An OpenACC Optimizer for Accelerating Histogram Computation on a GPU
Kei Ikeda, Fumihiko Ino, Kenichi Hagihara · 2016
This paper presents a source-to-source OpenACC optimizer that automatically optimizes a histogram computation code for a graphics processing unit (GPU). Parallel histogram computation codes typically deploy multiple copies of histograms and update them with atomic operations. This duplication method can be implemented as an OpenACC code. However, the structure of sequential code blocks must be manually rewritten owing to the limitation on OpenACC directives. Such a rewritten code does not always achieve the highest performance on arbitrary platforms, and thus, the duplication method degrades the performance portability of the code. To tackle this issue, we propose an optimizer that identifies histogram-related blocks in a naive OpenACC code and automatically rewrites the detected blocks such that multiple copies of histograms can be exploited for acceleration. In experiments, we apply our optimizer to three practical applications and investigate their performance on three platforms: an NVIDIA GPU, an AMD GPU and an Intel CPU. Experimental results show that our automated approach is useful for OpenACC codes to maximize the performance of histogram computation, and thereby enhancing the performance portability of the code.