is not easy what to look at.
• So, ideally, the more you can see the more you can find.
Data distributed around the SE on the grid.
Could be possible for the user to:
• Interact with the remotelly stored data
• Analyze data in real time on the grid
• Visualize a complex graphical representation generated on
the grid
Proposed model
Conclusions
Grid has demostrated to be useful for the
Fusion community through developed
INT.EU.GRID applications (IVISDEP)
Next applications seem to be more parallel
demanding codes. Here we propose one
We propose too an interactive data
visualization and management application
for scientific results analysis
Thank you!
Questions?
Slide 3
Fusion Simulations, data visualization
and future requirements for the
interactive grid infraestructure
F. Castejón1, D. López Bruna1, J.M. Reynolds2, A. Tarancón2,
R. Vallés2, J.L. Velasco2
1.
2.
Instituto de Biocomputación y Física de Sistemas Complejos
Laboratorio nacional de Fusión-Asociación Euratom/Ciemat
28040 Madrid, Spain.
WWW on Fusion
What is Fusion?
• Increasing the temperature of a gas, we get a plasma state
• At this temperature, the union of light atom nuclei is possible
through an exothermal process:
Mass after fusion process less than before it
Exceeding mass -> energy
GAS
High
Temperatures
PLASMA
Why Fusion?
• Long-term safe and enviromentally friendly
• Delocalized fuel: water and lithium
When Fusion?
• ITER will produce about 400MW of power excess, and it is the
previous step to DEMO, a demostration reactor, the last stage
(50 years)
THEORETICAL MODELS
PLASMA SIMULATION
Why?
• Reach a better understanding of plasma physics is
necessary to obtain good models
• Not expensive, compared with the experiment
• Very Flexible
What?
• Under several non-restrictive assumptions, the
plasma is modeled by the equations:
• Guiding center aproximation
• Evolution of the distribution function for every specie:
Convection terms
Collision terms
Solving GC plasma equation
The parameters are:
• Magnetic field (supose fixed)
• Electric field (for the moment supose fixed)
The only aditional non linearity are the collision
terms
Even assuming all previous simplifications, it is
not possible to obtain a general analytical
solution in the 5D space.
It is neccesary to obtain numerical solutions. Two
possible ways are:
• Langevin approach
• Direct approach
Langevin Approach: ISDEP
Linealizing the collision term:
• Equivalent to applying a thermal
bath
• Then, equation has a FockerPlanck equivalent:
• This is, the evolution of the distribution function is equivalent
to solve many independent test particles trajectories
collisioning with a fixed background caracterized through noise
terms.
Relatively easy to gridify (no communication between
particles)
Direct aproach
Solve directly the equation to get the distribution
function:
• Get a magnetic geometry adapted meshing in 3D spatial
dimensions.
• Get a truncated spectral expansion of the DF in space and
velocity:
If mesh is shared between computation nodes, the
parallelization is not trivial, not as easy to gridify
IMPLEMENTATION
Adaptation of IVISDEP TO INT.EU.GRID
Use case:Opengl+GLUT+MPI+i2glogin+
Gvid+Migrating Desktop
IVISDEP running on int2grid.
Different instances of IVISDEP running on
the grid and visualized using MD.
Direct Approach
Not implemented, only proposed.
Viability:
Computation: O(Nv2 Np2)
Transmision (Nv1/3 +1)2 Np
Direct Approach: An example
100 modes * 16 data * 8 bytes/data =
12.8 kbytes/surface
100 Hex (100x27 modes)
100 Hex (100x27 modes)
(1 sec/iteration)
(1 sec/iteration)
Node A
12.8kbytes *100 Hex=1.2 MB/s
Node B
Total bandwidth/node=2.4MB/s full duplex
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6
Node 7
Data management and
visualization on the grid
Analyze and visualize the big amount of data
generated (more than 100GB per simulation):
• If looking for something it is not easy what to look at.
• So, ideally, the more you can see the more you can find.
Data distributed around the SE on the grid.
Could be possible for the user to:
• Interact with the remotelly stored data
• Analyze data in real time on the grid
• Visualize a complex graphical representation generated on
the grid
Proposed model
Conclusions
Grid has demostrated to be useful for the
Fusion community through developed
INT.EU.GRID applications (IVISDEP)
Next applications seem to be more parallel
demanding codes. Here we propose one
We propose too an interactive data
visualization and management application
for scientific results analysis
Thank you!
Questions?
Slide 4
Fusion Simulations, data visualization
and future requirements for the
interactive grid infraestructure
F. Castejón1, D. López Bruna1, J.M. Reynolds2, A. Tarancón2,
R. Vallés2, J.L. Velasco2
1.
2.
Instituto de Biocomputación y Física de Sistemas Complejos
Laboratorio nacional de Fusión-Asociación Euratom/Ciemat
28040 Madrid, Spain.
WWW on Fusion
What is Fusion?
• Increasing the temperature of a gas, we get a plasma state
• At this temperature, the union of light atom nuclei is possible
through an exothermal process:
Mass after fusion process less than before it
Exceeding mass -> energy
GAS
High
Temperatures
PLASMA
Why Fusion?
• Long-term safe and enviromentally friendly
• Delocalized fuel: water and lithium
When Fusion?
• ITER will produce about 400MW of power excess, and it is the
previous step to DEMO, a demostration reactor, the last stage
(50 years)
THEORETICAL MODELS
PLASMA SIMULATION
Why?
• Reach a better understanding of plasma physics is
necessary to obtain good models
• Not expensive, compared with the experiment
• Very Flexible
What?
• Under several non-restrictive assumptions, the
plasma is modeled by the equations:
• Guiding center aproximation
• Evolution of the distribution function for every specie:
Convection terms
Collision terms
Solving GC plasma equation
The parameters are:
• Magnetic field (supose fixed)
• Electric field (for the moment supose fixed)
The only aditional non linearity are the collision
terms
Even assuming all previous simplifications, it is
not possible to obtain a general analytical
solution in the 5D space.
It is neccesary to obtain numerical solutions. Two
possible ways are:
• Langevin approach
• Direct approach
Langevin Approach: ISDEP
Linealizing the collision term:
• Equivalent to applying a thermal
bath
• Then, equation has a FockerPlanck equivalent:
• This is, the evolution of the distribution function is equivalent
to solve many independent test particles trajectories
collisioning with a fixed background caracterized through noise
terms.
Relatively easy to gridify (no communication between
particles)
Direct aproach
Solve directly the equation to get the distribution
function:
• Get a magnetic geometry adapted meshing in 3D spatial
dimensions.
• Get a truncated spectral expansion of the DF in space and
velocity:
If mesh is shared between computation nodes, the
parallelization is not trivial, not as easy to gridify
IMPLEMENTATION
Adaptation of IVISDEP TO INT.EU.GRID
Use case:Opengl+GLUT+MPI+i2glogin+
Gvid+Migrating Desktop
IVISDEP running on int2grid.
