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High-Performance Quasi-Monte Carlo Financial Simulation: FPGA vs. GPP vs. GPU

Authors:

Khaled BenkridAuthors Info & Claims

ACM Transactions on Reconfigurable Technology and Systems (TRETS), Volume 3, Issue 4

Article No.: 26, Pages 1 - 22

https://doi.org/10.1145/1862648.1862656

Published: 01 November 2010 Publication History

Abstract

Quasi-Monte Carlo simulation is a special Monte Carlo simulation method that uses quasi-random or low-discrepancy numbers as random sample sets. In many applications, this method has proved advantageous compared to the traditional Monte Carlo simulation method, which uses pseudo-random numbers, thanks to its faster convergence and higher level of accuracy. This article presents the design and implementation of a massively parallelized Quasi-Monte Carlo simulation engine on an FPGA-based supercomputer, called Maxwell. It also compares this implementation with equivalent graphics processing units (GPUs) and general purpose processors (GPP)-based implementations. The detailed comparison between these three implementations (FPGA vs. GPP vs. GPU) is done in the context of financial derivatives pricing based on our Quasi-Monte Carlo simulation engine. Real hardware implementations on the Maxwell machine show that FPGAs outperform equivalent GPP-based software implementations by 2 orders of magnitude, with the speed-up figure scaling linearly with the number of processing nodes used (FPGAs/GPPs). The same implementations show that FPGAs achieve a ~ 3x speedup compared to equivalent GPU-based implementations. Power consumption measurements also show FPGAs to be 336x more energy efficient than CPUs, and 16x more energy efficient than GPUs.

References

[1]

Baxter, R., Booth, S., Bull, M., Cawood, G., Perry, J., Parsons, M., Simpson, A., Trew, A., McCormick, A., Smart, G., Smart, R., Cantle, A., Chamberlain, R., and Genest, G. 2007. Maxwell - A 64 FPGA supercomputer. In Proceedings of the 2nd NASA/ESA Conference on Adaptive Hardware and Systems (AHS’07). 287--294.

Digital Library

[2]

Cheung, R. C. C., Lee, D.-U., Luk, W., and Villasenor, J. D. 2007. Hardware generation of arbitrary random number distributions from uniform distributions via the inversion method. IEEE Trans. on VLSI Syst. 952--962.

Digital Library

[3]

Dalal, I. L., Stefan, D., and Harwayne-Gidansky, J. 2008. Low discrepancy sequences for Monte Carlo simulations on reconfigurable platforms. In Proceedings of the International Conference on Application-Specific Systems, Architectures and Processors. 108--113.

Digital Library

[4]

Entacher, K., Schell, T., Schmid, W. C., and Uhl, A. 2003. Defects in parallel Monte Carlo and quasi-Monte Carlo integration using the leap-frog technique. Int. J. Paral. Emer. Distrib. Syst. 18, 13--26.

[5]

Hull, J. C. 2000. Option, Futures, and other Derivatives. Prentice Hall.

[6]

Intel. 2007a. Intel® Math Kernel Library Reference Manual. Intel Corporation.

[7]

Intel. 2007b. Intel® Math Kernel Library Vector Statistical Library Notes. Intel Corporation.

[8]

Jäckel, P. 2002. Monte Carlo Methods in Finance. John Wiley & Sons, Ltd.

[9]

Joy, C., Boyle, P. P., and Tan, K. S. 1996. Quasi-Monte Carlo methods in numerical finance. Manage. Sci. 42, 926--938.

Digital Library

[10]

Mencer, O., Boullis, N., Luk, W., and Styles, H. 2001. Parameterized function evaluation for FPGAs. In Proceedings of the 11th International Conference on Field-Programmable Logic and Applications. 544--554.

Digital Library

[11]

Morris, G. W. and Aubury, M. 2007. Design space exploration of the European option benchmark using hyperstreams. In Proceedings of the International Conference on Field Programmable Logic and Applications (FPL’07). 5--10.

[12]

Niederreiter, H. 1992. Random Number Generation and Quasi-Monte Carlo Methods. Society for Industrial and Applied Mathematics.

Digital Library

[13]

Nvidia. 2008. NVIDIA CUDA Compute Unified Device Architecture Programming Guide. NVIDIA Corporation.

[14]

Press, W. H., Teukolsky, S. A., Vetterling, W. T., and Flannery, B. P. 1992. Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press.

Digital Library

[15]

Snir, M. and Otto, S. 1998. MPI-The Complete Reference. MIT Press, Cambridge, MA.

Digital Library

[16]

SUN. 2002. Sun ONE Grid Engine Administration and User’s Guide. Sun Microsystems, Inc.

[17]

Thomas, D. B., Bower, J. A., and Luk, W. 2006. Hardware architectures for Monte-Carlo based financial simulations. In Proceedings of the IEEE International Conference on Field Programmable Technology (FPT’06). 377--380.

[18]

Tian, X. and Benkrid, K. 2008. Design and implementation of a high performance financial Monte-Carlo simulation engine on an FPGA supercomputer. In Proceedings of the International Conference on Field-Programmable Technology (FPT’08). 81--88.

[19]

Tian, X., Benkrid, K., and Gu, X. 2008. High performance Monte-Carlo based option pricing on FPGAs. Engi. Lett. 16, 434--442.

[20]

Woods, N. A. and VanCourt, T. 2008. FPGA acceleration of Quasi-Monte Carlo in finance. In Proceedings of the International Conference on Field Programmable Logic and Applications (FPL’08). 335--340.

