Although today’s graphics processing units (GPUs) have high performance and general-purpose computing on GPUs (GPGPU) is actively studied, developing GPGPU applications remains difficult for two reasons. First, both parallelization and optimization of GPGPU ap- plications is necessary to achieve high performance. Second, the suitabil- ity of the target application for GPGPU must be determined, because whether an application performs well with GPGPU heavily depends on its inherent properties, which are not obvious from the source code. To overcome these difficulties, we developed a skeletal parallel programming framework for rapid GPGPU application developments. It enables pro- grammers to easily write GPGPU applications and rapidly test them because it generates programs for both GPUs and CPUs from the same source code. It also provides an optimization mechanism based on fusion transformation. Its effectiveness was confirmed experimentally.
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