Transcript Implementation and experience with Big Red - a 20.4 TFLOPS IBM BladeCenter cluster Craig A.

Implementation and experience with Big Red - a
20.4 TFLOPS IBM BladeCenter cluster
Craig A. Stewart
[email protected]
26 June 2007
November 6, 2015
Outline
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Brief history of implementation
System architecture
Performance analysis
User experience and science results
Lessons learned to date
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IU & TeraGrid
Image from www.teragrid.org
• IU: 2 core campuses, 6
regional campuses
• President-elect: Michael A.
McRobbie
• Advanced computing:
University Information
Technology Services,
Pervasive Technology Labs,
School of Informatics
• Motivation for being part of
TeraGrid:
Support national research
agendas
Improve ability of IU
researchers to use national
cyberinfrastructure
Testbed for IU computer
science research
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Big Red - Basics and history
• IBM e1350 BladeCenter Cluster, SLES 9,
MPICH, Loadleveler, MOAB
• Spring 2006: 17 days assembly at IBM facility,
disassembled, reassembled in 10 days at IU.
• 20.48 TFLOPS peak theoretical, 15.04 achieved
on Linpack; 23rd on June 2006 Top500 List
(IU’s highest listing to date).
• In production for local users on 22 August 2006,
for TeraGrid users 1 October 2006
• Upgraded to 30.72 TFLOPS Spring 2008; ???
on June 2007 Top500 List
• Named after nickname for IU sports teams
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Motivations and goals
• Initial goals for 20.48 TFLOPS system:
Local demand for cycles exceeded supply
TeraGrid Resource Partner commitments to meet
Support life science research
Support applications at 100s to 1000s of
processors
• 2nd phase upgrade to 30.72 TFLOPS
Support economic development in State of
Indiana
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Why a PowerPC-based blade
cluster?
• Processing power per node
• Density, good power efficiency relative to
available processors
Processor
TFLOPS/
MWatt
MWatts/
PetaFLOPS
Intel Xeon 7041
145
6.88
AMD
219
4.57
PowerPC 970 MP (dual core)
200
5.00
• Possibility of performance gains through use of
Altivec unit & VMX instructions
• Blade architecture provides flexibility for future
• Results of Request for Proposals process
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Feature
20.4 TFLOPS
30.7 TFLOPS
JS21 components
Two 2.5 GHz PowerPC 970MP
processors, 8 GB RAM, 73 GB SAS
Drive, 40 GFLOPS
Same
No. of JS21 blades
512
768
No. of processors; cores
1,024 processors; 2,048 processor
cores
1,536 processors; 3,072 processor
cores
Total system memory
4 TB
6 TB
GPFS scratch space
266 TB
Same
Lustre
535 TB
Same
Home directory space
25 TB
Same
Total outbound network bandwidth
40 Gbit/sec
Same
Bisection bandwidth
64 GB/sec - Myrinet 2000
96 GB/sec - Myrinet 2000
Computational hardware, RAM
Disk storage
Networks
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HPCC and Linpack Results
(510 nodes)
G-HPL
GPTRANS
GRandom
Access
G-FFTE
EPSTREAM
Sys
TFlop/s
GB/s
Gup/s
GFlop/s
GB/s
Total
13.53
40.76
0.2497
67.33
2468
Per
processor
0.013264
0.0399
0.000244
0.066
Data posted to http://icl.cs.utk.edu/hpcc/hpcc_results.cgi
EPSTREAM
Triad
GB/s
EPDGEMM
Random
Ring
Bandwidth
Random
Ring
Latency
GB/s
usec
GFlop/s
17.73
2.42
8.27
0.0212
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IBM e1350 vs Cray XT3
Per processor
(data from http://icl.cs.utk.edu/hpcc/hpcc_results.cgi)
Per process (core)
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IBM e1350 vs HP XC4000 (data from http://icl.cs.utk.edu/hpcc/hpcc_results.cgi)
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Linpack performance
Benchmark
set
Nodes
Peak
Achieved
Theoretical TFLOPS
TFLOPS
HPCC
510
20.40
13.53
66.3
Top500
512
20.48
15.04
73.4
Top500
768
30.72
21.79
70.9
Difference: 4 KB vs 16 MB page size
%
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Elapsed time per simulation timestep among best in TeraGrid
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Image courtesy of Emad Tajkhorshid
• Simulation of TonB-dependent
transporter (TBDT)
• Used systems at NCSA, IU,
PSC
• Modeled mechanisms for
allowing transport of molecules
through cell membrane
• Work by Emad Tajkhorshid and
James Gumbart, of University
of Illinois Urbana-Champaign.
