Name
Abstract | ||
|---|---|---|
Practical deep learning applications require more and more computing power. New computing architectures emerge, specifically designed for the artificial intelligence applications, including the IBM Power System AC922. In this paper we confront an AC922 (8335-GTG) server equipped with 4 NVIDIA Volta V100 GPUs with selected deep neural network training applications, including four convolutional and one recurrent model. We report performance results depending on batch sizes and GPU selection and compare them with the results from another contemporary workstation based on the same set of GPUs - NVIDIA® DGX Station
<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">™</sup>
. The results show that the AC922 performs better in all tested configurations, achieving improvements up to 10.3%. Profiling indicates that the improvement is due to the efficient I/O pipeline. The performance differences depend on the specific model, rather than on the model class (RNN/CNN). Both systems offer good scalability up to 4 GPUs. In certain cases there is a significant difference in performance depending on exactly which GPUs are used for computations. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/HPCS48598.2019.9188164 | 2019 International Conference on High Performance Computing & Simulation (HPCS) |
Keywords | DocType | ISBN |
Benchmarking,CNN,RNN,AC922,domain-specific architecture | Conference | 978-1-7281-4485-6 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pawel Rosciszewski | 1 | 13 | 2.74 |
| Michal Iwanski | 2 | 0 | 0.34 |
| Pawel Czarnul | 3 | 121 | 21.11 |