| Budget Amount *help |
¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2016: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2015: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2014: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
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| Outline of Final Research Achievements |
Emerging NVM (Non-Volitle Memory) devices such as Flash, which have positive aspects of inexpensive cost, high-energy-efficiency, and huge capacity compared with conventional DRAM devices, as well, as negative aspects of low throughput and latency, are widely employed to existing supercomputers and clouds. However, efficient implementation techniques and its productivity to overcome deepening memory hierarchy are open problems, although these NVMs will greatly expand the possibility of processing extremely large-scale datasets that exceed the DRAM capacity of the nodes. In order to address the issues, we investigated the programming model for NVM toward extreme data-intensive computing. Based on our GPU-based MapReduce implementation, we enhanced out-of-core features of the implementation, including various Big Data Kernels such as Sort, PrefixSum, Unique, SetIntersection, and demonstrated efficient performance to datasets that exceed the DRAM capacity of the nodes.
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