Frontiers of Information Technology & Electronic Engineering
>> 2020,
Volume 21,
Issue 9
doi:
10.1631/FITEE.1900262
Optimizing non-coalesced memory access for irregular applications with GPU computing
Affiliation(s): National Engineering Research Center for Big Data Technology and System, Huazhong University of Science and Technology, Wuhan 430074, China; Services Computing Technology and System Lab, Huazhong University of Science and Technology, Wuhan 430074, China; Cluster and Grid Computing Lab, Huazhong University of Science and Technology, Wuhan 430074, China; School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China; less
Received: 2019-05-24
Accepted: 2020-09-09
Available online: 2020-09-09
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Abstract
(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. and index redirection are suggested to reduce the number of memory transactions, thereby improving the performance of GPU kernels. To improve the efficiency of , 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 and the data processing kernel, the overhead of 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.
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