Graphics Processor Units: New Prospects for Parallel Computing

Rumpf, Martin; Strzodka, Robert

doi:10.1007/3-540-31619-1_3

Cited by 24 publications

(10 citation statements)

References 15 publications

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“…The adaptation of algorithms using sparse matrix operations which cope with scattered data accesses has been studied in different scenarios for a long time, since the CPU performance is also affected by the weakly structured patterns for memory reading and writing involved in these operations (Davis, 2006). Efforts in mapping this knowledge into GPU computations are the subject of increasing interest, both using extensions of CUDA or others platforms (Bolz et al, 2003;Rumpf and Strzodka, 2005).…”

Section: Matrix Algebramentioning

confidence: 98%

Performance evaluation of image processing algorithms on the GPU

Castaño-Díez

Moser

Schoenegger

et al. 2008

Journal of Structural Biology

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Section: Matrix Algebramentioning

confidence: 98%

Performance evaluation of image processing algorithms on the GPU

Castaño-Díez

Moser

Schoenegger

et al. 2008

Journal of Structural Biology

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“…These platforms make it possible to achieve speedups of an order of magnitude over a standard CPU in many applications and are growing in popularity [1,2]. Moreover, several programming toolkits such as CUDA [3] have been developed to facilitate the programming of GPUs for general purpose applications.…”

Section: Introductionmentioning

confidence: 99%

An MPI-CUDA implementation of an improved Roe method for two-layer shallow water systems

Asunción

Mantas

Castro

et al. 2012

Journal of Parallel and Distributed Computing

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The numerical solution of two-layer shallow water systems is required to simulate accurately stratified fluids, which are ubiquitous in nature: they appear in atmospheric flows, ocean currents, oil spills, etc. Moreover, the implementation of the numerical schemes to solve these models in realistic scenarios imposes huge demands of computing power. In this paper, we tackle the acceleration of these simulations in triangular meshes by exploiting the combined power of several CUDA-enabled GPUs in a GPU cluster. For that purpose, an improvement of a path conservative Roe-type finite volume scheme which is specially suitable for GPU implementation is presented, and a distributed implementation of this scheme which uses CUDA and MPI to exploit the potential of a GPU cluster is developed. This implementation overlaps MPI communication with CPU-GPU memory transfers and GPU computation to increase efficiency. Several numerical experiments, performed on a cluster of modern CUDA-enabled GPUs, show the efficiency of the distributed solver.

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“…Detailed introductions on the use of GPUs for scientific computing with OpenGL and high level graphics languages (GPGPU -general purpose computation on GPUs) can be found in several book chapters [30,52]. For tutorial code see Göddeke [23].…”

Section: Gpu Backgroundmentioning

confidence: 99%

Using GPUs to improve multigrid solver performance on a cluster

Göddeke¹,

Strzodka²,

Mohd-Yusof³

et al. 2008

IJCSE

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This article explores the coupling of coarse and fine-grained parallelism for Finite Element simulations based on efficient parallel multigrid solvers. The focus lies on both system performance and a minimally invasive integration of hardware acceleration into an existing software package, requiring no changes to application code. Because of their excellent price performance ratio, we demonstrate the viability of our approach by using commodity graphics processors (GPUs) as efficient multigrid preconditioners. We address the issue of limited precision on GPUs by applying a mixed precision, iterative refinement technique. Other restrictions are also handled by a close interplay between the GPU and CPU. From a software perspective, we integrate the GPU solvers into the existing MPI-based Finite Element package by implementing the same interfaces as the CPU solvers, so that for the application programmer they are easily interchangeable. Our results show that we do not compromise any software functionality and gain speedups of two and more for large problems. Equipped with this additional option of hardware acceleration we compare different choices in increasing the performance of a conventional, commodity based cluster by increasing the number of nodes, replacement of nodes by a newer technology generation, and adding powerful graphics cards to the existing nodes.

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Graphics Processor Units: New Prospects for Parallel Computing

Cited by 24 publications

References 15 publications

Performance evaluation of image processing algorithms on the GPU

Performance evaluation of image processing algorithms on the GPU

An MPI-CUDA implementation of an improved Roe method for two-layer shallow water systems

Using GPUs to improve multigrid solver performance on a cluster

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