Different instances of IVISDEP running on
the grid and visualized using MD.
Direct Approach
Not implemented, only proposed.
Viability:
Computation: O(Nv2 Np2)
Transmision (Nv1/3 +1)2 Np
Direct Approach: An example
100 modes * 16 data * 8 bytes/data =
12.8 kbytes/surface
100 Hex (100x27 modes)
100 Hex (100x27 modes)
(1 sec/iteration)
(1 sec/iteration)
Node A
12.8kbytes *100 Hex=1.2 MB/s
Node B
Total bandwidth/node=2.4MB/s full duplex
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6
Node 7
Data management and
visualization on the grid
Analyze and visualize the big amount of data
generated (more than 100GB per simulation):
• If looking for something it is not easy what to look at.
• So, ideally, the more you can see the more you can find.
Data distributed around the SE on the grid.
Could be possible for the user to:
• Interact with the remotelly stored data
• Analyze data in real time on the grid
• Visualize a complex graphical representation generated on
the grid
Proposed model
Conclusions
Grid has demostrated to be useful for the
Fusion community through developed
INT.EU.GRID applications (IVISDEP)
Next applications seem to be more parallel
demanding codes. Here we propose one
We propose too an interactive data
visualization and management application
for scientific results analysis
Thank you!
Questions?
Slide 5
Fusion Simulations, data visualization
and future requirements for the
interactive grid infraestructure
F. Castejón1, D. López Bruna1, J.M. Reynolds2, A. Tarancón2,
R. Vallés2, J.L. Velasco2
1.
2.
Instituto de Biocomputación y Física de Sistemas Complejos
Laboratorio nacional de Fusión-Asociación Euratom/Ciemat
28040 Madrid, Spain.
WWW on Fusion
What is Fusion?
• Increasing the temperature of a gas, we get a plasma state
• At this temperature, the union of light atom nuclei is possible
through an exothermal process:
Mass after fusion process less than before it
Exceeding mass -> energy
GAS
High
Temperatures
PLASMA
Why Fusion?
• Long-term safe and enviromentally friendly
• Delocalized fuel: water and lithium
When Fusion?
• ITER will produce about 400MW of power excess, and it is the
previous step to DEMO, a demostration reactor, the last stage
(50 years)
THEORETICAL MODELS
PLASMA SIMULATION
Why?
• Reach a better understanding of plasma physics is
necessary to obtain good models
• Not expensive, compared with the experiment
• Very Flexible
What?
• Under several non-restrictive assumptions, the
plasma is modeled by the equations:
• Guiding center aproximation
• Evolution of the distribution function for every specie:
Convection terms
Collision terms
Solving GC plasma equation
The parameters are:
• Magnetic field (supose fixed)
• Electric field (for the moment supose fixed)
The only aditional non linearity are the collision
terms
Even assuming all previous simplifications, it is
not possible to obtain a general analytical
solution in the 5D space.
It is neccesary to obtain numerical solutions. Two
possible ways are:
• Langevin approach
• Direct approach
Langevin Approach: ISDEP
Linealizing the collision term:
• Equivalent to applying a thermal
bath
• Then, equation has a FockerPlanck equivalent:
• This is, the evolution of the distribution function is equivalent
to solve many independent test particles trajectories
collisioning with a fixed background caracterized through noise
terms.
Relatively easy to gridify (no communication between
particles)
Direct aproach
Solve directly the equation to get the distribution
function:
• Get a magnetic geometry adapted meshing in 3D spatial
dimensions.
• Get a truncated spectral expansion of the DF in space and
velocity:
If mesh is shared between computation nodes, the
parallelization is not trivial, not as easy to gridify
IMPLEMENTATION
Adaptation of IVISDEP TO INT.EU.GRID
Use case:Opengl+GLUT+MPI+i2glogin+
Gvid+Migrating Desktop
IVISDEP running on int2grid.
Different instances of IVISDEP running on
the grid and visualized using MD.
Direct Approach
Not implemented, only proposed.
Viability:
Computation: O(Nv2 Np2)
Transmision (Nv1/3 +1)2 Np
Direct Approach: An example
100 modes * 16 data * 8 bytes/data =
12.8 kbytes/surface
100 Hex (100x27 modes)
100 Hex (100x27 modes)
(1 sec/iteration)
(1 sec/iteration)
Node A
12.8kbytes *100 Hex=1.2 MB/s
Node B
Total bandwidth/node=2.4MB/s full duplex
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6
Node 7
Data management and
visualization on the grid
Analyze and visualize the big amount of data
generated (more than 100GB per simulation):
• If looking for something it is not easy what to look at.
• So, ideally, the more you can see the more you can find.
Data distributed around the SE on the grid.
Could be possible for the user to:
• Interact with the remotelly stored data
• Analyze data in real time on the grid
• Visualize a complex graphical representation generated on
the grid
Proposed model
Conclusions
Grid has demostrated to be useful for the
Fusion community through developed
INT.EU.GRID applications (IVISDEP)
Next applications seem to be more parallel
demanding codes. Here we propose one
We propose too an interactive data
visualization and management application
for scientific results analysis
Thank you!
Questions?
Slide 6
Fusion Simulations, data visualization
and future requirements for the
interactive grid infraestructure
F. Castejón1, D. López Bruna1, J.M. Reynolds2, A. Tarancón2,
R. Vallés2, J.L. Velasco2
1.
2.
Instituto de Biocomputación y Física de Sistemas Complejos
Laboratorio nacional de Fusión-Asociación Euratom/Ciemat
28040 Madrid, Spain.
WWW on Fusion
What is Fusion?
• Increasing the temperature of a gas, we get a plasma state
• At this temperature, the union of light atom nuclei is possible
through an exothermal process:
Mass after fusion process less than before it
Exceeding mass -> energy
GAS
High
Temperatures
PLASMA
Why Fusion?
• Long-term safe and enviromentally friendly
• Delocalized fuel: water and lithium
When Fusion?
• ITER will produce about 400MW of power excess, and it is the
previous step to DEMO, a demostration reactor, the last stage
(50 years)
THEORETICAL MODELS
PLASMA SIMULATION
Why?
• Reach a better understanding of plasma physics is
necessary to obtain good models
• Not expensive, compared with the experiment
• Very Flexible
What?
• Under several non-restrictive assumptions, the
plasma is modeled by the equations:
• Guiding center aproximation
• Evolution of the distribution function for every specie:
Convection terms
Collision terms
Solving GC plasma equation
The parameters are:
• Magnetic field (supose fixed)
• Electric field (for the moment supose fixed)
The only aditional non linearity are the collision
terms
Even assuming all previous simplifications, it is
not possible to obtain a general analytical
solution in the 5D space.
