High dimensional pricing of exotic European contracts on a GPU Cluster, and comparison to a CPU cluster

Abbas-Turki, Lokman; Vialle, Stéphane; Lapeyre, Bernard; Mercier, Patrick P.

doi:10.1109/ipdps.2009.5161143

Cited by 21 publications

(19 citation statements)

References 10 publications

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“…In [20], we use a parameterized prime modulus LCG (2 31 1), which is a single precision version of (8), and we implement it on single precision GPUs to compare two clusters of GPUs and CPUs. Because of its short period and its random behavior, the LCG (2 31 1) should be taken as a benchmark and not used for standardized applications.…”

Section: Parallel-random Number Generation From Parameterization Of Rmentioning

confidence: 99%

Pricing derivatives on graphics processing units using Monte Carlo simulation

Abbas-Turki

Vialle

Lapeyre

et al. 2012

Concurrency and Computation

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International audienceThis paper is about using the existing Monte Carlo approach for pricing European and American contracts on a state-of-the-art graphics processing unit (GPU) architecture. First, we adapt on a cluster of GPUs two different suitable paradigms of parallelizing random number generators, which were developed for CPU clusters. Because in financial applications, we request results within seconds of simulation, the sufficiently large computations should be implemented on a cluster of machines. Thus, we make the European contract comparison between CPUs and GPUs using from one up to 16 nodes of a CPU/GPU cluster. We show that using GPUs for European contracts reduces the execution time by ∼ 40 and diminishes the energy consumed by ∼ 50 during the simulation. In the second set of experiments, we investigate the benefits of using GPUs' parallelization for pricing American options that require solving an optimal stopping problem and which we implement using the Longstaff and Schwartz regression method. The speedup result obtained for American options varies between two and 10 according to the number of generated paths, the dimensions, and the time discretization

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Section: Parallel-random Number Generation From Parameterization Of Rmentioning

confidence: 99%

Pricing derivatives on graphics processing units using Monte Carlo simulation

Abbas-Turki

Vialle

Lapeyre

et al. 2012

Concurrency and Computation

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“…FPGA platforms have been used to accelerated the Monte-Carlo simulation for financial instruments [8], [9]. In [10], the authors studied the performance of a GPU cluster, which is 2.8 times faster and consumes 28.3 times less energy than a CPU cluster. In [11], the authors statically divided the computation of value-at-risk (VAR) in different computation stages and then mapped these stages to FPGA, GPU and CPU respectively.…”

Section: Related Workmentioning

confidence: 99%

Dynamic scheduling Monte-Carlo framework for multi-accelerator heterogeneous clusters

Tse

Thomas

Tsoi

et al. 2010

2010 International Conference on Field-Programmable Technology

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Monte-Carlo (MC) simulation is an effective tool for solving complex problems such as many-body simulation, exotic option pricing and partial differential equation solving. The huge amount of computation in MC makes it a good candidate for acceleration using hardware and distributed computing platforms. We propose a novel MC simulation framework suitable for a wide range of problems. This framework enables different hardware accelerators in a multi-accelerator heterogeneous cluster to work collaboratively on a single application. It also provides scheduling interfaces to adaptively balance the workload according to the cluster status. Two financial applications, involving asset simulation and option pricing, are built using this framework to demonstrate its capability and flexibility. A cluster with 8 Virtex-5 xc5vlx330t FPGAs and 8 Tesla C1060 GPUs using the proposed framework provides 44 times speedup and 19.6 times improved energy efficiency over a cluster with 16 AMD Phenom 9650 quad-core 2.4GHz CPUs for the GARCH asset simulation application. The Efficient Allocation Line (EAL) is proposed for determining the most efficient allocation of accelerators for either performance or energy consumption.

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“…The project in [1] examines the speed and energy efficiency of CPU and GPU clusters for pricing European exotic pathdependent options. The authors use OpenMP for multi-core CPU parallelization, MPI for communicating across both CPU and GPU clusters, and CUDA for GPU parallelization.…”

Section: Related Workmentioning

confidence: 99%