| Budget Amount *help |
¥15,990,000 (Direct Cost: ¥12,300,000、Indirect Cost: ¥3,690,000)
Fiscal Year 2018: ¥3,120,000 (Direct Cost: ¥2,400,000、Indirect Cost: ¥720,000)
Fiscal Year 2017: ¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
Fiscal Year 2016: ¥3,120,000 (Direct Cost: ¥2,400,000、Indirect Cost: ¥720,000)
Fiscal Year 2015: ¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
Fiscal Year 2014: ¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
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| Outline of Final Research Achievements |
The GPU (Graphics Processing Unit) is a specialized circuit designed to accelerate computation for building and manipulating images. Latest GPUs are designed for general purpose computing and can perform computation in applications traditionally handled by the CPU. The main purpose of this research is to propose appropriate theoretical models for GPU computing, develop efficient parallel algorithms based on the theoretical models, and evaluate the performance. We have developed theoretical models, Discrete Memory Machine model, Unified Memory Machine model, and Hierarchical Memory Machine model which capture the essence of memory access to the shared memory and the global memory of the GPU. Based on these models, we have developed many efficient algorithms on the GPU. In particular, we have developed a new technique that we call SKSS (Single Kernel Soft Synchronization) technique. We have shown that this technique can be applied to accelerate dynamic programming algorithms on the GPU.
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