Joint Forces: From Multithreaded Programming to GPU Computing

Feinbube, Frank; Tröger, Peter; Polze, Andreas

doi:10.1109/ms.2010.134

Cited by 12 publications

(9 citation statements)

References 2 publications

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“…This obviously means that SPRINT can run without modification on a variety of cluster configurations and shared memory multi‐processor platforms. Moreover, using MPI allows SPRINT to tackle more than the embarrassingly parallel problems that accelerators such as GPUs are optimized for .…”

Section: Benchmarksmentioning

confidence: 99%

Optimization of a parallel permutation testing function for the SPRINT R package

Petrou

Sloan

Mewissen

et al. 2011

Concurrency and Computation

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The statistical language R and its Bioconductor package are favoured by many biostatisticians for processing microarray data. The amount of data produced by some analyses has reached the limits of many common bioinformatics computing infrastructures. High Performance Computing systems offer a solution to this issue. The Simple Parallel R Interface (SPRINT) is a package that provides biostatisticians with easy access to High Performance Computing systems and allows the addition of parallelized functions to R. Previous work has established that the SPRINT implementation of an R permutation testing function has close to optimal scaling on up to 512 processors on a supercomputer. Access to supercomputers, however, is not always possible, and so the work presented here compares the performance of the SPRINT implementation on a supercomputer with benchmarks on a range of platforms including cloud resources and a common desktop machine with multiprocessing capabilities. Copyright © 2011 John Wiley & Sons, Ltd.

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Section: Benchmarksmentioning

confidence: 99%

Optimization of a parallel permutation testing function for the SPRINT R package

Petrou

Sloan

Mewissen

et al. 2011

Concurrency and Computation

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show abstract

“…This obviously means that SPRINT can run without modification on a variety of cluster configurations and shared memory multi-processor platforms. Moreover, using MPI allows SPRINT to tackle more than the embarrassingly parallel problems that accelerators such as GPUs are optimized for [14].…”

Section: The Parallel Version: Pmaxtmentioning

confidence: 99%

Optimization of a parallel permutation testing function for the SPRINT R package

Petrou¹,

Sloan²,

Mewissen

et al. 2010

Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing

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SUMMARYThe statistical language R and its Bioconductor package are favoured by many biostatisticians for processing microarray data. The amount of data produced by some analyses has reached the limits of many common bioinformatics computing infrastructures. High Performance Computing systems offer a solution to this issue. The Simple Parallel R Interface (SPRINT) is a package that provides biostatisticians with easy access to High Performance Computing systems and allows the addition of parallelized functions to R. Previous work has established that the SPRINT implementation of an R permutation testing function has close to optimal scaling on up to 512 processors on a supercomputer. Access to supercomputers, however, is not always possible, and so the work presented here compares the performance of the SPRINT implementation on a supercomputer with benchmarks on a range of platforms including cloud resources and a common desktop machine with multiprocessing capabilities.

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“…Furthermore, parallel algorithms as a concern of programming paradigm, have as long of a tradition as the one of sequential ones [4] and, although future processor generations promise to come along with hundreds of cores per socket [5], an application can only benefit from this if it's designed for parallel execution.…”

Section: Introductionmentioning

confidence: 99%

Using Graphics Processing Units for Accelerated Information Retrieval

Mocanu¹,

Din²,

Saru³

et al. 2014

SIC

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Abstract:The Parallel computing platforms enable dramatic increases in computing performance by harnessing the power of Graphics Processing Units (GPUs). Their design, based on a high level of hardware parallelization achieved through a big number of processing cores, made GPUs serious competitors for CPU based processing architectures. This fact is most obvious when it comes to processing huge amount of data. Many recent studies aimed at the development of GPU based implementations for various fields such as: astronomy, medicine, image processing, data compression and others. However, very few of them aimed at achieving information retrieval improvement based on GPU. Considering this, along with the latest stage of content based information retrieval algorithms and their practical efficiency for a wide variety of applications, this paper focuses on emphasizing parallel GPGPU algorithms performances against their CPU equivalents.

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Joint Forces: From Multithreaded Programming to GPU Computing

Cited by 12 publications

References 2 publications

Optimization of a parallel permutation testing function for the SPRINT R package

Optimization of a parallel permutation testing function for the SPRINT R package

Optimization of a parallel permutation testing function for the SPRINT R package

Using Graphics Processing Units for Accelerated Information Retrieval

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