intel faster risk oct08 - vassil alexandrov

fasterRISK DATA and ANALYTICS 07 October 2008 London

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

Monte Carlo Methods and Their Practical Applications in an HPC Environment

Professor Vassil Alexandrov

Advanced Computing and Emerging Technologies CentreUniversity of Reading, UK

ACET mission

“Scientific discovery and advancement of science through advanced computing”

Computational Science

• Mathematical modelling of complex systems• Scalable algorithms• Tools, environments (Collaborative, VR etc)

enabling researchers to efficiently collaborate

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.

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

Cluster used

• 16 Intel quad-core Harpertown nodes• 16 GByte main memory each• Double Data Rate Infiniband network• Intel C and FORTRAN compilers• OpenMPI

Relaxation Parameter MC for SLAESolve Ax=b using relaxation parameter MC

SLAE MC Results• Sparse matrix• Relaxation

parameter of 0.5 or 0.9

• 10-1 accuracy

Resolvent MC for Matrix Inversion

Invert A using resolvent MC

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)(

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.

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

fasterRISK DATA and ANALYTICS 07 October 2008 London