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DGX-A100 Face to Face DGX-2—Performance, Power and Thermal Behavior Evaluation
by
Jansík, Branislav
, Špeťko, Matej
, Vysocký, Ondřej
, Říha, Lubomír
in
Artificial intelligence
/ Communication
/ DGX-2
/ DGX-A100
/ DVFS
/ Energy efficiency
/ energy efficient computing
/ performance analysis
/ Power
/ Servers
/ Supercomputers
/ tensor cores
/ Transistors
/ Workloads
2021
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DGX-A100 Face to Face DGX-2—Performance, Power and Thermal Behavior Evaluation
by
Jansík, Branislav
, Špeťko, Matej
, Vysocký, Ondřej
, Říha, Lubomír
in
Artificial intelligence
/ Communication
/ DGX-2
/ DGX-A100
/ DVFS
/ Energy efficiency
/ energy efficient computing
/ performance analysis
/ Power
/ Servers
/ Supercomputers
/ tensor cores
/ Transistors
/ Workloads
2021
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Do you wish to request the book?
DGX-A100 Face to Face DGX-2—Performance, Power and Thermal Behavior Evaluation
by
Jansík, Branislav
, Špeťko, Matej
, Vysocký, Ondřej
, Říha, Lubomír
in
Artificial intelligence
/ Communication
/ DGX-2
/ DGX-A100
/ DVFS
/ Energy efficiency
/ energy efficient computing
/ performance analysis
/ Power
/ Servers
/ Supercomputers
/ tensor cores
/ Transistors
/ Workloads
2021
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DGX-A100 Face to Face DGX-2—Performance, Power and Thermal Behavior Evaluation
Journal Article
DGX-A100 Face to Face DGX-2—Performance, Power and Thermal Behavior Evaluation
2021
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Overview
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.
Publisher
MDPI AG
Subject
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