Transcript High Performance Multi-Paradigm Messaging Runtime Integrating Grids and Multicore Systems e-Science 2007 Conference Bangalore India December 13 2007 Geoffrey Fox, Huapeng Yuan, Seung-Hee Bae Community Grids Laboratory,

High Performance
Multi-Paradigm
Messaging Runtime Integrating
Grids and Multicore Systems
e-Science 2007 Conference Bangalore India
December 13 2007
Geoffrey Fox, Huapeng Yuan, Seung-Hee Bae
Community Grids Laboratory, Indiana University Bloomington IN 47404
Xiaohong Qiu
Research Computing UITS, Indiana University Bloomington IN
George Chrysanthakopoulos, Henrik Frystyk Nielsen
Microsoft Research, Redmond WA
[email protected], http://www.infomall.org
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Abstract of Multicore Salsa
Parallel Programming 2.0
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eScience applications need to use distributed Grid environments
where each component is an individual or cluster of multicore
machines. These are expected to have 64-128 cores 5 years from
now and need to support scalable parallelism.
Users will want to compose heterogeneous components into single
jobs and run seamlessly in both distributed fashion and on a future
“Grid on a chip” with different subsets of cores supporting
individual components.
We support this with a simple programming model made up of two
layers supporting traditional parallel and Grid programming
paradigms (workflow) respectively.
We examine for a parallel clustering application, the Concurrency
and Coordination Runtime CCR from Microsoft as a multiparadigm runtime that integrates the two layers.
Our work uses managed code (C#) and for AMD and Intel
processors shows around a factor of 5 better performance than
Java. CCR has MPI pattern and dynamic threading latencies of a
few microseconds that are competitive with the performance2 of
standard MPI for C.
Some links
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See http://www.connotea.org/user/crmc for references - select tag oldies for venerable links; tags like MPI
Applications Compiler have obvious significance
http://www.infomall.org/salsa for recent work
including publications
My tutorial on parallel computing
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http://grids.ucs.indiana.edu/ptliupages/presentations/PC2007/index.html
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Too much Computing?
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Historically both grids and parallel computing have tried to
increase computing capabilities by
• Optimizing performance of codes at cost of re-usability
• Exploiting all possible CPU’s such as Graphics coprocessors and “idle cycles” (across administrative
domains)
• Linking central computers together such as NSF/DoE/DoD
supercomputer networks without clear user requirements
Next Crisis in technology area will be the opposite problem –
commodity chips will be 32-128way parallel in 5 years time
and we currently have no idea how to use them on commodity
systems – especially on clients
• Only 2 releases of standard software (e.g. Office) in this
time span so need solutions that can be implemented in
next 3-5 years
Intel RMS analysis: Gaming and Generalized decision
support (data mining) are ways of using these cycles
Intel’s Projection
Too much Data to the Rescue?
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Multicore servers have clear “universal parallelism” as many
users can access and use machines simultaneously
Maybe also need application parallelism (e.g. datamining) as
needed on client machines
Over next years, we will be submerged of course in data
deluge
• Scientific observations for e-Science
• Local (video, environmental) sensors
• Data fetched from Internet defining users interests
Maybe data-mining of this “too much data” will use up the
“too much computing” both for science and commodity PC’s
• PC will use this data(-mining) to be intelligent user
assistant?
• Must have highly parallel algorithms
Parallel Programming Model
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If multicore technology is to succeed, mere mortals must be able to
build effective parallel programs on commodity machines
There are interesting new developments – especially the new Darpa
HPCS Languages X10, Chapel and Fortress
However if mortals are to program the 64-256 core chips expected in 5-7
years, then we must use near term technology and we must make it easy
• This rules out radical new approaches such as new languages
Remember that the important applications are not scientific computing
but most of the algorithms needed are similar to those explored in
scientific parallel computing
We can divide problem into two parts:
• “Micro-parallelism”: High Performance scalable (in number of
cores) parallel kernels or libraries
• Macro-parallelism: Composition of kernels into complete
applications
We currently assume that the kernels of the scalable parallel
algorithms/applications/libraries will be built by experts with a
Broader group of programmers (mere mortals) composing library
members into complete applications.