Mechanics of Force
Propagation in TonBDependent Outer Membrane
Transport. Biophysical Journal
93:496-504 (2007)
• To view the results of the
simulation, please go to:
http://www.life.uiuc.edu/emad/
TonB-BtuB/btub-2.5Ans.mpg
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ChemBioGrid
• Analyzed 555,007
abstracts in PubMed in
~ 8,000 CPU hours
• Used OSCAR3 to find
SMILES strings ->
SDF format -> 3D
structure (GAMESS) > into Varuna
database and then
other applications
• “Calculate and look
up” model for
ChemBioGrid
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WxChallenge
(www.wxchallenge.com)
• Over 1,000 undergraduate students, 64
teams, 56 institutions
• Usage on Big Red:
~16,000 CPU hours on Big Red
63% of processing done on Big Red
Most of the students who used Big Red
couldn’t tell you what it is
• Integration of computation and data flows
via Lustre (Data Capacitor)
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Overall user reactions
• NAMD, WRF users very pleased
• Porting from Intel instruction set a perceived
challenge in a cycle-rich environment
• MILC optimization with VMX not successful so
far in eyes of user community
• Keys to biggest successes:
Performance characteristics of JS21 nodes
Linkage of computation and storage (Lustre Data Capacitor)
Support for grid computing via TeraGrid
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Evaluation of implementation
• The manageability of the system is excellent
• For a select group of applications, Big Red
provides excellent performance and reasonable
scalability
• We are likely to expand bandwidth from Big Red
to the rest of the IU cyberinfrastructure
• We are installing a 7 TFLOPS Intel cluster;
model in future to be Intel-compatible processors
as “default entry point,” more specialized
systems for highly scalable codes
• Focus on data management and scalable
computation critical to success
• Next steps: industrial partnerships and economic
development in Indiana
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Conclusions
• A 20.4 TFLOPS system with “not the usual” processors was
successfully implemented serving local Indiana University
researchers, and the national research audience via the
TeraGrid
• Integration of computation and data management systems was
critical to success
• In the future Science Gateways will be increasingly important:
Most scientists can’t constantly chase after the fastest
available system; gateway developers might be able to
Programmability of increasingly unusual architectures not
likely to become easier
For applications with broad potential user bases, or extreme
scalability on specialized systems, Science Gateways will
be critical in enabling transformational capabilities and
supporting scientific workflows. Achieving broad use can
only be achieved by relieving scientists of need to
understand details of systems
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Acknowledgements - Funding
Sources
IU’s involvement as a TeraGrid Resource Partner is supported in part by the National Science
Foundation under Grants No. ACI-0338618l, OCI-0451237, OCI-0535258, and OCI-0504075
The IU Data Capacitor is supported in part by the National Science Foundation under Grant
No. CNS-0521433.
This research was supported in part by the Indiana METACyt Initiative. The Indiana METACyt
Initiative of Indiana University is supported in part by Lilly Endowment, Inc.
This work was supported in part by Shared University Research grants from IBM, Inc. to
Indiana University.
The LEAD portal is developed under the leadership of IU Professors Dr. Dennis Gannon and
Dr. Beth Plale, and supported by NSF grant 331480.
The ChemBioGrid Portal is developed under the leadership of IU Professor Dr. Geoffrey C.
Fox and Dr. Marlon Pierce and funded via the Pervasive Technology Labs (supported by the
Lilly Endowment, Inc.) and the National Institutes of Health grant P20 HG003894-01
Many of the ideas presented in this talk were developed under a Fulbright Senior Scholar’s
award to Stewart, funded by the US Department of State and the Technische Universitaet
Dresden.
Any opinions, findings and conclusions or recommendations expressed in this material are
those of the author(s) and do not necessarily reflect the views of the National Science
Foundation (NSF), National Institutes of Health (NIH), Lilly Endowment, Inc., or any other
funding agency
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Acknowledgements - People
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Maria Morris contributed to the graphics used in this talk
Marcus Christie and Surresh Marru of the Extreme! Computing Lab contributed
the LEAD graphics
John Morris (www.editide.us) and Cairril Mills (Cairril.com Design & Marketing)
contributed graphics
This work would not have been possible without the dedicated and expert efforts
of the staff of the Research Technologies Division of University Information
Technology Services, the faculty and staff of the Pervasive Technology Labs, and
the staff of UITS generally.
Thanks to the faculty and staff with whom we collaborate locally at IU and globally
(via the TeraGrid, and especially at Technische Universitaet Dresden)
Please cite as: Stewart, C.A. Implementation and experience with Big Red –
a 20.4 TFLOPS IBM BladeCenter cluster. 2007. Presentation. Presented at:
International Supercomputer Conference (Dresden, Germany, 26 Jun 2007).
Available from: http://hdl.handle.net/2022/14607
November 6, 2015
Co-author affiliations
Craig A. Stewart; [email protected]; Office of the Vice President and CIO, Indiana University, 601 E. Kirkwood, Bloomington, IN
Matthew Link; [email protected]; University Information Technology Services (UITS), Indiana University, 2711 E. 10 th St.,
Bloomington, IN 47408
D. Scott McCaulay, [email protected],UITS, Indiana University, 2711 E. 10 th St., Bloomington, IN 47408
Greg Rodgers; [email protected]; IBM Corporation, 2455 South Road, Poughkeepsie, New York 12601
George Turner; [email protected]; UITS, Indiana University, 2711 E. 10 th St., Bloomington, IN 47408
David Hancock; dyhancoc@iupui,edu; UITS, Indiana University — Purdue University Indianapolis, 535 W. Michigan Street,
Indianapolis, IN 46202
Richard Repasky; [email protected],UITS, Indiana University, 2711 E. 10 th St., Bloomington, IN 47408
Peng Wang; [email protected]; UITS, Indiana University — Purdue University Indianapolis, 535 W. Michigan Street,
Indianapolis, IN 46202
Faisal Saied; [email protected]; Rosen Center for Advanced Computing, Purdue University, 302 W. Wood Street, West Lafayette,
Indiana 47907
Marlon Pierce; Community Grids Lab, Pervasive Technology Labs at Indiana University, 501 N. Morton Street, Bloomington, IN
47404
Ross Aiken; [email protected]; IBM Corporation, 9229 Delegates Row, Precedent Office Park Bldg 81, Indianapolis, IN 46240;
Matthias Mueller; [email protected]; Center for Information Services and High Performance Computing (ZIH)
Dresden University of Technology D-01062 Dresden, Germany
Matthias Jurenz; [email protected]; Center for Information Services and High Performance Computing (ZIH)
Dresden University of Technology D-01062 Dresden, Germany
Matthias Lieber; [email protected];Center for Information Services and High Performance Computing (ZIH) Dresden
University of Technology D-01062 Dresden, Germany
November 6, 2015
Thank you
• Any questions?