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.

In this article, we present the ExaScale PaRallel finite element tearing and interconnecting SOlver (ESPRESO) finite element method (FEM) library, which includes an FEM toolbox with interfaces to professional and open-source simulation tools, and a massively parallel hybrid total finite element tearing and interconnecting (HTFETI) solver which can fully utilize the Oak Ridge Leadership Computing Facility Titan supercomputer and achieve superlinear scaling. This article presents several new techniques for finite element tearing and interconnecting (FETI) solvers designed for efficient utilization of supercomputers with a focus on (i) performance—we present a fivefold reduction of solver runtime for the Laplace equation by redesigning the FETI solver and offloading the key workload to the accelerator. We compare Intel Xeon Phi 7120p and Tesla K80 and P100 accelerators to Intel Xeon E5-2680v3 and Xeon Phi 7210 central processing units; and (ii) memory efficiency—we present two techniques which increase the efficiency of the HTFETI solver 1.8 times and push the limits of the largest possible problem ESPRESO that can solve from 124 to 223 billion unknowns for problems with unstructured meshes. Finally, we show that by dynamically tuning hardware parameters, we can reduce energy consumption by up to 33%.

This paper introduces two tools for manual energy evaluation and runtime tuning developed at IT4Innovations in the READEX project. The MERIC library can be used for manual instrumentation and analysis of any application from the energy and time consumption point of view. Besides tracing, MERIC can also change environment and hardware parameters during the application runtime, which leads to energy savings. MERIC stores large amounts of data, which are difficult to read by a human. The RADAR generator analyses the MERIC output files to find the best settings of evaluated parameters for each instrumented region. It generates a Open image in new window report and a MERIC configuration file for application production runs.