【摘 要】
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General purpose graphics processing units (GPGPUs) can be used to improve computing performance considerably for regular applications. However, irregular memory
【机 构】
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National Engineering Research Center for Big Data Technology and System, Huazhong University of Scie
论文部分内容阅读
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 offiine 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 keels. To improve the efficiency of data reordering, an operation to reorder data is offioaded 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 keel, 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 keels can be improved by 9.64％–34.9％using an NVIDIA Tesla P4 GPU.
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