It is neccesary to obtain numerical solutions. Two
possible ways are:
• Langevin approach
• Direct approach
Langevin Approach: ISDEP
Linealizing the collision term:
• Equivalent to applying a thermal
bath
• Then, equation has a FockerPlanck equivalent:
• This is, the evolution of the distribution function is equivalent
to solve many independent test particles trajectories
collisioning with a fixed background caracterized through noise
terms.
Relatively easy to gridify (no communication between
particles)
Direct aproach
Solve directly the equation to get the distribution
function:
• Get a magnetic geometry adapted meshing in 3D spatial
dimensions.
• Get a truncated spectral expansion of the DF in space and
velocity:
If mesh is shared between computation nodes, the
parallelization is not trivial, not as easy to gridify
IMPLEMENTATION
Adaptation of IVISDEP TO INT.EU.GRID
Use case:Opengl+GLUT+MPI+i2glogin+
Gvid+Migrating Desktop
IVISDEP running on int2grid.
Different instances of IVISDEP running on
the grid and visualized using MD.
Direct Approach
Not implemented, only proposed.
Viability:
Computation: O(Nv2 Np2)
Transmision (Nv1/3 +1)2 Np
Direct Approach: An example
100 modes * 16 data * 8 bytes/data =
12.8 kbytes/surface
100 Hex (100x27 modes)
100 Hex (100x27 modes)
(1 sec/iteration)
(1 sec/iteration)
Node A
12.8kbytes *100 Hex=1.2 MB/s
Node B
Total bandwidth/node=2.4MB/s full duplex
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6
Node 7
Data management and
visualization on the grid
Analyze and visualize the big amount of data
generated (more than 100GB per simulation):
• If looking for something it is not easy what to look at.
• So, ideally, the more you can see the more you can find.
Data distributed around the SE on the grid.
Could be possible for the user to:
• Interact with the remotelly stored data
• Analyze data in real time on the grid
• Visualize a complex graphical representation generated on
the grid
Proposed model
Conclusions
Grid has demostrated to be useful for the
Fusion community through developed
INT.EU.GRID applications (IVISDEP)
Next applications seem to be more parallel
demanding codes. Here we propose one
We propose too an interactive data
visualization and management application
for scientific results analysis
Thank you!
Questions?
Slide 7
Fusion Simulations, data visualization
and future requirements for the
interactive grid infraestructure
F. Castejón1, D. López Bruna1, J.M. Reynolds2, A. Tarancón2,
R. Vallés2, J.L. Velasco2
1.
2.
Instituto de Biocomputación y Física de Sistemas Complejos
Laboratorio nacional de Fusión-Asociación Euratom/Ciemat
28040 Madrid, Spain.
WWW on Fusion
What is Fusion?
• Increasing the temperature of a gas, we get a plasma state
• At this temperature, the union of light atom nuclei is possible
through an exothermal process:
Mass after fusion process less than before it
Exceeding mass -> energy
GAS
High
Temperatures
PLASMA
Why Fusion?
• Long-term safe and enviromentally friendly
• Delocalized fuel: water and lithium
When Fusion?
• ITER will produce about 400MW of power excess, and it is the
previous step to DEMO, a demostration reactor, the last stage
(50 years)
THEORETICAL MODELS
PLASMA SIMULATION
Why?
• Reach a better understanding of plasma physics is
necessary to obtain good models
• Not expensive, compared with the experiment
• Very Flexible
What?
• Under several non-restrictive assumptions, the
plasma is modeled by the equations:
• Guiding center aproximation
• Evolution of the distribution function for every specie:
Convection terms
Collision terms
Solving GC plasma equation
The parameters are:
• Magnetic field (supose fixed)
• Electric field (for the moment supose fixed)
The only aditional non linearity are the collision
terms
Even assuming all previous simplifications, it is
not possible to obtain a general analytical
solution in the 5D space.
It is neccesary to obtain numerical solutions. Two
possible ways are:
• Langevin approach
• Direct approach
Langevin Approach: ISDEP
Linealizing the collision term:
• Equivalent to applying a thermal
bath
• Then, equation has a FockerPlanck equivalent:
• This is, the evolution of the distribution function is equivalent
to solve many independent test particles trajectories
collisioning with a fixed background caracterized through noise
terms.
Relatively easy to gridify (no communication between
particles)
Direct aproach
Solve directly the equation to get the distribution
function:
• Get a magnetic geometry adapted meshing in 3D spatial
dimensions.
• Get a truncated spectral expansion of the DF in space and
velocity:
If mesh is shared between computation nodes, the
parallelization is not trivial, not as easy to gridify
IMPLEMENTATION
Adaptation of IVISDEP TO INT.EU.GRID
Use case:Opengl+GLUT+MPI+i2glogin+
Gvid+Migrating Desktop
IVISDEP running on int2grid.
Different instances of IVISDEP running on
the grid and visualized using MD.
Direct Approach
Not implemented, only proposed.
Viability:
Computation: O(Nv2 Np2)
Transmision (Nv1/3 +1)2 Np
Direct Approach: An example
100 modes * 16 data * 8 bytes/data =
12.8 kbytes/surface
100 Hex (100x27 modes)
100 Hex (100x27 modes)
(1 sec/iteration)
(1 sec/iteration)
Node A
12.8kbytes *100 Hex=1.2 MB/s
Node B
Total bandwidth/node=2.4MB/s full duplex
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6
Node 7
Data management and
visualization on the grid
Analyze and visualize the big amount of data
generated (more than 100GB per simulation):
• If looking for something it is not easy what to look at.
• So, ideally, the more you can see the more you can find.
Data distributed around the SE on the grid.
Could be possible for the user to:
• Interact with the remotelly stored data
• Analyze data in real time on the grid
• Visualize a complex graphical representation generated on
the grid
Proposed model
Conclusions
Grid has demostrated to be useful for the
Fusion community through developed
INT.EU.GRID applications (IVISDEP)
Next applications seem to be more parallel
demanding codes. Here we propose one
We propose too an interactive data
visualization and management application
for scientific results analysis
Thank you!
Questions?
Slide 8
Fusion Simulations, data visualization
and future requirements for the
interactive grid infraestructure
F. Castejón1, D. López Bruna1, J.M. Reynolds2, A. Tarancón2,
R. Vallés2, J.L. Velasco2
1.
2.
Instituto de Biocomputación y Física de Sistemas Complejos
Laboratorio nacional de Fusión-Asociación Euratom/Ciemat
28040 Madrid, Spain.
WWW on Fusion
What is Fusion?
• Increasing the temperature of a gas, we get a plasma state
• At this temperature, the union of light atom nuclei is possible
through an exothermal process:
Mass after fusion process less than before it
Exceeding mass -> energy
GAS
High
Temperatures
PLASMA
Why Fusion?
