Performance and Scalability of Materials Science and Machine Learning Codes on the State-of-Art Hybrid Supercomputer Architecture

Авторы: Kondratyuk Nikolay, Smirnov Grigory, Agarkov Alexander, Osokin Anton, Nikolskiy Vsevolod, Semenov Alexander, Stegailov Vladimir
Сборник: Communications in Computer and Information Science
Год издания: 2019
Место издания: Springer International Publishing Cham
Первая страница: 597
Последняя страница: 609
DOI: 10.1007/978-3-030-36592-9_49
Аннотация: 8 of top 10 supercomputers of Top500 list published in November 2018 consist of computing nodes with hybrid architectures that require special programming techniques. 5 systems among these are based on Nvidia GPUs. In this paper, we consider the benchmark results of the brand new hybrid supercomputer installed in March 2019 in NRU HSE. This system gives us the possibility to estimate the performance of several widely used material science and machine learning codes that we discuss in this work within the framework of the results available for older HPC systems.
Добавил в систему: Семенов Александр Сергеевич

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