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Accelerating financial applications on the GPU

Authors:

Scott Grauer-Gray,

William Killian,

Robert Searles,

John CavazosAuthors Info & Claims

GPGPU-6: Proceedings of the 6th Workshop on General Purpose Processor Using Graphics Processing Units

Pages 127 - 136

https://doi.org/10.1145/2458523.2458536

Published: 16 March 2013 Publication History

Abstract

The QuantLib library is a popular library used for many areas of computational finance. In this work, the parallel processing power of the GPU is used to accelerate QuantLib financial applications. Black-Scholes, Monte-Carlo, Bonds, and Repo code paths in QuantLib are accelerated using hand-written CUDA and OpenCL codes specifically targeted for the GPU. Additionally, HMPP and OpenACC versions of the applications were created to drive the automatic generation of GPU code from sequential code. The results demonstrate a significant speedup for each code using each parallelization method. We were also able to increase the speedup of HMPP-generated code with auto-tuning.

References

[1]

cuRand. {Online}. http://developer.nvidia.com/cuRAND.

[2]

Profiler User's Guide. {Online}. http://docs.nvidia.com/cuda/profiler-users-guide.

[3]

Quantlib home page. {Online}. http://quantlib.org.

[4]

Thrust. {Online}. http://thrust.github.com.

[5]

L. Abbas-Turki and B. Lapeyre. American options pricing on multi-core graphic cards. In Business Intelligence and Financial Engineering, 2009. BIFE '09. International Conference on, pages 307--311, july 2009.

[6]

A. Bernemann, R. Schreyer, and K. Spanderen. Pricing structured equity products on gpus. In High Performance Computational Finance (WHPCF), 2010 IEEE Workshop on, pages 1--7, nov. 2010.

[7]

D. M. Dang, C. C. Christara, and K. R. Jackson. Pricing multi-asset american options on graphics processing units using a pde approach. In High Performance Computational Finance (WHPCF), 2010 IEEE Workshop on, pages 1--8, nov. 2010.

[8]

B. T. C. J. Dixon, Matthew F. and K. Keutzer. Monte carlo-based financial market value-at-risk estimation on gpus. In GPU Computing Gems Jade Edition. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2011.

[9]

D. Egloff. Pricing financial derivatives with high performance finite difference solvers on gpus. In GPU Computing Gems Jade Edition. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1st edition, 2011.

[10]

C. Entreprise. HMPP Basics - Introduction to Concepts. CAPS Entreprise, 2012.

[11]

S. Grauer-Gray, L. Xu, R. Searles, S. Ayalasomayajula, and J. Cavazos. Auto-tuning a high-level language targeted to gpu codes. 2012 Innovative Parallel Computing: Foundations And Applications of GPU, Manycore, and Heterogeneous Systems (InPar2012), 2012.

[12]

M. S. Joshi. Graphical asian options. In Wilmott J., volume 2, pages 97--107, 2010.

[13]

S. Lee and J. S. Vetter. Early evaluation of directive-based gpu programming models for productive exascale computing. In Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, SC '12, pages 23:1--23:11, Los Alamitos, CA, USA, 2012. IEEE Computer Society Press.

Digital Library

[14]

A. Munshi, editor. The OpenCL Specification. Khronos OpenCL Working Group, 2011.

[15]

D. Murakowski, W. Brouwer, and V. Natoli. Cuda implementation of barrier option valuation with jump-diffusion process and brownian bridge. In High Performance Computational Finance (WHPCF), 2010 IEEE Workshop on, pages 1--4, nov. 2010.

[16]

T. Nishimura and M. Matsumoto. A c-program for mt19937. http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/MT2002/CODES/mt19937ar.c.

[17]

NVIDIA. NVIDIA CUDA Compute Unified Device Architecture Programming Guide. NVIDIA Corporation, 2007.

[18]

NVIDIA. Nvidia geforce gtx 200 gpu architectural overview, second-generation unified gpu architecture for visual computing. Technical report, May 2008.

[19]

NVIDIA. Nvidia's next generation cuda compute architecture: Fermi. Technical report, 2009.

[20]

NVIDIA. Gtx 680 kepler whitepaper - geforce. Technical report, 2012.

[21]

NVIDIA. Nvidia's next generation compute architecture: Kepler gk110. Technical report, 2012.

[22]

OpenACC. The OpenACC Application Programming Interface. OpenACC-Standard.org, 2011.

[23]

G. Pages and B. Wilbertz. Parallel implementation of quantization methods for the valuation of swing options on gpgpu. In High Performance Computational Finance (WHPCF), 2010 IEEE Workshop on, pages 1--5, nov. 2010.

[24]

V. Podlozhnyuk. Black-scholes option pricing. In CUDA SDK, 2007.

[25]

V. Podlozhnyuk and M. Harris. Monte carlo option pricing. In CUDA SDK, 2008.

[26]

S. J. Rees and J. Walkenhorst. Large-scale credit risk loss simulation. In GPU Computing Gems Jade Edition. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2011.