Multicore SALSA at CGL
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Service Aggregated Linked Sequential Activities
Aims to link parallel and distributed (Grid) computing by
developing parallel applications as services and not as
programs or libraries
• Improve traditionally poor parallel programming
development environments
Developing set of services (library) of multicore parallel data
mining algorithms
Looking at Intel list of algorithms (and all previous experience),
we find there are two styles of “micro-parallelism”
• Dynamic search as in integer programming, Hidden Markov Methods
(and computer chess); irregular synchronization with dynamic threads
• “MPI Style” i.e. several threads running typically in SPMD (Single
Program Multiple Data); collective synchronization of all threads together
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Most Intel RMS are “MPI Style” and very close to scientific
algorithms even if applications are not science
Intel’s Application Stack
Scalable Parallel Components
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How do we implement micro-parallelism?
There are no agreed high-level programming environments for
building library members that are broadly applicable.
However lower level approaches where experts define
parallelism explicitly are available and have clear performance
models.
These include MPI for messaging or just locks within a single
shared memory.
There are several patterns to support here including the
collective synchronization of MPI, dynamic irregular thread
parallelism needed in search algorithms, and more specialized
cases like discrete event simulation.
We use Microsoft CCR
http://msdn.microsoft.com/robotics/ as it supports both MPI
and dynamic threading style of parallelism
There is MPI style messaging and ..
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OpenMP annotation or Automatic Parallelism of existing
software is practical way to use those pesky cores with existing
code
• As parallelism is typically not expressed precisely, one needs luck to get
good performance
• Remember writing in Fortran, C, C#, Java … throws away information
about parallelism
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HPCS Languages should be able to properly express parallelism
but we do not know how efficient and reliable compilers will be
• High Performance Fortran failed as language expressed a subset of
parallelism and compilers did not give predictable performance
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PGAS (Partitioned Global Address Space) like UPC, Co-array
Fortran, Titanium, HPJava
• One decomposes application into parts and writes the code for each
component but use some form of global index
• Compiler generates synchronization and messaging
• PGAS approach should work but has never been widely used – presumably
because compilers not mature
Summary of micro-parallelism
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On new applications, use MPI/locks with explicit
user decomposition
A subset of applications can use “data parallel”
compilers which follow in HPF footsteps
• Graphics Chips and Cell processor motivate such
special compilers but not clear how many
applications can be done this way
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OpenMP and/or Compiler-based Automatic
Parallelism for existing codes in conventional
languages
Composition of Parallel Components
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The composition (macro-parallelism) step has many excellent solutions
as this does not have the same drastic synchronization and correctness
constraints as one has for scalable kernels
• Unlike micro-parallelism step which has no very good solutions
Task parallelism in languages such as C++, C#, Java and Fortran90;
General scripting languages like PHP Perl Python
Domain specific environments like Matlab and Mathematica
Functional Languages like MapReduce, F#
HeNCE, AVS and Khoros from the past and CCA from DoE
Web Service/Grid Workflow like Taverna, Kepler, InforSense KDE,
Pipeline Pilot (from SciTegic) and the LEAD environment built at
Indiana University.
Web solutions like Mash-ups and DSS
Many scientific applications use MPI for the coarse grain composition
as well as fine grain parallelism but this doesn’t seem elegant
The new languages from Darpa’s HPCS program support task
parallelism (composition of parallel components) decoupling
composition and scalable parallelism will remain popular and must be
supported.
Integration of Services and “MPI”/Threads
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Kernels and Composition must be supported both inside chips (the multicore
problem) and between machines in clusters (the traditional parallel computing
problem) or Grids.
The scalable parallelism (kernel) problem is typically only interesting on true
parallel computers (rather than grids) as the algorithms require low
communication latency.