• Long-term safe and enviromentally friendly
• Delocalized fuel: water and lithium
When Fusion?
• ITER will produce about 400MW of power excess, and it is the
previous step to DEMO, a demostration reactor, the last stage
(50 years)
THEORETICAL MODELS
PLASMA SIMULATION
Why?
• Reach a better understanding of plasma physics is
necessary to obtain good models
• Not expensive, compared with the experiment
• Very Flexible
What?
• Under several non-restrictive assumptions, the
plasma is modeled by the equations:
• Guiding center aproximation
• Evolution of the distribution function for every specie:
Convection terms
Collision terms
Solving GC plasma equation
The parameters are:
• Magnetic field (supose fixed)
• Electric field (for the moment supose fixed)
The only aditional non linearity are the collision
terms
Even assuming all previous simplifications, it is
not possible to obtain a general analytical
solution in the 5D space.
It is neccesary to obtain numerical solutions. Two
possible ways are:
• Langevin approach
• Direct approach
Langevin Approach: ISDEP
Linealizing the collision term:
• Equivalent to applying a thermal
bath
• Then, equation has a FockerPlanck equivalent:
• This is, the evolution of the distribution function is equivalent
to solve many independent test particles trajectories
collisioning with a fixed background caracterized through noise
terms.
Relatively easy to gridify (no communication between
particles)
Direct aproach
Solve directly the equation to get the distribution
function:
• Get a magnetic geometry adapted meshing in 3D spatial
dimensions.
• Get a truncated spectral expansion of the DF in space and
velocity:
If mesh is shared between computation nodes, the
parallelization is not trivial, not as easy to gridify
IMPLEMENTATION
Adaptation of IVISDEP TO INT.EU.GRID
Use case:Opengl+GLUT+MPI+i2glogin+
Gvid+Migrating Desktop
IVISDEP running on int2grid.
Different instances of IVISDEP running on
the grid and visualized using MD.
Direct Approach
Not implemented, only proposed.
Viability:
Computation: O(Nv2 Np2)
Transmision (Nv1/3 +1)2 Np
Direct Approach: An example
100 modes * 16 data * 8 bytes/data =
12.8 kbytes/surface
100 Hex (100x27 modes)
100 Hex (100x27 modes)
(1 sec/iteration)
(1 sec/iteration)
Node A
12.8kbytes *100 Hex=1.2 MB/s
Node B
Total bandwidth/node=2.4MB/s full duplex
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6
Node 7
Data management and
visualization on the grid
Analyze and visualize the big amount of data
generated (more than 100GB per simulation):
• If looking for something it is not easy what to look at.
• So, ideally, the more you can see the more you can find.
Data distributed around the SE on the grid.
Could be possible for the user to:
• Interact with the remotelly stored data
• Analyze data in real time on the grid
• Visualize a complex graphical representation generated on
the grid
Proposed model
Conclusions
Grid has demostrated to be useful for the
Fusion community through developed
INT.EU.GRID applications (IVISDEP)
Next applications seem to be more parallel
demanding codes. Here we propose one
We propose too an interactive data
visualization and management application
for scientific results analysis
Thank you!
Questions?
Slide 9
Fusion Simulations, data visualization
and future requirements for the
interactive grid infraestructure
F. Castejón1, D. López Bruna1, J.M. Reynolds2, A. Tarancón2,
R. Vallés2, J.L. Velasco2
1.
2.
Instituto de Biocomputación y Física de Sistemas Complejos
Laboratorio nacional de Fusión-Asociación Euratom/Ciemat
28040 Madrid, Spain.
WWW on Fusion
What is Fusion?
• Increasing the temperature of a gas, we get a plasma state
• At this temperature, the union of light atom nuclei is possible
through an exothermal process:
Mass after fusion process less than before it
Exceeding mass -> energy
GAS
High
Temperatures
PLASMA
Why Fusion?
• Long-term safe and enviromentally friendly
• Delocalized fuel: water and lithium
When Fusion?
• ITER will produce about 400MW of power excess, and it is the
previous step to DEMO, a demostration reactor, the last stage
(50 years)
THEORETICAL MODELS
PLASMA SIMULATION
Why?
• Reach a better understanding of plasma physics is
necessary to obtain good models
• Not expensive, compared with the experiment
• Very Flexible
What?
• Under several non-restrictive assumptions, the
plasma is modeled by the equations:
• Guiding center aproximation
• Evolution of the distribution function for every specie:
Convection terms
Collision terms
Solving GC plasma equation
The parameters are:
• Magnetic field (supose fixed)
• Electric field (for the moment supose fixed)
The only aditional non linearity are the collision
terms
Even assuming all previous simplifications, it is
not possible to obtain a general analytical
solution in the 5D space.
It is neccesary to obtain numerical solutions. Two
possible ways are:
• Langevin approach
• Direct approach
Langevin Approach: ISDEP
Linealizing the collision term:
• Equivalent to applying a thermal
bath
• Then, equation has a FockerPlanck equivalent:
• This is, the evolution of the distribution function is equivalent
to solve many independent test particles trajectories
collisioning with a fixed background caracterized through noise
terms.
Relatively easy to gridify (no communication between
particles)
Direct aproach
Solve directly the equation to get the distribution
function:
• Get a magnetic geometry adapted meshing in 3D spatial
dimensions.
• Get a truncated spectral expansion of the DF in space and
velocity:
If mesh is shared between computation nodes, the
parallelization is not trivial, not as easy to gridify
IMPLEMENTATION
Adaptation of IVISDEP TO INT.EU.GRID
Use case:Opengl+GLUT+MPI+i2glogin+
Gvid+Migrating Desktop
IVISDEP running on int2grid.
Different instances of IVISDEP running on
the grid and visualized using MD.
Direct Approach
Not implemented, only proposed.
Viability:
Computation: O(Nv2 Np2)
Transmision (Nv1/3 +1)2 Np
Direct Approach: An example
100 modes * 16 data * 8 bytes/data =
12.8 kbytes/surface
100 Hex (100x27 modes)
100 Hex (100x27 modes)
(1 sec/iteration)
(1 sec/iteration)
Node A
12.8kbytes *100 Hex=1.2 MB/s
Node B
Total bandwidth/node=2.4MB/s full duplex
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6
Node 7
Data management and
visualization on the grid
Analyze and visualize the big amount of data
generated (more than 100GB per simulation):
• If looking for something it is not easy what to look at.
• So, ideally, the more you can see the more you can find.
Data distributed around the SE on the grid.
