Abstract
There is a variety of known HPC ratings nowadays which represent machine capability for solving a fixed problem, based on a certain algorithm, but these ratings represent a top of the iceberg, and as a rule, one can’t compare application tuning features even for the selected system, and the details of system architecture are not usually described precisely. At the same time lots of efforts are made to describe diverse algorithm features formally, AlgoWiki is one of the most notable recent projects. The idea of Algo500 is joining precise description of computer system with detailed formal descriptions of algorithms using implementation performance data, and building an engine over such joint base to allow various queries, thus giving means of building user-defined ratings regarding selected method, algorithms and/or computer platform features. This paper gives an overview of Algo500 design and some use cases.
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Acknowledgements
The results were obtained in Lomonosov Moscow State University with the financial support of the Russian Science Foundation (agreement N 20-11-20194). The research is carried out using the equipment of the shared research facilities of HPC computing resources at Lomonosov Moscow State University [15].
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Nikitenko, D., Antonov, A., Zheltkov, A., Voevodin, V. (2020). Describing HPC System Architecture for Understanding Its Capabilities. In: Voevodin, V., Sobolev, S. (eds) Supercomputing. RuSCDays 2020. Communications in Computer and Information Science, vol 1331. Springer, Cham. https://doi.org/10.1007/978-3-030-64616-5_37
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