Computational Finance on the GPU

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Education • Parallel programming is paradigm shift – Quants are starting to rethink algorithms – Designing or reusing different algorithms/strategies • Reuse of libraries and frameworks – Concentrate on the core algorithm – Increasing number of libraries for random numbers, linear algebra, reduction etc.
Case Study: Equity Derivatives 15 15x Faster 1 2 Tesla S1070 16x Less Space 500 CPU Cores $24 K 10x Lower Cost $250 K 2.8 KWatts 13x Lower Power 37.5 KWatts Source: BNP Paribas, March 4, 2009
Page 1 and 2: Computational <str
Page 3 and 4: AGENDA Financial Instruments Who’
Page 5 and 6: Financial Instruments Cash Instrume
Page 7 and 8: Commodities • Raw resource - Agri
Page 9 and 10: Foreign Exchange • Also: Forex or
Page 11 and 12: Example: Options • Holder has the
Page 13 and 14: Traders • Trading: - Standardised
Page 15 and 16: Quants • Develop and implement mo
Page 19 and 20: Traders • Determine price for tra
Page 23 and 24: Binomial Trees • Represent possib
Page 25 and 26: Binomial Trees
Page 27 and 28: Finite Differences • Solve the Pa
Page 29 and 30: Finite Differences - Explicit • R
Page 31 and 32: Explicit Finite Differences on the
Page 33 and 34: Monte Carlo • Sample a random wal
Page 35 and 36: Monte Carlo on the GPU Generate Ran
Page 37 and 38: Monte Carlo - Single Kernel parfor
Page 39: Software legacy • Millions of lin
Page 43 and 44: Case Study: Real-time Options 1 Sam
Page 47: Resources • GTC presentations - <

Computational Finance on the GPU

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