[27]

D. B. Thomas. Acceleration of financial monte-carlo simulations using fpgas. In High Performance Computational Finance (WHPCF), 2010 IEEE Workshop on, pages 1--6, nov. 2010.

[28]

Y. Tian, Z. Zhu, F. C. Klebaner, and K. Hamza. Option pricing with the sabr model on the gpu. In High Performance Computational Finance (WHPCF), 2010 IEEE Workshop on, pages 1--8, nov. 2010.

[29]

B. Zhang and C. Oosterlee. Option pricing with cos method on graphics processing units. In Parallel Distributed Processing, 2009. IPDPS 2009. IEEE International Symposium on, pages 1--8, may 2009.

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Wei XJiang NYue HWang XZhao JLi GQiu M(2024)ApproxDup: Developing an Approximate Instruction Duplication Mechanism for Efficient SDC Detection in GPGPUsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2023.333082143:4(1051-1064)Online publication date: Apr-2024
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Fink ZParasyris KRathi PGeorgakoudis GMenon HBremer P(2024)HPAC-ML: A Programming Model for Embedding ML Surrogates in Scientific ApplicationsProceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis10.1109/SC41406.2024.00078(1-16)Online publication date: 17-Nov-2024
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Shivdikar KBao YAgrawal RShen MJonatan GMora EIngare ALivesay NAbellÁN JKim JJoshi AKaeli D(2023)GME: GPU-based Microarchitectural Extensions to Accelerate Homomorphic EncryptionProceedings of the 56th Annual IEEE/ACM International Symposium on Microarchitecture10.1145/3613424.3614279(670-684)Online publication date: 28-Oct-2023
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Accelerating financial applications on the GPU

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Information

Published In

cover image ACM Other conferences

GPGPU-6: Proceedings of the 6th Workshop on General Purpose Processor Using Graphics Processing Units

March 2013

156 pages

ISBN:9781450320177

DOI:10.1145/2458523

Editors:
John Cavazos
University of Delaware
,
Xiang Gong,
David Kaeli

Copyright © 2013 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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SIGARCH: ACM Special Interest Group on Computer Architecture

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 March 2013

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JPMorgan Chase and Company

Conference

GPGPU-6

GPGPU-6: Sixth Workshop on General Purpose Processing Using GPUs

March 16, 2013

Texas, Houston, USA

Acceptance Rates

GPGPU-6 Paper Acceptance Rate 15 of 37 submissions, 41%;

Overall Acceptance Rate 57 of 129 submissions, 44%

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Cited By

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https://doi.org/10.1109/TCAD.2023.3330821
Fink ZParasyris KRathi PGeorgakoudis GMenon HBremer P(2024)HPAC-ML: A Programming Model for Embedding ML Surrogates in Scientific ApplicationsProceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis10.1109/SC41406.2024.00078(1-16)Online publication date: 17-Nov-2024
https://dl.acm.org/doi/10.1109/SC41406.2024.00078
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https://dl.acm.org/doi/10.1145/3613424.3614279
Xu JCharlton M(2023)GPU libraries speed performance analysis for RCWA simulation matrix operationsPhysics and Simulation of Optoelectronic Devices XXXI10.1117/12.2650112(48)Online publication date: 10-Mar-2023
https://doi.org/10.1117/12.2650112
Salzmann PKnorr FThoman PGschwandtner PCosenza BFahringer T(2023)An Asynchronous Dataflow-Driven Execution Model For Distributed Accelerator Computing2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing (CCGrid)10.1109/CCGrid57682.2023.00018(82-93)Online publication date: May-2023
https://doi.org/10.1109/CCGrid57682.2023.00018
Wei XWu YJiang NYue H(2023)Detecting SDCs in GPGPUs Through Efficient Partial Thread RedundancyAlgorithms and Architectures for Parallel Processing10.1007/978-981-97-0862-8_14(224-239)Online publication date: 20-Oct-2023
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Jo JGahm J(2022)G-RMOS: GPU-accelerated Riemannian Metric Optimization on SurfacesComputers in Biology and Medicine10.1016/j.compbiomed.2022.106167150(106167)Online publication date: Nov-2022
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Stockinger MGuerine Mde Paula USantiago FFrota YRosseti IPlastino Ade Oliveira D(2022)A Provenance-based Execution Strategy for Variant GPU-accelerated Scientific Workflows in CloudsJournal of Grid Computing10.1007/s10723-022-09625-y20:4Online publication date: 1-Dec-2022
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Panova EVolokitin VGorshkov AMeyerov I(2022)Black-Scholes Option Pricing on Intel CPUs and GPUs: Implementation on SYCL and Optimization TechniquesSupercomputing10.1007/978-3-031-22941-1_4(48-62)Online publication date: 26-Sep-2022
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