Could be possible for the user to:
• Interact with the remotelly stored data
• Analyze data in real time on the grid
• Visualize a complex graphical representation generated on
the grid
Proposed model
Conclusions
Grid has demostrated to be useful for the
Fusion community through developed
INT.EU.GRID applications (IVISDEP)
Next applications seem to be more parallel
demanding codes. Here we propose one
We propose too an interactive data
visualization and management application
for scientific results analysis
Thank you!
Questions?
Slide 10
Fusion Simulations, data visualization
and future requirements for the
interactive grid infraestructure
F. Castejón1, D. López Bruna1, J.M. Reynolds2, A. Tarancón2,
R. Vallés2, J.L. Velasco2
1.
2.
Instituto de Biocomputación y Física de Sistemas Complejos
Laboratorio nacional de Fusión-Asociación Euratom/Ciemat
28040 Madrid, Spain.
WWW on Fusion
What is Fusion?
• Increasing the temperature of a gas, we get a plasma state
• At this temperature, the union of light atom nuclei is possible
through an exothermal process:
Mass after fusion process less than before it
Exceeding mass -> energy
GAS
High
Temperatures
PLASMA
Why Fusion?
• Long-term safe and enviromentally friendly
• Delocalized fuel: water and lithium
When Fusion?
• ITER will produce about 400MW of power excess, and it is the
previous step to DEMO, a demostration reactor, the last stage
(50 years)
THEORETICAL MODELS
PLASMA SIMULATION
Why?
• Reach a better understanding of plasma physics is
necessary to obtain good models
• Not expensive, compared with the experiment
• Very Flexible
What?
• Under several non-restrictive assumptions, the
plasma is modeled by the equations:
• Guiding center aproximation
• Evolution of the distribution function for every specie:
Convection terms
Collision terms
Solving GC plasma equation
The parameters are:
• Magnetic field (supose fixed)
• Electric field (for the moment supose fixed)
The only aditional non linearity are the collision
terms
Even assuming all previous simplifications, it is
not possible to obtain a general analytical
solution in the 5D space.
It is neccesary to obtain numerical solutions. Two
possible ways are:
• Langevin approach
• Direct approach
Langevin Approach: ISDEP
Linealizing the collision term:
• Equivalent to applying a thermal
bath
• Then, equation has a FockerPlanck equivalent:
• This is, the evolution of the distribution function is equivalent
to solve many independent test particles trajectories
collisioning with a fixed background caracterized through noise
terms.
Relatively easy to gridify (no communication between
particles)
Direct aproach
Solve directly the equation to get the distribution
function:
• Get a magnetic geometry adapted meshing in 3D spatial
dimensions.
• Get a truncated spectral expansion of the DF in space and
velocity:
If mesh is shared between computation nodes, the
parallelization is not trivial, not as easy to gridify
IMPLEMENTATION
Adaptation of IVISDEP TO INT.EU.GRID
Use case:Opengl+GLUT+MPI+i2glogin+
Gvid+Migrating Desktop
IVISDEP running on int2grid.
Different instances of IVISDEP running on
the grid and visualized using MD.
Direct Approach
Not implemented, only proposed.
Viability:
Computation: O(Nv2 Np2)
Transmision (Nv1/3 +1)2 Np
Direct Approach: An example
100 modes * 16 data * 8 bytes/data =
12.8 kbytes/surface
100 Hex (100x27 modes)
100 Hex (100x27 modes)
(1 sec/iteration)
(1 sec/iteration)
Node A
12.8kbytes *100 Hex=1.2 MB/s
Node B
Total bandwidth/node=2.4MB/s full duplex
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6
Node 7
Data management and
visualization on the grid
Analyze and visualize the big amount of data
generated (more than 100GB per simulation):
• If looking for something it is not easy what to look at.
• So, ideally, the more you can see the more you can find.
Data distributed around the SE on the grid.
Could be possible for the user to:
• Interact with the remotelly stored data
• Analyze data in real time on the grid
• Visualize a complex graphical representation generated on
the grid
Proposed model
Conclusions
Grid has demostrated to be useful for the
Fusion community through developed
INT.EU.GRID applications (IVISDEP)
Next applications seem to be more parallel
demanding codes. Here we propose one
We propose too an interactive data
visualization and management application
for scientific results analysis
Thank you!
Questions?
Slide 11
Fusion Simulations, data visualization
and future requirements for the
interactive grid infraestructure
F. Castejón1, D. López Bruna1, J.M. Reynolds2, A. Tarancón2,
R. Vallés2, J.L. Velasco2
1.
2.
Instituto de Biocomputación y Física de Sistemas Complejos
Laboratorio nacional de Fusión-Asociación Euratom/Ciemat
28040 Madrid, Spain.
WWW on Fusion
What is Fusion?
• Increasing the temperature of a gas, we get a plasma state
• At this temperature, the union of light atom nuclei is possible
through an exothermal process:
Mass after fusion process less than before it
Exceeding mass -> energy
GAS
High
Temperatures
PLASMA
Why Fusion?
• Long-term safe and enviromentally friendly
• Delocalized fuel: water and lithium
When Fusion?
• ITER will produce about 400MW of power excess, and it is the
previous step to DEMO, a demostration reactor, the last stage
(50 years)
THEORETICAL MODELS
PLASMA SIMULATION
Why?
• Reach a better understanding of plasma physics is
necessary to obtain good models
• Not expensive, compared with the experiment
• Very Flexible
What?
• Under several non-restrictive assumptions, the
plasma is modeled by the equations:
• Guiding center aproximation
• Evolution of the distribution function for every specie:
Convection terms
Collision terms
Solving GC plasma equation
The parameters are:
• Magnetic field (supose fixed)
• Electric field (for the moment supose fixed)
The only aditional non linearity are the collision
terms
Even assuming all previous simplifications, it is
not possible to obtain a general analytical
solution in the 5D space.
It is neccesary to obtain numerical solutions. Two
possible ways are:
• Langevin approach
• Direct approach
Langevin Approach: ISDEP
Linealizing the collision term:
• Equivalent to applying a thermal
bath
• Then, equation has a FockerPlanck equivalent:
• This is, the evolution of the distribution function is equivalent
to solve many independent test particles trajectories
collisioning with a fixed background caracterized through noise
terms.
Relatively easy to gridify (no communication between
particles)
Direct aproach
Solve directly the equation to get the distribution
function:
• Get a magnetic geometry adapted meshing in 3D spatial
dimensions.
• Get a truncated spectral expansion of the DF in space and
velocity:
If mesh is shared between computation nodes, the
parallelization is not trivial, not as easy to gridify
IMPLEMENTATION
Adaptation of IVISDEP TO INT.EU.GRID
Use case:Opengl+GLUT+MPI+i2glogin+
Gvid+Migrating Desktop
IVISDEP running on int2grid.
Different instances of IVISDEP running on
the grid and visualized using MD.
Direct Approach
Not implemented, only proposed.
