Scheduling independent Tasks on multi-cores with GPU accelerators

Abstract

More and more computers use hybrid architectures combining multi-core processors and hardware accelerators like GPUs (Graphics Processing Units). We present in This paper a new method for scheduling efficiently parallel applications with m CPUs and k GPUs, where each Task of The application can be processed either on a core (CPU) or on a GPU. The objective is To minimize The makespan. The corresponding scheduling problem is NP-hard, we propose an efficient approximation algorithm which achieves an approximation ratio of. We first detail and analyze The method, based on a dual approximation scheme, That uses a dynamic programming scheme To balance evenly The load between The heterogeneous resources. Finally, we run some simulations based on realistic benchmarks and compare The solution obtained by a relaxed version of This method To The one provided by a classical greedy algorithm and To lower bounds on The value of The optimal makespan. © 2014 Springer-Verlag Berlin Heidelberg.