Equidistant Memory Access Coalescing on GPGPU

With the massive processing power, GPGPU can execute thousands of threads in parallel at the cost of highmemory bandwidth to support the large number of concurrent memory requests. To alleviate the demands, GPGPU adopts memory access coalescing to reduce the memory requests issued to memory system. In this paper, we first introduced the concept of memory access distance, and classify GPGPU programs into three types according to their memory access distances. We discovered that programs with large but equal memory access distance are popular in GPGPU, which, however, cannot be optimized by the original memory access coalescing. Thus, we proposed equidistant memory access coalescing, which is able to merge requests with any equal memory access distance. We evaluated our method with 30 benchmarks. Compared with original memory access coalescing, equidistant memory access coalescing can improve performance of 19 benchmarks among them. For the benchmarks with equal and large memory access distance, the average speedup is 151% and the maximum speedup is 200%. The memory access requests are reduced to 32% on average.