Viability:
Computation: O(Nv2 Np2)
Transmision (Nv1/3 +1)2 Np
Direct Approach: An example
100 modes * 16 data * 8 bytes/data =
12.8 kbytes/surface
100 Hex (100x27 modes)
100 Hex (100x27 modes)
(1 sec/iteration)
(1 sec/iteration)
Node A
12.8kbytes *100 Hex=1.2 MB/s
Node B
Total bandwidth/node=2.4MB/s full duplex
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6
Node 7
Data management and
visualization on the grid
Analyze and visualize the big amount of data
generated (more than 100GB per simulation):
• If looking for something it is not easy what to look at.
• So, ideally, the more you can see the more you can find.
Data distributed around the SE on the grid.
Could be possible for the user to:
• Interact with the remotelly stored data
• Analyze data in real time on the grid
• Visualize a complex graphical representation generated on
the grid
Proposed model
Conclusions
Grid has demostrated to be useful for the
Fusion community through developed
INT.EU.GRID applications (IVISDEP)
Next applications seem to be more parallel
demanding codes. Here we propose one
We propose too an interactive data
visualization and management application
for scientific results analysis
Thank you!
Questions?
Slide 12
Fusion Simulations, data visualization
and future requirements for the
interactive grid infraestructure
F. Castejón1, D. López Bruna1, J.M. Reynolds2, A. Tarancón2,
R. Vallés2, J.L. Velasco2
1.
2.
Instituto de Biocomputación y Física de Sistemas Complejos
Laboratorio nacional de Fusión-Asociación Euratom/Ciemat
28040 Madrid, Spain.
WWW on Fusion
What is Fusion?
• Increasing the temperature of a gas, we get a plasma state
• At this temperature, the union of light atom nuclei is possible
through an exothermal process:
Mass after fusion process less than before it
Exceeding mass -> energy
GAS
High
Temperatures
PLASMA
Why Fusion?
• Long-term safe and enviromentally friendly
• Delocalized fuel: water and lithium
When Fusion?
• ITER will produce about 400MW of power excess, and it is the
previous step to DEMO, a demostration reactor, the last stage
(50 years)
THEORETICAL MODELS
PLASMA SIMULATION
Why?
• Reach a better understanding of plasma physics is
necessary to obtain good models
• Not expensive, compared with the experiment
• Very Flexible
What?
• Under several non-restrictive assumptions, the
plasma is modeled by the equations:
• Guiding center aproximation
• Evolution of the distribution function for every specie:
Convection terms
Collision terms
Solving GC plasma equation
The parameters are:
• Magnetic field (supose fixed)
• Electric field (for the moment supose fixed)
The only aditional non linearity are the collision
terms
Even assuming all previous simplifications, it is
not possible to obtain a general analytical
solution in the 5D space.
It is neccesary to obtain numerical solutions. Two
possible ways are:
• Langevin approach
• Direct approach
Langevin Approach: ISDEP
Linealizing the collision term:
• Equivalent to applying a thermal
bath
• Then, equation has a FockerPlanck equivalent:
• This is, the evolution of the distribution function is equivalent
to solve many independent test particles trajectories
collisioning with a fixed background caracterized through noise
terms.
Relatively easy to gridify (no communication between
particles)
Direct aproach
Solve directly the equation to get the distribution
function:
• Get a magnetic geometry adapted meshing in 3D spatial
dimensions.
• Get a truncated spectral expansion of the DF in space and
velocity:
If mesh is shared between computation nodes, the
parallelization is not trivial, not as easy to gridify
IMPLEMENTATION
Adaptation of IVISDEP TO INT.EU.GRID
Use case:Opengl+GLUT+MPI+i2glogin+
Gvid+Migrating Desktop
IVISDEP running on int2grid.
Different instances of IVISDEP running on
the grid and visualized using MD.
Direct Approach
Not implemented, only proposed.
Viability:
Computation: O(Nv2 Np2)
Transmision (Nv1/3 +1)2 Np
Direct Approach: An example
100 modes * 16 data * 8 bytes/data =
12.8 kbytes/surface
100 Hex (100x27 modes)
100 Hex (100x27 modes)
(1 sec/iteration)
(1 sec/iteration)
Node A
12.8kbytes *100 Hex=1.2 MB/s
Node B
Total bandwidth/node=2.4MB/s full duplex
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6
Node 7
Data management and
visualization on the grid
Analyze and visualize the big amount of data
generated (more than 100GB per simulation):
• If looking for something it is not easy what to look at.
• So, ideally, the more you can see the more you can find.
Data distributed around the SE on the grid.
Could be possible for the user to:
• Interact with the remotelly stored data
• Analyze data in real time on the grid
• Visualize a complex graphical representation generated on
the grid
Proposed model
Conclusions
Grid has demostrated to be useful for the
Fusion community through developed
INT.EU.GRID applications (IVISDEP)
Next applications seem to be more parallel
demanding codes. Here we propose one
We propose too an interactive data
visualization and management application
for scientific results analysis
Thank you!
Questions?
Slide 13
Fusion Simulations, data visualization
and future requirements for the
interactive grid infraestructure
F. Castejón1, D. López Bruna1, J.M. Reynolds2, A. Tarancón2,
R. Vallés2, J.L. Velasco2
1.
2.
Instituto de Biocomputación y Física de Sistemas Complejos
Laboratorio nacional de Fusión-Asociación Euratom/Ciemat
28040 Madrid, Spain.
WWW on Fusion
What is Fusion?
• Increasing the temperature of a gas, we get a plasma state
• At this temperature, the union of light atom nuclei is possible
through an exothermal process:
Mass after fusion process less than before it
Exceeding mass -> energy
GAS
High
Temperatures
PLASMA
Why Fusion?
• Long-term safe and enviromentally friendly
• Delocalized fuel: water and lithium
When Fusion?
• ITER will produce about 400MW of power excess, and it is the
previous step to DEMO, a demostration reactor, the last stage
(50 years)
THEORETICAL MODELS
PLASMA SIMULATION
Why?
• Reach a better understanding of plasma physics is
necessary to obtain good models
• Not expensive, compared with the experiment
• Very Flexible
What?
• Under several non-restrictive assumptions, the
plasma is modeled by the equations:
• Guiding center aproximation
• Evolution of the distribution function for every specie:
Convection terms
Collision terms
Solving GC plasma equation
The parameters are:
• Magnetic field (supose fixed)
• Electric field (for the moment supose fixed)
The only aditional non linearity are the collision
terms
Even assuming all previous simplifications, it is
not possible to obtain a general analytical
solution in the 5D space.
