Parallelization Techniques for Random Number Generators

Bradley, T. Douglas; Toit, Jacques du; Tong, R. P.; Giles, Michael B.; Woodhams, Paul

doi:10.1016/b978-0-12-384988-5.00016-4

Cited by 27 publications

(30 citation statements)

References 7 publications

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“…Recently, algorithms have been proposed to produce advanced and customized VRNGs with MRG32k3a and MT19937 [3].…”

Section: Random Number Generatorsmentioning

confidence: 99%

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A Strategy for Parallel Implementations of Stochastic Lagrangian Simulation

Lenôtre

2016

Springer Proceedings in Mathematics &Amp; Statistics

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International audienceIn this paper, we present some investigations on the parallelization of stochastic Lagrangian simulations. The challenge is the proper management of the random numbers. We review two different object-oriented strategies: to draw the random numbers on the fly within each MPI's process or to use a different random number generator for each simulated path. We show the benefits of the second technique which is implemented in the PALMTREE software developed by the Project-team Sage of Inria Rennes. The efficiency of PALMTREE is demonstrated on two classical examples

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“…Recently, algorithms have been proposed to produce advanced and customized VRNGs with MRG32k3a and MT19937 [3].…”

Section: Random Number Generatorsmentioning

confidence: 99%

“…Subsequently, we propose to use the algorithm developed in [3] based on the storage in memory of already computed matrix and the decomposition…”

mentioning

confidence: 99%

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A Strategy for Parallel Implementations of Stochastic Lagrangian Simulation

Lenôtre

2016

Springer Proceedings in Mathematics &Amp; Statistics

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“…The reason why there should be multiple active blocks per multiprocessor is that blocks aren't waiting for a __syncthreads() which can keep the hardware busy. Additional factors include the register availability and the block size [14]. Because of these reasons, we make the size of each block a multiple of Max_threads, which is a factor of both the maximum number of threads per multiprocessor and the maximum number of threads per block.…”

Section: Task Partitionmentioning

confidence: 99%

GPU-Based Monte Carlo Methods for Solving Linear Algebraic Equations

Lai¹

2016

Proceedings of the Fourth International Conference on Information Science and Cloud Computing — PoS(ISCC2015)

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Many engineering, physics, chemistry, and computer science problems involve solving systems of linear algebraic equations (SLAE). It is an important issue in scientific computing field. In this paper we study the Monte Carlo methods (MCMs) for solving SLAE. We take the advantage of Graphic Processor Unit (GPU) to accelerate MCMs for solving SLAE. The result of numerical experiments demonstrates that GPU is very suitable for speeding up this application. Moreover, the accelerated ratio can be up 50X with the problem size increasing. 2015 18-19, December, ISCC

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“…Consequently, the seeding must be made taking into account the principles detailed in [39,40]. However, this seeding process must only be done once for each simulation, so it can be done in software and the different seeds loaded into the FPGA before starting the execution.…”

Section: Random Number Generationmentioning

confidence: 99%

FPGA acceleration using high-level languages of a Monte-Carlo method for pricing complex options

Sanchez-Roman

Moreno

López-Buedo

et al. 2013

Journal of Systems Architecture

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Esta es la versión de autor de la comunicación de congreso publicada en: This is an author produced version of a paper published in: AbstractIn this paper we present an FPGA implementation of a Monte-Carlo method for pricing Asian Options using Impulse C and floating-point arithmetic. In an Altera Stratix-V FPGA, a 149x speedup factor was obtained against an OpenMP-based solution in a 4-core Intel Core i7 processor. This speedup is comparable to that reported in the literature using a classic HDL-based methodology, but the development time is significantly reduced. Additionally, the use of a HLL-based methodology allowed us to implement a highquality gaussian random number generator, which produces more precise results than those obtained with the simple generators usually present in HDL-based designs.

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Parallelization Techniques for Random Number Generators

Cited by 27 publications

References 7 publications

A Strategy for Parallel Implementations of Stochastic Lagrangian Simulation

A Strategy for Parallel Implementations of Stochastic Lagrangian Simulation

GPU-Based Monte Carlo Methods for Solving Linear Algebraic Equations

FPGA acceleration using high-level languages of a Monte-Carlo method for pricing complex options

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