JAPARA - a Java parallel random number generator library for high-performance computing

Coddington, Paul; Newell, Andrew J.

doi:10.1109/ipdps.2004.1303143

Cited by 11 publications

(13 citation statements)

References 10 publications

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“…In this context, since each thread has its own private data (not each core), the threads can be seen as (logical) PEs [64,67], and it appears more relevant to have one stream per thread rather than one stream per physical PE. This is done for example in JAPARA [9], in which a synchronized method in a central monitor computes (sequentially) the seeds of the different streams and assigns them to the threads, using a splitting technique as in [45,46]. Once the thread gets its seed, it can evolves independently; there is no further need for the central monitor.…”

Section: How To Produce Parallel Streams and Substreamsmentioning

confidence: 99%

Random numbers for parallel computers: Requirements and methods, with emphasis on GPUs

L’Ecuyer

Munger

Oreshkin

et al. 2017

Mathematics and Computers in Simulation

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We examine the requirements and the available methods and software to provide (or imitate) uniform random numbers in parallel computing environments. In this context, for the great majority of applications, independent streams of random numbers are required, each being computed on a single processing element at a time. Sometimes, thousands or even millions of such streams are needed. We explain how they can be produced and managed. We devote particular attention to multiple streams for GPU devices.

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Section: How To Produce Parallel Streams and Substreamsmentioning

confidence: 99%

Random numbers for parallel computers: Requirements and methods, with emphasis on GPUs

L’Ecuyer

Munger

Oreshkin

et al. 2017

Mathematics and Computers in Simulation

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“…L'Ecuyer and his team proposed a package able to produce many long streams and substreams in C, C++, Java, and FORTRAN [12] and also in R in a later version. Coddington and Newell proposed the JaPara library (a Java Parallel Random Number Library) for High-Performance Computing [25]. If we consider Java, our advice would be to use the SSJ package, still from L'Ecuyer's team.…”

Section: E Software Aid For the Distribution Of Random Streamsmentioning

confidence: 99%

How to correctly deal with pseudorandom numbers in manycore environments: Application to GPU programming with Shoverand

Passerat–Palmbach

Hill

2012

2012 International Conference on High Performance Computing &Amp; Simulation (HPCS)

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Stochastic simulations are often sensitive to the source of randomness that characterizes the statistical quality of their results. Consequently, we need highly reliable Random Number Generators (RNGs) to feed such applications. Recent developments try to shrink the computation time by relying more and more General Purpose Graphics Processing Units (GPGPUs) to speed-up stochastic simulations. Such devices bring new parallelization possibilities, but they also introduce new programming difficulties. Since RNGs are at the base of any stochastic simulation, they also need to be ported to GP-GPU. There is still a lack of well-designed implementations of quality-proven RNGs on GP-GPU platforms. In this paper, we introduce ShoveRand, a framework defining common rules to generate random numbers uniformly on GP-GPU. Our framework is designed to cope with any GPU-enabled development platform and to expose a straightforward interface to users. We also provide an existing RNG implementation with this framework to demonstrate its efficiency in both development and ease of use.

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“…This approach is the natural one but doesn't fulfill the requirements for a good parallel RNG [18] and creates a bottleneck that slows down the distributed simulation. The "sequence splitting" or "blocking" consists in splitting the RNG cycle into non-overlapping contiguous sections [19]. This 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.…”

Section: Parallelization Of Prngmentioning

confidence: 99%

“…This technique is available for a few generators like some lagged Fibonacci pseudo-random number generators. [19]. This last technique is close to the "parameterization" technique that might be used with some RNG like the Mersenne Twister [16] or linear congruential generator with Mersenne or Sophie-Germain prime moduli [21].…”

Section: Parallelization Of Prngmentioning

confidence: 99%

Monte Carlo simulation with the GATE software using grid computing

Reuillon¹,

Hill²,

Gouinaud³

et al. 2008

Proceedings of the 8th International Conference on New Technologies in Distributed Systems - NOTERE '08

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Monte Carlo simulations needing many replicates to obtain good statistical results can be easily executed in parallel using the "Multiple Replications In Parallel" approach. However, several precautions have to be taken in the generation of the parallel streams of pseudo-random numbers. In this paper, we present the distribution of Monte Carlo simulations performed with the GATE software using local clusters and grid computing. We obtained very convincing results with this large medical application, thanks to the EGEE Grid (Enabling Grid for EsciencE), achieving in one week computations that could have taken more than 3 years of processing on a single computer. This work has been achieved thanks to a generic object-oriented toolbox called DistMe which we designed to automate this kind of parallelization for Monte Carlo simulations. This toolbox, written in Java is freely available on SourceForge and helped to ensure a rigorous distribution of pseudo-random number streams. It is based on the use of a documented XML format for random numbers generators statuses.

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JAPARA - a Java parallel random number generator library for high-performance computing

Abstract: Abstract

Cited by 11 publications

References 10 publications

Random numbers for parallel computers: Requirements and methods, with emphasis on GPUs

Random numbers for parallel computers: Requirements and methods, with emphasis on GPUs

How to correctly deal with pseudorandom numbers in manycore environments: Application to GPU programming with Shoverand

Monte Carlo simulation with the GATE software using grid computing

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