| Budget Amount *help |
¥17,030,000 (Direct Cost: ¥13,100,000、Indirect Cost: ¥3,930,000)
Fiscal Year 2019: ¥2,470,000 (Direct Cost: ¥1,900,000、Indirect Cost: ¥570,000)
Fiscal Year 2018: ¥5,720,000 (Direct Cost: ¥4,400,000、Indirect Cost: ¥1,320,000)
Fiscal Year 2017: ¥3,640,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥840,000)
Fiscal Year 2016: ¥5,200,000 (Direct Cost: ¥4,000,000、Indirect Cost: ¥1,200,000)
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| Outline of Final Research Achievements |
In this work, we developed software techniques for exploiting milliseconds of idle periods that occur on remote graphics hardware (i.e., GPU) in home and office. The developed techniques are capable of accelerating compute-intensive scientific computation on shared GPUs while providing fast screen updating on the GPUs. We further developed a program translator that automatically generates parallel programs for multi-GPU environments, and demonstrated that the GPUs in home and office are useful for accelerating applications, such as distributed deep learning, that involve communication between computing nodes.
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