Financial applications on multi-CPU and multi-GPU architectures
- 198 Downloads
The use of high-performance computing systems to help to make the right investment decisions in financial markets is an open research field where multiple efforts have being carried out during the past few years. Specifically, the Heath–Jarrow–Morton (HJM) model has a number of features that make it well suited for implementation on massively parallel architectures. This paper presents a multi-CPU and multi-GPU implementation of the HJM model that improves both the performance and energy efficiency. The experimental results reveal that the proposed architectures achieve excellent performance improvements, as well as optimize the energy efficiency and the cost/performance ratio.
KeywordsHeterogeneous computing Multi-GPU Financial applications
The authors would like to express their gratitude to François Friggit of Banco Santander who inspired and motivated this challenge as a real business case and provided all necessary assistance to carry out this work. This work has been supported by the Spanish Science and Technology Commission (CICYT) under contract TIN2010-21291-C02-02, the European Unions FP7 under Agreements ERC-321253 (RoMoL) and ICT-288777 (Mont-Blanc) and by the European HiPEAC Network of Excellence.
- 1.Quinn MJ (2003) Parallel programming in C with MPI and OpenMP. McGraw-Hill Education GroupGoogle Scholar
- 2.Morris GW, Aubury M (2007) Design space exploration of the European option benchmark using hyperstreams. In: International conference on field programmable logic and applications, AmsterdamGoogle Scholar
- 3.Agarwal V, Liu L-K, Bader DA (2008) Financial modelling on the cell broadband engine. In: 22nd IEEE international symposium on parallel and distributed processing, IPDPS 2008, Miami, pp 1–12Google Scholar
- 6.Bienia C, Kumarand S, Pal Singh SJ, Li K (2008) The PARSEC benchmark suite: characterization and architectural implications. In: Proceedings of the 17th international conference on parallel architectures and compilation techniques, pp 72–81Google Scholar
- 7.Sinclair M, Duwe H, Sankaralingam K (2011) Porting CMP Benchmarks to GPUs Computer Sciences Department, Technical Report 1693. University of Wisconsin, MadrisonGoogle Scholar
- 8.Castillo J, Bosque JL, Castillo E, Huerta P, Martínez JI (2009) Hardware accelerated montecarlo financial simulation over low cost fpga cluster. In: IPDPS, pp 1–8Google Scholar
- 9.Tian X, Benkrid K (2010) High-performance quasi-monte carlo financial simulation: FPGA vs. GPP vs. GPU. ACM transactions on reconfigurable technolgy systems, vol 3, no 4, pp 1–26Google Scholar
- 10.Baxter R, Booth S, Bull M, Cawood G, Perry J, Parsons M, Trew A (2008) Maxwell: 64 FPGA supercomputer. Eng Lett 16(3):8Google Scholar
- 11.Abbas-Turki LA, Vialle S, Lapeyre B, Mercier P (2009) High dimensional pricing of exotic European contracts on a gpu cluster, and comparison to a CPU cluster. In: IEEE international symposium on parallel distributed processing (IPDPS), pp 1–8Google Scholar
- 12.Gaikwad A, Toke LM (2010) Parallel iterative linear solvers on GPU: a financial engineering case. In: 18th euromicro international conference on parallel, distributed and network-based processing (PDP), pp 607–614Google Scholar
- 14.Musiela M (2004) Methods in financial modeling, 2nd edn. Springer, BerlinGoogle Scholar