It is neccesary to obtain numerical solutions. Two
possible ways are:
• Langevin approach
• Direct approach
Langevin Approach: ISDEP
Linealizing the collision term:
• Equivalent to applying a thermal
bath
• Then, equation has a FockerPlanck equivalent:
• This is, the evolution of the distribution function is equivalent
to solve many independent test particles trajectories
collisioning with a fixed background caracterized through noise
terms.
Relatively easy to gridify (no communication between
particles)
Direct aproach
Solve directly the equation to get the distribution
function:
• Get a magnetic geometry adapted meshing in 3D spatial
dimensions.
• Get a truncated spectral expansion of the DF in space and
velocity:
If mesh is shared between computation nodes, the
parallelization is not trivial, not as easy to gridify
IMPLEMENTATION
Adaptation of IVISDEP TO INT.EU.GRID
Use case:Opengl+GLUT+MPI+i2glogin+
Gvid+Migrating Desktop
IVISDEP running on int2grid.
Different instances of IVISDEP running on
the grid and visualized using MD.
Direct Approach
Not implemented, only proposed.
Viability:
Computation: O(Nv2 Np2)
Transmision (Nv1/3 +1)2 Np
Direct Approach: An example
100 modes * 16 data * 8 bytes/data =
12.8 kbytes/surface
100 Hex (100x27 modes)
100 Hex (100x27 modes)
(1 sec/iteration)
(1 sec/iteration)
Node A
12.8kbytes *100 Hex=1.2 MB/s
Node B
Total bandwidth/node=2.4MB/s full duplex
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6
Node 7
Data management and
visualization on the grid
Analyze and visualize the big amount of data
generated (more than 100GB per simulation):
• If looking for something it is not easy what to look at.
• So, ideally, the more you can see the more you can find.
Data distributed around the SE on the grid.
Could be possible for the user to:
• Interact with the remotelly stored data
• Analyze data in real time on the grid
• Visualize a complex graphical representation generated on
the grid
Proposed model
Conclusions
Grid has demostrated to be useful for the
Fusion community through developed
INT.EU.GRID applications (IVISDEP)
Next applications seem to be more parallel
demanding codes. Here we propose one
We propose too an interactive data
visualization and management application
for scientific results analysis
Thank you!
Questions?
Slide 14
Fusion Simulations, data visualization
and future requirements for the
interactive grid infraestructure
F. Castejón1, D. López Bruna1, J.M. Reynolds2, A. Tarancón2,
R. Vallés2, J.L. Velasco2
1.
2.
Instituto de Biocomputación y Física de Sistemas Complejos
Laboratorio nacional de Fusión-Asociación Euratom/Ciemat
28040 Madrid, Spain.
WWW on Fusion
What is Fusion?
• Increasing the temperature of a gas, we get a plasma state
• At this temperature, the union of light atom nuclei is possible
through an exothermal process:
Mass after fusion process less than before it
Exceeding mass -> energy
GAS
High
Temperatures
PLASMA
Why Fusion?
• Long-term safe and enviromentally friendly
• Delocalized fuel: water and lithium
When Fusion?
• ITER will produce about 400MW of power excess, and it is the
previous step to DEMO, a demostration reactor, the last stage
(50 years)
THEORETICAL MODELS
PLASMA SIMULATION
Why?
• Reach a better understanding of plasma physics is
necessary to obtain good models
• Not expensive, compared with the experiment
• Very Flexible
What?
• Under several non-restrictive assumptions, the
plasma is modeled by the equations:
• Guiding center aproximation
• Evolution of the distribution function for every specie:
Convection terms
Collision terms
Solving GC plasma equation
The parameters are:
• Magnetic field (supose fixed)
• Electric field (for the moment supose fixed)
The only aditional non linearity are the collision
terms
Even assuming all previous simplifications, it is
not possible to obtain a general analytical
solution in the 5D space.
It is neccesary to obtain numerical solutions. Two
possible ways are:
• Langevin approach
• Direct approach
Langevin Approach: ISDEP
Linealizing the collision term:
• Equivalent to applying a thermal
bath
• Then, equation has a FockerPlanck equivalent:
• This is, the evolution of the distribution function is equivalent
to solve many independent test particles trajectories
collisioning with a fixed background caracterized through noise
terms.
Relatively easy to gridify (no communication between
particles)
Direct aproach
Solve directly the equation to get the distribution
function:
• Get a magnetic geometry adapted meshing in 3D spatial
dimensions.
• Get a truncated spectral expansion of the DF in space and
velocity:
If mesh is shared between computation nodes, the
parallelization is not trivial, not as easy to gridify
IMPLEMENTATION
Adaptation of IVISDEP TO INT.EU.GRID
Use case:Opengl+GLUT+MPI+i2glogin+
Gvid+Migrating Desktop
IVISDEP running on int2grid.
Different instances of IVISDEP running on
the grid and visualized using MD.
Direct Approach
Not implemented, only proposed.
Viability:
Computation: O(Nv2 Np2)
Transmision (Nv1/3 +1)2 Np
Direct Approach: An example
100 modes * 16 data * 8 bytes/data =
12.8 kbytes/surface
100 Hex (100x27 modes)
100 Hex (100x27 modes)
(1 sec/iteration)
(1 sec/iteration)
Node A
12.8kbytes *100 Hex=1.2 MB/s
Node B
Total bandwidth/node=2.4MB/s full duplex
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6
Node 7
Data management and
visualization on the grid
Analyze and visualize the big amount of data
generated (more than 100GB per simulation):
• If looking for something it is not easy what to look at.
• So, ideally, the more you can see the more you can find.
Data distributed around the SE on the grid.
Could be possible for the user to:
• Interact with the remotelly stored data
• Analyze data in real time on the grid
• Visualize a complex graphical representation generated on
the grid
Proposed model
Conclusions
Grid has demostrated to be useful for the
Fusion community through developed
INT.EU.GRID applications (IVISDEP)
Next applications seem to be more parallel
demanding codes. Here we propose one
We propose too an interactive data
visualization and management application
for scientific results analysis
Thank you!
Questions?
Slide 15
Fusion Simulations, data visualization
and future requirements for the
interactive grid infraestructure
F. Castejón1, D. López Bruna1, J.M. Reynolds2, A. Tarancón2,
R. Vallés2, J.L. Velasco2
1.
2.
Instituto de Biocomputación y Física de Sistemas Complejos
Laboratorio nacional de Fusión-Asociación Euratom/Ciemat
28040 Madrid, Spain.
WWW on Fusion
What is Fusion?
• Increasing the temperature of a gas, we get a plasma state
• At this temperature, the union of light atom nuclei is possible
through an exothermal process:
Mass after fusion process less than before it
Exceeding mass -> energy
GAS
High
Temperatures
PLASMA
Why Fusion?
• Long-term safe and enviromentally friendly
• Delocalized fuel: water and lithium
When Fusion?
