An On-Demand Fast Parallel Pseudo Random Number Generator with Applications

Banerjee, Dip Sankar; Bahl, A. K.; Kothapalli, Kishore

doi:10.1109/ipdpsw.2012.212

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…We chose the application of photon migration from [12]. In the hybrid solution, we use a hybrid pseudorandom number generator that we developed in [8]. In photon migration, several photons are launched with their position and direction initialized to either zeros (for some pencil beam initialized at the origin) or some random numbers.…”

Section: Monte Carlomentioning

confidence: 99%

“…For the list ranking workload, we summarize the approach used in [9,8]. We start by preprocessing the linked list to reduce the size of the list from n nodes to a size of n/ log n using ideas from fractional independent sets.…”

Section: List Ranking and Connected Componentsmentioning

confidence: 99%

“…To this end, we select a collection of 13 different workloads ranging from databases to graphs, and image analysis, and present hybrid algorithmic solutions for these workloads. Some of the results that we show in this paper are reported recently [8,9,18,15,33], or under submission [10]. We identify two different algorithm engineering approaches that we have used across our 13 workloads.…”

Section: Introductionmentioning

confidence: 99%

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CPU and/or GPU: Revisiting the GPU Vs. CPU Myth

Kothapalli,

Banerjee,

Narayanan

et al. 2013

Preprint

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Parallel computing using accelerators has gained widespread research attention in the past few years. In particular, using GPUs for general purpose computing has brought forth several success stories with respect to time taken, cost, power, and other metrics. However, accelerator based computing has significantly relegated the role of CPUs in computation. As CPUs evolve and also offer matching computational resources, it is important to also include CPUs in the computation. We call this the hybrid computing model. Indeed, most computer systems of the present age offer a degree of heterogeneity and therefore such a model is quite natural.We reevaluate the claim of a recent paper by Lee et al. (ISCA 2010). We argue that the right question arising out of Lee et al. (ISCA 2010) should be how to use a CPU+GPU platform efficiently, instead of whether one should use a CPU or a GPU exclusively. To this end, we experiment with a set of 13 diverse workloads ranging from databases, image processing, sparse matrix kernels, and graphs. We experiment with two different hybrid platforms: one consisting of a 6-core Intel i7-980X CPU and an NVidia Tesla T10 GPU, and another consisting of an Intel E7400 dual core CPU with an NVidia GT520 GPU. On both these platforms, we show that hybrid solutions offer good advantage over CPU or GPU alone solutions. On both these platforms, we also show that our solutions are 90% resource efficient on average.Our work therefore suggests that hybrid computing can offer tremendous advantages at not only research-scale platforms but also the more realistic scale systems with significant performance gains and resource efficiency to the large scale user community.

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Section: Monte Carlomentioning

confidence: 99%

Section: List Ranking and Connected Componentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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CPU and/or GPU: Revisiting the GPU Vs. CPU Myth

Kothapalli,

Banerjee,

Narayanan

et al. 2013

Preprint

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Secrets from the GPU

Mahé¹,

Chauvet²

2014

J Comput Virol Hack Tech

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A Survey of CPU-GPU Heterogeneous Computing Techniques

2015

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As both CPUs and GPUs become employed in a wide range of applications, it has been acknowledged that both of these Processing Units (PUs) have their unique features and strengths and hence, CPU-GPU collaboration is inevitable to achieve high-performance computing. This has motivated a significant amount of research on heterogeneous computing techniques, along with the design of CPU-GPU fused chips and petascale heterogeneous supercomputers. In this article, we survey Heterogeneous Computing Techniques (HCTs) such as workload partitioning that enable utilizing both CPUs and GPUs to improve performance and/or energy efficiency. We review heterogeneous computing approaches at runtime, algorithm, programming, compiler, and application levels. Further, we review both discrete and fused CPU-GPU systems and discuss benchmark suites designed for evaluating Heterogeneous Computing Systems (HCSs). We believe that this article will provide insights into the workings and scope of applications of HCTs to researchers and motivate them to further harness the computational powers of CPUs and GPUs to achieve the goal of exascale performance.

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An On-Demand Fast Parallel Pseudo Random Number Generator with Applications

Cited by 3 publications

References 16 publications

CPU and/or GPU: Revisiting the GPU Vs. CPU Myth

CPU and/or GPU: Revisiting the GPU Vs. CPU Myth

Secrets from the GPU

A Survey of CPU-GPU Heterogeneous Computing Techniques

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