A GPGPU PROGRAMMING FRAMEWORK BASED ON A SHARED-MEMORY MODEL

Kazuhiko Ohno, Dai Michiura, Masaki Matsumoto, Takahiro Sasaki, and Toshio Kondo

Keywords

Parallel programming language, compiler, GPU, CUDA, virtualshared memory

Abstract

Although general purpose computation on GPU (GPGPU) seems to be a promising method for high-performance computing, current programming frameworks such as CUDA and OpenCL are difficult and not portable enough. Therefore, we propose a new framework MESI-CUDA for easier GPGPU programming. MESI-CUDA provides shared variables which can be accessed from both CPU and GPU. Our compiler translates user’s shared-memory-based program into a CUDA program automatically generating the memory allocation and data transfer code. The compiler also overlaps kernel executions and data transfers by optimizing the scheduling. The evaluation results show that programs using MESI-CUDA can achieve the performance close to hand-optimized CUDA programs, largely reducing user’s coding cost.

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