AuthorsH. Su, X. Cai, M. Wen and C. Zhang
TitleAn Analytical GPU Performance Model for 3D Stencil Computations from the Angle of Data Traffic
AfilliationScientific Computing, ,
Project(s)Center for Biomedical Computing (SFF)
StatusPublished
Publication TypeJournal Article
Year of Publication2015
JournalThe Journal of Supercomputing
Volume71
Issue7
Pagination2433-2453
Date Published02/2015
PublisherSpringer
ISSN0920-8542
Keywords3D stencil methods, GPU, performance modeling
Abstract

The achievable GPU performance of many scientific computations is not determined by a GPU's peak floating-point rate, but rather how fast data are moved through different stages of the entire memory hierarchy. We take low-order 3D stencil computations as a representative class to study the reachable GPU performance from the angle of data traffic. Specifically, we propose a simple analytical model to estimate the execution time based on quantifying the data traffic volume at three stages: (1) between registers and on-SMX storage, (2) between on-SMX storage and L2 cache, (3) between L2 cache and GPU's device memory. Three associated granularities are used: a CUDA thread, a thread block, and a set of simultaneously active thread blocks. For four 3D stencil computations, NVIDIA's profiling tools have been used to verify the accuracy of the quantified data traffic volumes, by trying a large number of executions with different problem sizes and thread block configurations. Moreover, by introducing an imbalance coefficient, together with the known realistic memory bandwidths, we can predict the execution time usage based on the quantified data traffic volumes. For the four 3D stencils, the average error of the time prediction is 6.9% for a baseline implementation approach, whereas for a blocking implementation approach the average prediction error is 9.5%.

URLhttp://link.springer.com/article/10.1007/s11227-015-1392-1
DOI10.1007/s11227-015-1392-1
Citation Key23328

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