POSTER

Abstract

Various existing optimization and memory consistency management techniques for GPU applications rely on memory access patterns of kernels. However, they suffer from poor practicality because they require explicit user interventions to extract kernel memory access patterns. This paper proposes an automatic memory-access-pattern analysis framework called MAPA. MAPA is based on a source-level analysis technique derived from traditional symbolic analyses and a run-time pattern selection technique. The experimental results show that MAPA properly analyzes 116 real-world OpenCL kernels from Rodinia and Parboil.