The Co-design of Astrophysical Code for Massively Parallel Supercomputers

Glinsky, B.; Kulikov, Igor; Chernykh, Igor; Weins, Dmitry; Снытников, А. В.; Nenashev, Vladislav; Andreev, Andrey; Егунов, В. А.; Kharkov, Egor

doi:10.1007/978-3-319-49956-7_27

Cited by 11 publications

(1 citation statement)

References 23 publications

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“…In modern computing systems, parallelism can be implemented at different levels, and different types of parallelism can be used, often several within the same computing architecture [42,43]. Accordingly, research is being conducted on the adaptation of wellknown numerical methods for effective implementation on modern parallel architectures, or to create completely alternative algorithms, the effectiveness of implementing algorithms on various parallel architectures is evaluated, for example, in [44,45].…”

Section: Basic Transformationmentioning

confidence: 99%

The Software Cache Optimization-Based Method for Decreasing Energy Consumption of Computational Clusters

Kravets

Егунов

2022

Energies

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Reducing the consumption of electricity by computing devices is currently an urgent task. Moreover, if earlier this problem belonged to the competence of hardware developers and the design of more cost-effective equipment, then more recently there has been an increased interest in this issue on the part of software developers. The issues of these studies are extensive. From energy efficiency issues of various programming languages to the development of energy-saving software for smartphones and other gadgets. However, to the best of our knowledge, no study has reported an analysis of the impact of cache optimizations on computing devices’ power consumption. Hence, this paper aims to provide an analysis of such impact on the software energy efficiency using the original software design procedure and computational experiments. The proposed Software Cache Optimization (SCO)-based Methodology was applied to one of the key linear algebra transformations. Experiments were carried out to determine software energy efficiency. RAPL (Running Average Power Limit) was used—an interface developed by Intel, which provides built-in counters of Central Processing Unit (CPU) energy consumption. Measurements have shown that optimized software versions reduce power consumption up to 4 times in relation to the basic transformation scheme. Experimental results confirm the effectiveness of the SCO-based Methodology used to reduce energy consumption and the applicability of this technique for software optimization.

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