• ITER will produce about 400MW of power excess, and it is the
previous step to DEMO, a demostration reactor, the last stage
(50 years)
THEORETICAL MODELS
PLASMA SIMULATION
Why?
• Reach a better understanding of plasma physics is
necessary to obtain good models
• Not expensive, compared with the experiment
• Very Flexible
What?
• Under several non-restrictive assumptions, the
plasma is modeled by the equations:
• Guiding center aproximation
• Evolution of the distribution function for every specie:
Convection terms
Collision terms
Solving GC plasma equation
The parameters are:
• Magnetic field (supose fixed)
• Electric field (for the moment supose fixed)
The only aditional non linearity are the collision
terms
Even assuming all previous simplifications, it is
not possible to obtain a general analytical
solution in the 5D space.
It is neccesary to obtain numerical solutions. Two
possible ways are:
• Langevin approach
• Direct approach
Langevin Approach: ISDEP
Linealizing the collision term:
• Equivalent to applying a thermal
bath
• Then, equation has a FockerPlanck equivalent:
• This is, the evolution of the distribution function is equivalent
to solve many independent test particles trajectories
collisioning with a fixed background caracterized through noise
terms.
Relatively easy to gridify (no communication between
particles)
Direct aproach
Solve directly the equation to get the distribution
function:
• Get a magnetic geometry adapted meshing in 3D spatial
dimensions.
• Get a truncated spectral expansion of the DF in space and
velocity:
If mesh is shared between computation nodes, the
parallelization is not trivial, not as easy to gridify
IMPLEMENTATION
Adaptation of IVISDEP TO INT.EU.GRID
Use case:Opengl+GLUT+MPI+i2glogin+
Gvid+Migrating Desktop
IVISDEP running on int2grid.
Different instances of IVISDEP running on
the grid and visualized using MD.
Direct Approach
Not implemented, only proposed.
Viability:
Computation: O(Nv2 Np2)
Transmision (Nv1/3 +1)2 Np
Direct Approach: An example
100 modes * 16 data * 8 bytes/data =
12.8 kbytes/surface
100 Hex (100x27 modes)
100 Hex (100x27 modes)
(1 sec/iteration)
(1 sec/iteration)
Node A
12.8kbytes *100 Hex=1.2 MB/s
Node B
Total bandwidth/node=2.4MB/s full duplex
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6
Node 7
Data management and
visualization on the grid
Analyze and visualize the big amount of data
generated (more than 100GB per simulation):
• If looking for something it is not easy what to look at.
• So, ideally, the more you can see the more you can find.
Data distributed around the SE on the grid.
Could be possible for the user to:
• Interact with the remotelly stored data
• Analyze data in real time on the grid
• Visualize a complex graphical representation generated on
the grid
Proposed model
Conclusions
Grid has demostrated to be useful for the
Fusion community through developed
INT.EU.GRID applications (IVISDEP)
Next applications seem to be more parallel
demanding codes. Here we propose one
We propose too an interactive data
visualization and management application
for scientific results analysis
Thank you!
Questions?
Slide 16
Fusion Simulations, data visualization
and future requirements for the
interactive grid infraestructure
F. Castejón1, D. López Bruna1, J.M. Reynolds2, A. Tarancón2,
R. Vallés2, J.L. Velasco2
1.
2.
Instituto de Biocomputación y Física de Sistemas Complejos
Laboratorio nacional de Fusión-Asociación Euratom/Ciemat
28040 Madrid, Spain.
WWW on Fusion
What is Fusion?
• Increasing the temperature of a gas, we get a plasma state
• At this temperature, the union of light atom nuclei is possible
through an exothermal process:
Mass after fusion process less than before it
Exceeding mass -> energy
GAS
High
Temperatures
PLASMA
Why Fusion?
• Long-term safe and enviromentally friendly
• Delocalized fuel: water and lithium
When Fusion?
• ITER will produce about 400MW of power excess, and it is the
previous step to DEMO, a demostration reactor, the last stage
(50 years)
THEORETICAL MODELS
PLASMA SIMULATION
Why?
• Reach a better understanding of plasma physics is
necessary to obtain good models
• Not expensive, compared with the experiment
• Very Flexible
What?
• Under several non-restrictive assumptions, the
plasma is modeled by the equations:
• Guiding center aproximation
• Evolution of the distribution function for every specie:
Convection terms
Collision terms
Solving GC plasma equation
The parameters are:
• Magnetic field (supose fixed)
• Electric field (for the moment supose fixed)
The only aditional non linearity are the collision
terms
Even assuming all previous simplifications, it is
not possible to obtain a general analytical
solution in the 5D space.
It is neccesary to obtain numerical solutions. Two
possible ways are:
• Langevin approach
• Direct approach
Langevin Approach: ISDEP
Linealizing the collision term:
• Equivalent to applying a thermal
bath
• Then, equation has a FockerPlanck equivalent:
• This is, the evolution of the distribution function is equivalent
to solve many independent test particles trajectories
collisioning with a fixed background caracterized through noise
terms.
Relatively easy to gridify (no communication between
particles)
Direct aproach
Solve directly the equation to get the distribution
function:
• Get a magnetic geometry adapted meshing in 3D spatial
dimensions.
• Get a truncated spectral expansion of the DF in space and
velocity:
If mesh is shared between computation nodes, the
parallelization is not trivial, not as easy to gridify
IMPLEMENTATION
Adaptation of IVISDEP TO INT.EU.GRID
Use case:Opengl+GLUT+MPI+i2glogin+
Gvid+Migrating Desktop
IVISDEP running on int2grid.
Different instances of IVISDEP running on
the grid and visualized using MD.
Direct Approach
Not implemented, only proposed.
Viability:
Computation: O(Nv2 Np2)
Transmision (Nv1/3 +1)2 Np
Direct Approach: An example
100 modes * 16 data * 8 bytes/data =
12.8 kbytes/surface
100 Hex (100x27 modes)
100 Hex (100x27 modes)
(1 sec/iteration)
(1 sec/iteration)
Node A
12.8kbytes *100 Hex=1.2 MB/s
Node B
Total bandwidth/node=2.4MB/s full duplex
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6
Node 7
Data management and
visualization on the grid
Analyze and visualize the big amount of data
generated (more than 100GB per simulation):
• If looking for something it is not easy what to look at.
• So, ideally, the more you can see the more you can find.
Data distributed around the SE on the grid.
Could be possible for the user to:
• Interact with the remotelly stored data
• Analyze data in real time on the grid
• Visualize a complex graphical representation generated on
the grid
Proposed model
Conclusions
Grid has demostrated to be useful for the
Fusion community through developed
INT.EU.GRID applications (IVISDEP)
Next applications seem to be more parallel
demanding codes. Here we propose one
We propose too an interactive data
visualization and management application
for scientific results analysis
Thank you!
Questions?