Performance, Power Consumption and Thermal Behavioral Evaluation of the DGX-2 Platform
Matěj Špeťko, Lubomı́r Řı́ha, Branislav Jansı́k · Advances in parallel computing · 2020
In this paper, we evaluate the performance, power consumption and its variation and also thermal behavior of the DGX-2 server from Nvidia.We present a development of specialized synthetic benchmarks to measure raw performance of GPUs for single, double, half precision and also Tensor Core units.With these benchmarks, we were able to reach peak performance and verify the specification provided by Nvidia.We achieved 130.79 TFLOPS peak performance in halfprecision on Tensor Cores.We also measured the thermal stability of the DGX-2 system.It can hold its peak performance when all 16 GPUs are fully loaded except Tensor Core workload, when thermal throttling occurred with with up to 1 % performance penalty.During single-precision workload we observed 23 % variation of the power consummation of individual GPUs installed in the system.Finally, we have evaluated the behavior of the Tesla V100-SXM3 chip under the DVFS tuning.Running at optimal frequency, the compute bound workload can save up to 39% energy while the run-time increases by 51 %.More importantly, memory bound workload can save up to 31 % with 2 % throughput penalty and during the communication over NVLink one can save up to 26 % energy with no penalty.