To Distribute or Not to Distribute: The Question of Load Balancing for Performance or Energy

Abstract

Heterogeneous systems are nowadays a common choice in the path to Exascale. Through the use of accelerators they offer outstanding energy efficiency. The programming of these devices employs the host-device model, which is suboptimal as CPU remains idle during kernel executions, but still consumes energy. Making the CPU contribute computing effort might improve the performance and energy consumption of the system. This paper analyses the advantages of this approach and sets the limits of when its beneficial. The claims are supported by a set of models that determine how to share a single data-parallel task between the CPU and the accelerator for optimum performance, energy consumption or efficiency. Interestingly, the models show that optimising performance does not always mean optimum energy or efficiency as well. The paper experimentally validates the models, which represent an invaluable tool for programmers when faced with the dilemma of whether to distribute their workload in these systems.

EngineCL: Usability and Performance in Heterogeneous Computing

“…To overcome this problem, it is necessary to propose a paradigm change in the programming model and to encourage dataparallelism. This is achieved through co-execution, defined as the collaboration of all the devices in the system (including the CPU) to execute a single massive data-parallel kernel [4,5,6]. However, it is a hard task for the programmer and needs to be done effortless in order to be widely used.…”