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Optimizing non-coalesced memory access for irregular applications with GPU computing.

Authors :
Zheng, Ran
Liu, Yuan-dong
Jin, Hai
Source :
Frontiers of Information Technology & Electronic Engineering; Sep2020, Vol. 21 Issue 9, p1285-1301, 17p
Publication Year :
2020

Abstract

General purpose graphics processing units (GPGPUs) can be used to improve computing performance considerably for regular applications. However, irregular memory access exists in many applications, and the benefits of graphics processing units (GPUs) are less substantial for irregular applications. In recent years, several studies have presented some solutions to remove static irregular memory access. However, eliminating dynamic irregular memory access with software remains a serious challenge. A pure software solution without hardware extensions or offline profiling is proposed to eliminate dynamic irregular memory access, especially for indirect memory access. Data reordering and index redirection are suggested to reduce the number of memory transactions, thereby improving the performance of GPU kernels. To improve the efficiency of data reordering, an operation to reorder data is offloaded to a GPU to reduce overhead and thus transfer data. Through concurrently executing the compute unified device architecture (CUDA) streams of data reordering and the data processing kernel, the overhead of data reordering can be reduced. After these optimizations, the volume of memory transactions can be reduced by 16.7%–50% compared with CUSPARSE-based benchmarks, and the performance of irregular kernels can be improved by 9.64%–34.9% using an NVIDIA Tesla P4 GPU. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20959184
Volume :
21
Issue :
9
Database :
Complementary Index
Journal :
Frontiers of Information Technology & Electronic Engineering
Publication Type :
Academic Journal
Accession number :
145948822
Full Text :
https://doi.org/10.1631/FITEE.1900262