2019/07/09 by Hamidreza Khaleghzadeh, Muhammad Fahad, Khaleghzadeh, Hamidreza +7
Computer Science · Engineering · #Cloud Computing and Resource Management #Distributed #Distributed and Parallel Computing Systems #FOS: Computer and information sciences #FOS: Electrical engineering #Parallel #Parallel Computing and Optimization Techniques #Performance (cs.PF) #Systems and Control (eess.SY) #and Cluster Computing (cs.DC) #cs.DC #cs.PF #cs.SY #eess.SY #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1907.04080
arxiv created 2019/07/09 · openalex publication_date 2019/07/09 · arxiv updated 2019/07/10 · openalex created_date 2019/07/23 · openalex updated_date 2026/07/28
Performance and energy are the two most important objectives for optimisation on modern parallel platforms. Latest research demonstrated the importance of workload distribution as a decision variable in the bi-objective optimisation for performance and energy on homogeneous multicore clusters. We show in this work that bi-objective optimisation for performance and energy on heterogeneous processors results in a large number of Pareto-optimal optimal solutions (workload distributions) even in the simple case of linear performance and energy profiles. We then study performance and energy profiles of real-life data-parallel applications and find that their shapes are non-linear, complex and non-smooth. We, therefore, propose an efficient and exact global optimisation algorithm, which takes as an input most general discrete performance and dynamic energy profiles of the heterogeneous processors and solves the bi-objective optimisation problem. The algorithm is also used as a building block to solve the bi-objective optimisation problem for performance and total energy. We also propose a novel methodology to build discrete dynamic energy profiles of individual computing devices, which are input to the algorithm. The methodology is based purely on system-level measurements and addresses the fundamental challenge of accurate component-level energy modelling of a hybrid data-parallel application running on a heterogeneous platform integrating CPUs and accelerators. We experimentally validate the proposed method using two data-parallel applications, matrix multiplication and 2D fast Fourier transform (2D-FFT).