This paper formulates the program runtime prediction problem subject to algorithm parameters and characteristics of a computational system to be used to run the algorithm. A two-step method of problem solution using linear and non-linear machine learning algorithms is proposed. This paper features a comparative analysis of runtime prediction results for solution of several linear algebra problems on 84 personal computers and servers using a number of machine learning algorithms. Use of a random forest combined with the linear least square method shows an error of less than 15% for most computational systems of similar architecture.

Aleksey Sidnev

Lobachevsky State University of Nizhni Novgorod

Master of Science, Faculty of Computational Mathematics and Cybernetics, Lobachevsky State University of Nizhni Novgorod (2009).

Research interests: system programming, optimization and performance analysis, parallel computing, machine learning.

Since 2008 I have been working at the Department of Computer Software faculty CMC UNN. Worked in projects related to parallel computing, the development of distributed systems and monitoring tool. Led a team to develop software optimization tool.