intel faster risk oct08 - vassil alexandrov
TRANSCRIPT
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fasterRISK DATA and ANALYTICS 07 October 2008 London
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18.30Keynote SpeakersKeynote Speakers
Andrew Parry J.P.Morgan Chase
Vassil Alexandrov Reading University ACET (Advanced Computing Emerging Technology
Table Discussion
Brian Sentance Xenomorph
Boris Lipiainen Thomson Reuters
PanelPanel
Yaacov Mutnikas AlgorithmicsJohn Godfrey m35Nigel Matthews Thomson ReutersJim Burns MicrosoftAndy Hirst SAP – Business Objects
Chief Wine OfficerChief Wine Officer– 21.30
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Monte Carlo Methods and Their Practical Applications in an HPC Environment
Professor Vassil Alexandrov
Advanced Computing and Emerging Technologies CentreUniversity of Reading, UK
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ACET mission
“Scientific discovery and advancement of science through advanced computing”
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Computational Science
• Mathematical modelling of complex systems• Scalable algorithms• Tools, environments (Collaborative, VR etc)
enabling researchers to efficiently collaborate
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Motivation
• Bridging the Performance Gap • Need to run efficiently on various advanced
architectures• Need to calculate with higher precision• Need to tackle efficiently Grand Challenges
problems • Important applications: Financial Modelling,
Engineering, Simulations etc.
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Intel Quad-Core Xeon CPU
• Harpertown• Dual-die dual-core
CPU, 45nm• x86-64 architecture• 3 GHz Clock speed• 2 x 6 MB L2 cache• 1600 MHz FSB
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Cluster used
• 16 Intel quad-core Harpertown nodes• 16 GByte main memory each• Double Data Rate Infiniband network• Intel C and FORTRAN compilers• OpenMPI
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Relaxation Parameter MC for SLAESolve Ax=b using relaxation parameter MC
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SLAE MC Results• Sparse matrix• Relaxation
parameter of 0.5 or 0.9
• 10-1 accuracy
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Resolvent MC for Matrix Inversion
Invert A using resolvent MC
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Resolvent MC Results• Dense matrix• Eigenvalues of L:
0.05/i• 10-1 accuracy
m
tj
jtjt
tCamtg
aaa
a
*121
*
2
4),(
1221
1
4)(
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Important Properties
• Efficient Distribution of the compute data.• Minimum communication while computing.• Increased precision is achieved adding extra
computations.• Fault-Tolerance – add extra computations and
continue.
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Challenges/Opportunities:
• Computational Science Generic Developments:• Super-scalable Algorithms• Novel Scalable Collaborative Environments• Novel Fault Tolerant Computing Environments • Novel Visualisation techniques
• Applied to large scale problems in:• Computational Biology & Biomedical Applications• Climate & Global Air Pollution Modelling• Financial modelling • Material Science • Risk analysis• Virtual Organisations
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fasterRISK DATA and ANALYTICS 07 October 2008 London