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DGX-A100 Face to Face DGX-2—Performance, Power and Thermal Behavior Evaluation

Author

Listed:
  • Matej Špeťko

    (IT4Innovations National Supercomputing Center, VŠB—Technical University of Ostrava, 708 00 Ostrava, Czech Republic)

  • Ondřej Vysocký

    (IT4Innovations National Supercomputing Center, VŠB—Technical University of Ostrava, 708 00 Ostrava, Czech Republic)

  • Branislav Jansík

    (IT4Innovations National Supercomputing Center, VŠB—Technical University of Ostrava, 708 00 Ostrava, Czech Republic)

  • Lubomír Říha

    (IT4Innovations National Supercomputing Center, VŠB—Technical University of Ostrava, 708 00 Ostrava, Czech Republic)

Abstract

Nvidia is a leading producer of GPUs for high-performance computing and artificial intelligence, bringing top performance and energy-efficiency. We present performance, power consumption, and thermal behavior analysis of the new Nvidia DGX-A100 server equipped with eight A100 Ampere microarchitecture GPUs. The results are compared against the previous generation of the server, Nvidia DGX-2, based on Tesla V100 GPUs. We developed a synthetic benchmark to measure the raw performance of floating-point computing units including Tensor Cores. Furthermore, thermal stability was investigated. In addition, Dynamic Frequency and Voltage Scaling (DVFS) analysis was performed to determine the best energy-efficient configuration of the GPUs executing workloads of various arithmetical intensities. Under the energy-optimal configuration the A100 GPU reaches efficiency of 51 GFLOPS/W for double-precision workload and 91 GFLOPS/W for tensor core double precision workload, which makes the A100 the most energy-efficient server accelerator for scientific simulations in the market.

Suggested Citation

  • Matej Špeťko & Ondřej Vysocký & Branislav Jansík & Lubomír Říha, 2021. "DGX-A100 Face to Face DGX-2—Performance, Power and Thermal Behavior Evaluation," Energies, MDPI, vol. 14(2), pages 1-18, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:2:p:376-:d:478628
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    Cited by:

    1. Baty, Hubert & Drui, Florence & Helluy, Philippe & Franck, Emmanuel & Klingenberg, Christian & Thanhäuser, Lukas, 2023. "A robust and efficient solver based on kinetic schemes for Magnetohydrodynamics (MHD) equations," Applied Mathematics and Computation, Elsevier, vol. 440(C).

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