Name
Abstract | ||
|---|---|---|
Field-programmable gate arrays (FPGAs) have garnered significant interest in research on high-performance computing because their computation and communication capabilities have drastically improved in recent years due to advances in semiconductor integration technologies that rely on Moore's Law. These improvements reveal the possibility of implementing a concept to enable on-the-fly offloading computation at which CPUs/GPUs perform poorly to FPGAs while performing low-latency data movement. We think that this concept is key to improving the performance of heterogeneous supercomputers using accelerators such as the GPU. In this paper, we propose a GPU-FPGA-accelerated simulation based on the concept and show preliminary results of the proposed concept. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/CLUSTER49012.2020.00060 | 2020 IEEE International Conference on Cluster Computing (CLUSTER) |
Keywords | DocType | ISSN |
heterogeneous supercomputers,low-latency data movement,CPU-GPUs,OpenACC-enabled GPU-FPGA accelerated computing,high-performance computing,field-programmable gate arrays,GPU-FPGA-accelerated simulation,on-the-fly offloading computation,Moore law,semiconductor integration technologies,communication capabilities | Conference | 1552-5244 |
ISBN | Citations | PageRank |
978-1-7281-6678-0 | 0 | 0.34 |
References | Authors | |
2 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Norihisa Fujita | 1 | 22 | 7.20 |
| Ryohei Kobayashi | 2 | 10 | 3.34 |
| Yoshiki Yamaguchi | 3 | 231 | 34.53 |
| Kohji Yoshikawa | 4 | 4 | 1.94 |
| Makito Abe | 5 | 4 | 1.60 |
| Masayuki Umemura | 6 | 17 | 4.87 |