Name
Abstract | ||
|---|---|---|
Recent years witness a trend of applying large-scale distributed deep learning algorithms (HPC AI) in both business and scientific computing areas, whose goal is to speed up the training time to achieve a state-of-the-art quality. The HPC AI benchmarks accelerate the process. Unfortunately, benchmarking HPC AI systems at scale raises serious challenges. This paper presents a comprehensive HPC AI b... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/Cluster48925.2021.00022 | 2021 IEEE International Conference on Cluster Computing (CLUSTER) |
Keywords | DocType | ISSN |
Measurement,Training,Codes,Scientific computing,Benchmark testing,Tools,Throughput | Conference | 1552-5244 |
ISBN | Citations | PageRank |
978-1-7281-9666-4 | 0 | 0.34 |
References | Authors | |
0 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zihan Jiang | 1 | 1 | 2.39 |
| Wanling Gao | 2 | 299 | 19.12 |
| Fei Tang | 3 | 17 | 7.36 |
| Lei Wang | 4 | 577 | 46.85 |
| Xingwang Xiong | 5 | 0 | 0.68 |
| Chunjie Luo | 6 | 434 | 21.86 |
| Chuanxin Lan | 7 | 1 | 0.70 |
| Hongxiao Li | 8 | 0 | 0.34 |
| Jianfeng Zhan | 9 | 767 | 62.86 |