PAMPAR: A new parallel benchmark for performance and energy consumption evaluation

Garcia, Adriano Marques; Schepke, Claudio; Girardi, Alessandro

doi:10.1002/cpe.5504

Cited by 4 publications

References 35 publications

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High‐performance computing for computational science

Gil-Costa

Senger

2020

Concurrency and Computation

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High-performance computing is the application of large-scale computer resources to solve computational problems that are either too large for standard computers or would take too long. Different parallel techniques, parallel and distributed programming libraries, and performance evaluation libraries are used to enhance the performance and to feature the execution of the algorithms. These techniques and tools are a valuable resource for anyone developing software codes for computational sciences. Moreover, all these techniques and tools have pushed the progress in several areas of science and engineering, which either demand large amounts of calculations or manipulate large volumes of data. This special issue focuses on efficient experimental solutions to problems on state-of-the-art computational systems consisting of large numbers of computational elements, including clusters, massively parallel supercomputers, and GPU-based systems. The objective is to open an opportunity for researchers to present and discuss new ideas and proposals for state-of-the art in HPC for computational science. The expected audience includes researchers and students in academic departments, government laboratories, and industrial organizations. This special issue

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High‐performance computing for computational science

Gil-Costa

Senger

2020

Concurrency and Computation

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The Impact of CPU Frequency Scaling on Power Consumption of Computing Infrastructures

Garcia

Serpa

Griebler

et al. 2020

Computational Science and Its Applications – ICCSA 2020

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Since the demand for computing power increases, new architectures emerged to obtain better performance. Reducing the power and energy consumption of these architectures is one of the main challenges to achieving high-performance computing. Current research trends aim at developing new software and hardware techniques to achieve the best performance and energy trade-offs. In this work, we investigate the impact of different CPU frequency scaling techniques such as ondemand, performance, and powersave on the power and energy consumption of multi-core based computer infrastructure. We apply these techniques in PAMPAR, a parallel benchmark suite implemented in PThreads, OpenMP, MPI-1, and MPI-2 (spawn). We measure the energy and execution time of 10 benchmarks, varying the number of threads. Our results show that although powersave consumes up to 43.1% less power than performance and ondemand governors, it consumes the triple of energy due to the high execution time. Our experiments also show that the performance governor consumes up to 9.8% more energy than ondemand for CPU-bound benchmarks. Finally, our results show that PThreads has the lowest power consumption, consuming less than the sequential version for memory-bound benchmarks. Regarding performance, the performance governor achieved 3% of performance over the ondemand.

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