A New Parallel Benchmark for Performance Evaluation and Energy Consumption

Garcia, Adriano Marques; Schepke, Claudio; Girardi, Alessandro; Silva, Sherlon Almeida da

doi:10.1007/978-3-030-15996-2_14

Cited by 2 publications

(2 citation statements)

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“…These results refer to running using 2, 4, 8, and 16 parallel threads/processes for each PPI. We did not get results using 20 parallel tasks because these were the first experiments and not all pseudo‐applications were prepared to do load balancing for numbers that are not a power of two at that time . They are currently able to run with any number of threads/processes, but we were not able to redo those experiments on time.…”

Section: Resultsmentioning

confidence: 99%

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

Garcia

Schepke

Girardi

2019

Concurrency and Computation

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Summary This paper presents PAMPAR, a new benchmark to evaluate the performance and energy consumption of different Parallel Programming Interfaces (PPIs). The benchmark is composed of 11 algorithms implemented in PThreads, OpenMP, MPI‐1, and MPI‐2 (spawn) PPIs. Previous studies have used some of these pseudo‐applications to perform this type of evaluation in different architectures since there is no benchmark that offers this variety of PPIs and communication models. In this work, we measure the energy and performance of each pseudo‐application in a single architecture, varying the number of threads/processes. We also organize the pseudo‐applications according to their memory accesses, floating‐point operations, and branches. The goal is to show that this set of pseudo‐applications has enough features to build a parallel benchmark. The results show that there is no single best case that provides both better performance and low energy consumption in the presented scenarios. Moreover, the pseudo‐applications usage of the system resources are different enough to represent different scenarios and be efficient as a benchmark.

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Section: Resultsmentioning

confidence: 99%

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

Garcia

Schepke

Girardi

2019

Concurrency and Computation

Self Cite

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“…Despite the serial code, each benchmark is implemented using well-known PPIs in the Computational Science field, such as PThreads, OpenMP, MPI-1, and MPI-2 (dynamic process spawn). The authors of the suite ran experiments to measure the performance and energy consumption of PAMPAR benchmarks to estimate the power and energy consumption of the PPIs without regard to the CPU-enabled governor [ 9 , 10 ]. In this paper, we explore this gap.…”

Section: Introductionmentioning

confidence: 99%

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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A New Parallel Benchmark for Performance Evaluation and Energy Consumption

Cited by 2 publications

References 9 publications

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

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

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

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