However composition is similar in both parallel and distributed scenarios and it
seems useful to allow the use of Grid and Web composition tools for the parallel
problem.
• This should allow parallel computing to exploit large investment in service
programming environments
Thus in SALSA we express parallel kernels not as traditional libraries but as (some
variant of) services so they can be used by non expert programmers
Bottom Line: We need a runtime that supports inter-service linkage and microparallelism linkage
CCR and DSS have this property
• Does it work and what are performance costs of the universality of runtime?
• Messaging need not be explicit for large data sets inside multicore node.
However still use small messages to synchronize
CICC Chemical Informatics and Cyberinfrastructure
Collaboratory Web Service Infrastructure
Cheminformatics Services
Statistics Services
Database Services
Core functionality
Fingerprints
Similarity
Descriptors
2D diagrams
File format conversion
Computation functionality
Regression
Classification
Clustering
Sampling distributions
3D structures by
CID
SMARTS
3D Similarity
Docking scores/poses by
CID
SMARTS
Protein
Docking scores
Applications
Applications
Docking
Predictive models
Filtering
Feature selection
Druglikeness
2D plots
Toxicity predictions
Arbitrary R code (PkCell)
Mutagenecity predictions
PubChem related data by
Anti-cancer activity predictions
Need to make
Pharmacokinetic parameters
CID, SMARTS
all this parallel
OSCAR Document Analysis
InChI Generation/Search
Computational Chemistry (Gamess, Jaguar etc.)
Core Grid Services
Service Registry
Job Submission and Management
Local Clusters
IU Big Red, TeraGrid, Open Science Grid
Varuna.net
Quantum Chemistry
Portal Services
RSS Feeds
User Profiles
Collaboration as in Sakai
Deterministic Annealing for Data Mining
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We are looking at deterministic annealing algorithms because
although heuristic
• They have clear scalable parallelism (e.g. use parallel BLAS)
• They avoid (some) local minima and regularize ill defined
problems in an intuitively clear fashion
• They are fast (no Monte Carlo)
• I understand them and Google Scholar likes them
Developed first by Durbin as Elastic Net for TSP
Extended by Rose (my student then; now at UCSB)) and Gurewitz
(visitor to C3P) at Caltech for signal processing and applied later to
many optimization and supervised and unsupervised learning
methods.
See K. Rose, "Deterministic Annealing for Clustering, Compression,
Classification, Regression, and Related Optimization Problems,"
Proceedings of the IEEE, vol. 80, pp. 2210-2239, November 1998
High Level Theory
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Deterministic Annealing can be looked at from a
Physics, Statistics and/or Information theoretic point of
view
Consider a function (e.g. a likelihood) L({y}) that we
want to operate on (e.g. maximize)
Set L ({y},T) =  L({y}) exp(- ({y} - {y})2 /T ) d{y}
• Incorporating entropy term ensuring that one looks for most
likely states at temperature T
• If {y} is a distance, replacing L by L corresponds to smearing
or smoothing it over resolution T
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Minimize Free Energy F = -Ln L ({y},T) rather than
energy E = -Ln L ({y})
• Use mean field approximation to avoid Monte Carlo
(simulated annealing)
Deterministic Annealing for Clustering I
N P ointsxi and K Cluster Centersyk
P r(xi  Ck )  exp( E ( xi , yk ) / T ) / Z ( xi ) where
Z ( xi )  k 1 exp( E ( xi , yk ) / T )
K
E ( xi , yk )  ( xi  yk ) 2
Free Energy F  T i 1 ln Z ( xi ))
N
CompareSimple Gaussian Mixture(K centers)with
Z ( xi )  k 1 Pk exp( E ( xi , yk ) /(2 k ))
K
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Illustrating similarity between clustering and Gaussian mixtures
Deterministic annealing for mixtures replaces 2 k 2 by 2 k 2  T
and anneals down to mixture size
Deterministic Annealing for Clustering II
wit h P r(xi  Ck )  exp( E ( x i , y k
Calculat e y k
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new

N
i 1
old
) / T ) / Z ( xi , y k
x i P r(x i  Ck )
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N
i 1
old
)
P r(x i  Ck )
This is an extended K-means algorithm guaranteed not to diverge
Start with a single cluster giving as solution y1 as centroid
For some annealing schedule for T, iterate above algorithm
