Adapting Software Applications to Hybrid Supercomputer

The work(1) considers an approach to the adaptation of applications that use algorithms proven to be successful and developed with the help of computers that do not have a high degree of parallelism, though in a number of implementations they require a sharp reduction in the computing time. A natural way-out is to transfer the algorithm solution to a highly parallel heterogeneous processing environment, i.e. hybrid supercomputer. Unfortunately, the result does not always meet expectations. The challenge is the need to consider architectural features of the supercomputer and the corresponding translation of the generic algorithm, while maintaining its semantic features, i.e. the development of parallel software of the generic algorithm scalable to allocated supercomputer resources. Available approaches to the software parallelization deliver superb results when algorithms demonstrate obvious parallelism. Otherwise, their transformation to the parallel representation requires an analysis of dependencies in parallel threads on data and costs of the parallel supercomputer execution. Presented in this paper is an algorithm analysis technique that allows to determine fragments for a significant reduction in the computing time during the parallel execution. The result is an algorithm specification work schedule that ensures the effective solution, using the supercomputer. The schedule is used to create the dedicated control over the execution of parallelized algorithm for its effective solution with the help of hybrid supercomputer resources. The work shows results of the implementation of the developed technique in terms of the genetic research.