Section: New Results

Scheduling independent tasks on multi-cores with GPU accelerators

More and more computers use hybrid architectures combining multi-core processors and hardware accelerators like GPUs (Graphics Processing Units). We present in this paper [3] 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 maximum completion time (makespan). The corresponding scheduling problem is NP-hard, we propose an efficient approximation algorithm which achieves an approximation ratio of $4/3+1/3k$ . We first detail and analyze the method, based on a dual approximation scheme, that uses dynamic programming to balance evenly the load between the heterogeneous resources. Then, we present a faster approximation algorithm for a special case of the previous problem, where all the tasks are accelerated when affected to GPU, with a performance guarantee of $3/2$ for any number of GPUs. We run some simulations based on realistic benchmarks and compare the solutions obtained by a relaxed version of the generic method to the one provided by a classical scheduling algorithm (HEFT). Finally, we present an implementation of the $4/3$-approximation and its relaxed version on a classical linear algebra kernel into the scheduler of the XKaapi runtime system.