Communications in Computer and Information Science
This book constitutes the refereed proceedings of the Third Russian Supercomputing Days, RuSCDays 2017, held in Moscow, Russia, in September 2017. The 41 revised full papers and one revised short paper presented were carefully reviewed and selected from 120 submissions. The papers are organized in topical sections on parallel algorithms; supercomputer simulation; high performance architectures, tools and technologies.
Nowadays, the wide spectrum of Intel Xeon processors is available. The new Zen CPU architecture developed by AMD has extended the number of options for x86_64 HPC hardware. This large number of options makes the optimal CPU choice for HPC systems not a straightforward procedure. Such a co-design procedure should follow the requests from the end-users community. Modern computational materials science studies are among the major consumers of HPC resources worldwide. The VASP code is perhaps the most popular tool for these research. In this work, we discuss the benchmark metric and results based on a VASP test model that give us the possibility to compare different CPUs and to select best options with respect to time-to-solution and energy-to-solution criteria.
The most important factors of successful trading strategy are the decisions to sell or buy. We propose multi-classifier system for decision making in algorithmic trading, whose training is carried out in three stages. At the first stage, features set is calculated based on historical data. These can be oscillators and moments that used in technical analysis, other characteristics of time series, market indexes, etc. At the second stage, base classifiers are trained using genetic algorithms, and optimal feature set for each of them is selected. At the third stage, a voting ensemble is designed, weights of base classifiers are selected also using genetic algorithms. However, the usage of genetic algorithms requires considerable time for computing, so the proposed system is implemented in a parallel environment. Testing on real data confirmed that the proposed approach allows to build a decision-making system, the results of which significantly exceed the trading strategies based on indicators of technical analysis and other techniques of machine learning.