testing correlation matrix in xi about each cluster center to see if
“elongated”
Split cluster if elongation “long enough”; splitting is a phase
transition in physics view
You do not need to assume number of clusters but rather a final
resolution T or equivalent
At T=0, uninteresting solution is N clusters; one at each point xi
Deterministic
Annealing
F({y}, T)
Solve Linear
Equations for
each temperature
Nonlinearity
removed by
approximating
with solution at
previous higher
temperature
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Configuration {y}
Minimum evolving as temperature decreases
Movement at fixed temperature going to local
minima if not initialized “correctly
Clustering Data
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Cheminformatics was tested successfully with small datasets and
compared to commercial tools
Cluster on properties of chemicals from high throughput
screening results to chemical properties (structure, molecular
weight etc.)
Applying to PubChem (and commercial databases) that have 620 million compounds
• Comparing traditional fingerprint (binary properties) with real-valued
properties
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GIS uses publicly available Census data; in particular the 2000
Census aggregated in 200,000 Census Blocks covering Indiana
• 100MB of data
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Initial clustering done on simple attributes given in this data
• Total population and number of Asian, Hispanic and Renters
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Working with POLIS Center at Indianapolis on clustering of
SAVI (Social Assets and Vulnerabilities Indicators) attributes at
http://www.savi.org) for community and decision makers
• Economy, Loans, Crime, Religion etc.
Total
Asian
Hispanic
Renters
30 Clusters
30 Clusters
10 Clusters
In detail, different groups have
different cluster centers
Where are we?
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We have deterministically annealed clustering running well on 8core (2-processor quad core) Intel systems using C# and
Microsoft Robotics Studio CCR/DSS
Could also run on multicore-based parallel machines but didn’t
do this (is there a large Windows quad core cluster on
TeraGrid?)
• This would also be efficient on large problems
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Applied to Geographical Information Systems (GIS) and census
data
• Could be an interesting application on future broadly deployed PC’s
• Visualize nicely on Google Maps (and presumably Microsoft Virtual Earth)
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Applied to several Cheminformatics problems and have parallel
efficiency but visualization harder as in 150-1024 (or more)
dimensions
Will develop a family of such parallel annealing data-mining
tools where basic approach known for
• Clustering
• Gaussian Mixtures (Expectation Maximization)
• and possibly Hidden Markov Methods
Microsoft CCR
• Supports exchange of messages between threads using named
ports
• Fewer more general primitives than MPI
• FromHandler: Spawn threads without reading ports
• Receive: Each handler reads one item from a single port
• MultipleItemReceive: Each handler reads a prescribed number of
items of a given type from a given port. Note items in a port can
be general structures but all must have same type.
• MultiplePortReceive: Each handler reads a one item of a given
type from multiple ports.
• JoinedReceive: Each handler reads one item from each of two
ports. The items can be of different type.
• Choice: Execute a choice of two or more port-handler pairings
• Interleave: Consists of a set of arbiters (port -- handler pairs) of 3
types that are Concurrent, Exclusive or Teardown (called at end
for clean up). Concurrent arbiters are run concurrently but
exclusive handlers are
• http://msdn.microsoft.com/robotics/
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Preliminary Results
• Parallel Deterministic Annealing Clustering in
C# with speed-up of 7 on Intel 2 quadcore
systems
• Analysis of performance of Java, C, C# in
MPI and dynamic threading with XP, Vista,
Windows Server, Fedora, Redhat on
Intel/AMD systems
• Study of cache effects coming with MPI
thread-based parallelism
• Study of execution time fluctuations in
Windows (limiting speed-up to 7 not 8!)
