СПОСІБ РОЗРОБКИ ІЗОЕФЕКТИВНОЇ ГЕТЕРОГЕННОЇ СИСТЕМИ НА ОСНОВІ МАШИННОГО НАВЧАННЯ ДЛЯ ЗАДАЧІ ДИСКРЕТНОГО ПЕРЕТВОРЕННЯ ФУР’Є

Abstract

In this paper, the isoefficiency of MPP systems and heterogeneous CPU-GPU systems on the problem of discrete Fourier transform is considered. The development of parallel applications as its goal can not only reduce execution time, but also provide opportunities to solve problems of a larger dimension. The peculiarity of algorithm parallelization includes the efficient use of hardware while increasing the dimension of the problem is an important characteristic of parallel computing. However, currently heterogeneous systems have not been researched extensively to determine isoefficiency characteristics and build application-specific systems around said method, although there are articles that show potential using isoefficiency to design the system and using heterogeneous approach to accelerate performance of different tasks. Discrete Fourier Transform algorithm lets build systems that discretize analogue and digital signals and it can serve as a benchmark to test different systems. Algorithms suited for MPP systems can use analytical approach to find out issoefficiency function and to determine how scaling the system or changing the size of the task will change its performance metrics. One of the most popular approaches to linking up processing units in MPP systems is using hypercube topology. MPP system that is connected using this topology will be analyzed. CPU-GPU heterogeneous system will be analyzed using an approach based on polynomial regression. Due to the nature of heterogeneous systems, analytic approach used in MPP system is impossible. Predictive model based on polynomial regression will use modelling results from using CPU and GPU separately to estimate how much time it will take for heterogeneous system to finish the task. To ensure accuracy of the experiment, several systems will be used to model the task. Using this approach, resulting issoefficient heterogeneous system will be analyzed using performance metrics s