SCIENCE FOUNDATION IRELAND DIGITAL CONTENT WORKSHOP Monday, July 25th 2011, Guinness Storehouse, Dublin Session 4 - Data Analytics, Mining and Visualisation Dr Eoin Brazil, Senior Software Developer and Tech Transfer Manager, Irish Centre for High End Computing (NUIG) Pragmatic Analytics - Case Studies of High Performance Computing for Better Business and Big Data.

1. Consultancy –
Pragmatic
Analytics
Irish Centre for High End Computing
Dr. Eoin Brazil
www.ichec.ie/consultancy

2. Technology Transfer @ ICHEC
• Started just over eighteen months ago
• Core competencies include:
– Performance Optimization
– Data Mining/Analytics (e.g. Computational Finance)
• Consultancy
• Training (e.g. R - & TSA / & AC, CUDA, HPC, etc.)
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4. Visual Exploration
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5. Example – Wine Vintage
• Hot, dry summers give
higher prices in mature
wines
• Chȃteau Pétrus 2000 ~$60,000 (liv-ex.com)
• Bordeaux Equation
• Wine quality = 12.145 + 0.00117 Winter Rainfall +
0.0614 Averarge Growing Season Temperature – 0.00386
Harvest Rainfall
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6. Financial services – Computational Finance
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7. Real-World Constraints
• My application / workflow:
– Deal with +2B transactions per day per site
– Less than 50ms for end-to-end processing
– Need real-time detection of fraud
– Multiple coupled models in ensemble
– Production platform is X
– Cannot incorrectly classify good client as
fraudster
– Data size is too large for my infrastructure
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8. Are you ready
for Big Data ?
• Hadoop is x50+ slower on relation data, can
be x1000+ slower on graph data
• Make sure you hone the tool first:
–
–
–
–
MCMC x53 faster using Rcpp Versus R
Linear Regression x8 using Eigen via R
x15 BLAS/LAPACK with ICC flags and hardware in R
Rmpi / multicore / MKL / pnmath / MR / gputools
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9. What are GPGPUs ?
• Disruptive Innovation in Parallel Computing
– HPC from desktop to supercomputers (10 Gen leap)
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12. Typical Business Results
Domain
Result
Computational
Finance
1 or 8 Cards (x121/x950) = Do in 1 second what used to
Oil and Gas
Data processing = x2 – x6 (profiling at this stage), e.g. if
volume took 44 mins could be done in 22 – 7 ½ mins
Life Sciences
Patient analytics, initial prototype for cardio-vascular
disease detection (~72% accuracy), ongoing work.
Telecomms
Fraud detection prototype for subscription fraud,
Detection (~99% accuracy), avoided predicting good
clients as fraudster*
Electronic
Commerce
Demand forecasting & customer segmentation = Using
historic data to predict future demand (~90% accuracy)
& identified valuable clients (~80% accuracy)
take 2/16 minutes, 10 generations of processor
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13. Acknowledgements
Supported by Science Foundation
Ireland under grant 08/HEC/I1450
and by HEA’s PRTLI-C4.