2018Concurrency and Computation Practice and ExperienceRequires access

Evaluating optimizations that reduce global memory accesses of stencil computations in GPGPUs

Thiago Carrijo Nasciutti, Jairo Panetta, Pedro Pais Lopes

Open publisher page 9 citations

Abstract

Summary This work compares the performance of optimizations that transform replicated global memory accesses into local memory accesses on 3D stencil computations in the NVIDIA Tesla K80 GPGPU. The optimizations reduce global memory contention caused by the set of multiprocessors. Evaluated optimizations are grid tiling, inserting spatial and temporal loops into kernels, register reuse, and some of their combinations. A standardized experiment evaluates performance variation with grid size and stencil size for each optimization. Experimental data show that codes that use these optimizations are up to 3.3 times faster than the classical stencil formulation. It also shows that the most profitable optimization varies with grid and stencil sizes.

About this research paper

What this paper is about

Summary This work compares the performance of optimizations that transform replicated global memory accesses into local memory accesses on 3D stencil computations in the NVIDIA Tesla K80 GPGPU. The optimizations reduce global memory contention caused by the set of multiprocessors. Evaluated optimizations are grid tiling, inserting spatial and temporal loops into kernels, register reuse, and some of their combinations. A standardized experiment evaluates performance variation with grid size and stencil size for each optimization. Experimental data show that codes that use these optimizations are up to 3.3 times faster than the classical stencil formulation. It also shows that the most profitable optimization varies with grid and stencil sizes.

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OpenAlex reports 9 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Summary This work compares the performance of optimizations that transform replicated global memory accesses into local memory accesses on 3D stencil computations in the NVIDIA Tesla K80 GPGPU. The optimizations reduce global memory contention caused by the set of multiprocessors. Evaluated optimizations are grid tiling, inserting spatial and temporal loops into kernels, register reuse, and some of their combinations. A standardized experiment evaluates performance variation with grid size and stencil size for each optimization. Experimental data show that codes that use these optimizations are up to 3.3 times faster than the classical stencil formulation. It also shows that the most profitable optimization varies with grid and stencil sizes.

Key concepts: Stencil, Parallel computing, Computer science, Grid, Computation, Computational science, Supercomputer, Algorithm

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