Parallel Multicore
Deterministic Annealing Clustering
Parallel Overhead
on 8 Threads Intel 8b
0.45
10 Clusters
0.4
Overhead = Constant1 + Constant2/n
Speedup = 8/(1+Overhead)
0.35
Constant1 = 0.05 to 0.1 (Client Windows) due to thread
runtime fluctuations
0.3
0.25
20 Clusters
0.2
0.15
0.1
0.05
10000/(Grain Size n = points per core)
0
0
0.5
1
1.5
2
2.5
3
3.5
4
Parallel Multicore
Deterministic Annealing Clustering
Parallel Overhead for large (2M points) Indiana Census clustering
on 8 Threads Intel 8b
This fluctuating overhead due to 5-10% runtime fluctuations between threads
0.250
0.200
overhead
“Constant1”
0.150
0.100
0.050
Increasing number of clusters decreases
communication/memory bandwidth overheads
0.000
0
5
10
15
20
#cluster
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30
35
Parallel Multicore
Deterministic Annealing Clustering
0.200
Parallel Overhead for subset of PubChem clustering on 8 Threads
(Intel 8b)
0.180
“Constant1”
The fluctuating overhead
is reduced to 2% (as bits not doubles)
40,000 points with 1052 binary properties
(Census is 2 real valued properties)
0.160
overhead
0.140
0.120
0.100
0.080
0.060
0.040
Increasing number of clusters decreases
communication/memory bandwidth overheads
0.020
0.000
0
2
4
6
8
10
#cluster
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14
16
18
Intel 8-core C# with 80 Clusters: Vista Run
Time Fluctuations for Clustering Kernel
• 2 Quadcore Processors
80 Cluster(ratio
of std to timeofvsrun
#thread)
• This is average of standard
deviation
time of the 8 threads
between messaging synchronization points
0.1
Standard Deviation/Run Time
10,000 Datpts
50,000 Datapts
0.05
500,000 Datapts
Number of Threads
0
0
1
2
3
4
5
6
7
8
Intel 8 core with 80 Clusters: Redhat Run
Time Fluctuations for Clustering Kernel
• This is average of standard deviation of run time of the
80 Cluster(ratio of std to time vs #thread)
8 threads between messaging synchronization points
0.006
Standard Deviation/Run Time
0.004
10,000 Datapts
50,000 Datapts
0.002
500,000 Datapts
Number of Threads
0
1
2
3
4
5
6
7
8
Basic Performance of CCR
MPI Exchange Latency in µs (20-30 µs computation between messaging)
Machine
OS
Runtime
Grains
Parallelism
MPI Exchange
Latency
Intel8c:gf12
(8 core 2.33 Ghz)
(in 2 chips)
Redhat
MPJE (Java)
Process
8
181
MPICH2 (C)
Process
8
40.0
MPICH2: Fast
Process
8
39.3
Nemesis
Process
8
4.21
MPJE
Process
8
157
mpiJava
Process
8
111
MPICH2
Process
8
64.2
Vista
MPJE
Process
8
170
Fedora
MPJE
Process
8
142
Fedora
mpiJava
Process
8
100
Vista
CCR (C#)
Thread
8
20.2
XP
MPJE
Process
4
185
Redhat
MPJE
Process
4
152
mpiJava
Process
4
99.4
MPICH2
Process
4
39.3
XP
CCR
Thread
4
16.3
XP
CCR
Thread
4
25.8
Intel8c:gf20
(8 core 2.33 Ghz)
Intel8b
(8 core 2.66 Ghz)
AMD4
(4 core 2.19 Ghz)
Intel4 (4 core 2.8 Ghz)
Fedora
CCR Overhead for a computation of
23.76 µs between messaging
Intel8b: 8 Core
(μs)
Pipeline
Spawned
Rendez
vous
MPI
Number of Parallel Computations
1
2
3
4
7
8
1.58
2.44
3
2.94
4.5
5.06
Shift
2.42
3.2
3.38
5.26
5.14
Two Shifts
Pipeline
4.94
3.96
5.9
4.52