[21]

Yeung, J. H. C., Tsang, C. C., Tsoi, K. H., Kwan, B. S. H., Cheung, C. C. C., Chan, A. P. C., and Leong, P. H. W. 2008. Map-reduce as a programming model for custom computing machines. In Proceedings of the 16th International Symposium on Field-Programmable Custom Computing Machines (FCCM’08). 149--159.

Digital Library

[22]

Zenios, S. A. 1999. High-performance computing in finance: The last 10 years and the next. Paral. Comput. 25, 2149--2175.

Digital Library

[23]

Zhang, G. L., Leong, P. H. W., Ho, C. H., Tsoi, K. H., Cheung, C. C. C., Lee, D.-U., Cheung, R. C. C., and Luk, W. 2005. Reconfigurable acceleration for Monte Carlo based financial simulation. In Proceedings of the IEEE International Conference on Field-Programmable Technology (FPT’05). 215--222.

Cited By

Prouveur CHaefele MKenter TVoss NHuebl ASilvano CRobinson T(2023)FPGA Acceleration for HPC Supercapacitor SimulationsProceedings of the Platform for Advanced Scientific Computing Conference10.1145/3592979.3593419(1-11)Online publication date: 26-Jun-2023
https://dl.acm.org/doi/10.1145/3592979.3593419
Pinto PBispo JCardoso JBarbosa JGadioli DPalermo GMartinovic JGolasowski MSlaninova KCmar RSilvano C(2022)Pegasus: Performance Engineering for Software Applications Targeting HPC SystemsIEEE Transactions on Software Engineering10.1109/TSE.2020.300125748:3(732-754)Online publication date: 1-Mar-2022
https://doi.org/10.1109/TSE.2020.3001257
Asadova FKertesz GLovas RSzenasi S(2022)Fault detection in GPU-enabled Cloud Systems – An Overview2022 IEEE 20th Jubilee World Symposium on Applied Machine Intelligence and Informatics (SAMI)10.1109/SAMI54271.2022.9780804(000317-000322)Online publication date: 2-Mar-2022
https://doi.org/10.1109/SAMI54271.2022.9780804
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Index Terms

High-Performance Quasi-Monte Carlo Financial Simulation: FPGA vs. GPP vs. GPU
1. Hardware
  1. Emerging technologies
  2. Very large scale integration design

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Information

Published In

cover image ACM Transactions on Reconfigurable Technology and Systems

ACM Transactions on Reconfigurable Technology and Systems Volume 3, Issue 4

November 2010

240 pages

ISSN:1936-7406

EISSN:1936-7414

DOI:10.1145/1862648

Issue’s Table of Contents

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 November 2010

Accepted: 01 September 2009

Revised: 01 July 2009

Received: 01 February 2009

Published in TRETS Volume 3, Issue 4

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Cited By

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https://dl.acm.org/doi/10.1145/3592979.3593419
Pinto PBispo JCardoso JBarbosa JGadioli DPalermo GMartinovic JGolasowski MSlaninova KCmar RSilvano C(2022)Pegasus: Performance Engineering for Software Applications Targeting HPC SystemsIEEE Transactions on Software Engineering10.1109/TSE.2020.300125748:3(732-754)Online publication date: 1-Mar-2022
https://doi.org/10.1109/TSE.2020.3001257
Asadova FKertesz GLovas RSzenasi S(2022)Fault detection in GPU-enabled Cloud Systems – An Overview2022 IEEE 20th Jubilee World Symposium on Applied Machine Intelligence and Informatics (SAMI)10.1109/SAMI54271.2022.9780804(000317-000322)Online publication date: 2-Mar-2022
https://doi.org/10.1109/SAMI54271.2022.9780804
Mahony AZeidan GHanzon BPopovici E(2022)A parallel and pipelined implementation of a pascal-simplex based multi-asset option pricer on FPGA using OpenCLMicroprocessors & Microsystems10.1016/j.micpro.2022.10450890:COnline publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1016/j.micpro.2022.104508
Mahony AZeidan GHanzon BPopovici E(2020)A Parallel and Pipelined Implementation of a Pascal-Simplex Based Two Asset Option Pricer on FPGA using OpenCL2020 IEEE Nordic Circuits and Systems Conference (NorCAS)10.1109/NorCAS51424.2020.9264992(1-6)Online publication date: 27-Oct-2020
https://doi.org/10.1109/NorCAS51424.2020.9264992
Mannatunga KAli SCrespo MCicuttin ASamarawikrama J(2020)High Performance 128-Channel Acquisition System for Electrophysiological SignalsIEEE Access10.1109/ACCESS.2020.30070828(122366-122383)Online publication date: 2020
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Peri A(2020)A Hardware Approach to Value Function IterationJournal of Economic Dynamics and Control10.1016/j.jedc.2020.103894(103894)Online publication date: Mar-2020
https://doi.org/10.1016/j.jedc.2020.103894
Chamberlain R(2020)Architecturally truly diverse systems: A reviewFuture Generation Computer Systems10.1016/j.future.2020.03.061Online publication date: Apr-2020
https://doi.org/10.1016/j.future.2020.03.061
Bhagirath Mittal NKumar S(2019)Machine Learning Computation on Multiple GPU's using CUDA and Message Passing Interface2019 2nd International Conference on Power Energy, Environment and Intelligent Control (PEEIC)10.1109/PEEIC47157.2019.8976714(18-22)Online publication date: Oct-2019
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Sedighi AJacobson D(2019)Computational Challenges and Opportunities in Financial ServicesSmart Computing and Communication10.1007/978-3-030-34139-8_31(310-319)Online publication date: 11-Oct-2019
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