6.84
5.78
14.32 19.44
6.82 7.18
Shift
4.46
6.42
5.86
10.86 11.74
Exchange As
Two Shifts
7.4
11.64
14.16 31.86 35.62
Exchange
6.94
11.22
13.3
2.48
18.78 20.16
Cache Line Interference
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•
•
•
•
Cache
Line
Interference
Early implementations of our clustering algorithm
showed large fluctuations due to the cache line
interference effect discussed here and on next slide
in a simple case
We have one thread on each core each calculating a
sum of same complexity storing result in a common
array A with different cores using different array
locations
Thread i stores sum in A(i) is separation 1 – no
variable access interference but cache line
interference
Thread i stores sum in A(X*i) is separation X
Serious degradation if X < 8 (64 bytes) with Windows
– Note A is a double (8 bytes)
– Less interference effect with Linux – especially Red Hat
Cache Line Interference
•
•
•
Machine
OS
Run
Time
Intel8b
Intel8b
Intel8b
Intel8b
Intel8a
Intel8a
Intel8a
Intel8c
AMD4
AMD4
AMD4
AMD4
AMD4
AMD4
Vista
Vista
Vista
Fedora
XP CCR
XP Locks
XP
Red Hat
WinSrvr
WinSrvr
WinSrvr
XP
XP
XP
C# CCR
C# Locks
C
C
C#
C#
C
C
C# CCR
C# Locks
C
C# CCR
C# Locks
C
Time µs versus Thread Array Separation (unit is 8 bytes)
1
4
8
1024
Mean Std/
Mean
Std/
Mean Std/
Mean Std/
Mean
Mean
Mean
Mean
8.03
.029
3.04
.059
0.884 .0051
0.884 .0069
13.0
.0095 3.08
.0028
0.883 .0043
0.883 .0036
13.4
.0047 1.69
.0026
0.66
.029
0.659 .0057
1.50
.01
0.69
.21
0.307 .0045
0.307 .016
10.6
.033
4.16
.041
1.27
.051
1.43
.049
16.6
.016
4.31
.0067
1.27
.066
1.27
.054
16.9
.0016 2.27
.0042
0.946 .056
0.946 .058
0.441 .0035 0.423
.0031
0.423 .0030
0.423 .032
8.58
.0080 2.62
.081
0.839 .0031
0.838 .0031
8.72
.0036 2.42
0.01
0.836 .0016
0.836 .0013
5.65
.020
2.69
.0060
1.05
.0013
1.05
.0014
8.05
0.010
2.84
0.077
0.84
0.040
0.840 0.022
8.21
0.006
2.57
0.016
0.84
0.007
0.84
0.007
6.10
0.026
2.95
0.017
1.05
0.019
1.05
0.017
Note measurements at a separation X of 8 (and values between 8 and 1024 not shown)
are essentially identical
Measurements at 7 (not shown) are higher than that at 8 (except for Red Hat which
shows essentially no enhancement at X<8)
If effects due to co-location of thread variables in a 64 byte cache line, the array must be
aligned with cache boundaries
–
In early implementations we found poor X=8 performance expected in words of A split across
cache lines
DSS Section
• We view system as a collection of services
– in this case
– One to supply data
– One to run parallel clustering
– One to visualize results – in this by spawning
a Google maps browser
– Note we are clustering Indiana census data
• DSS is convenient as built on CCR
Average run time (microseconds)
350
DSS Service Measurements
300
250
200
150
100
50
0
1
10
100
1000
10000
Timing of HP Opteron Multicore as aRound
functiontrips
of number of simultaneous twoway service messages processed (November 2006 DSS Release)
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Measurements of Axis 2 shows about 500 microseconds – DSS is 10 times better
39