Heuristics for concurrent task scheduling on GPUs

Abstract

Summary Concurrent execution of tasks in GPUs can reduce the computation time of a workload by overlapping data transfer and execution commands. However, it is difficult to implement an efficient runtime scheduler that minimizes the workload makespan as many execution orderings should be evaluated. In this paper, we employ scheduling theory to build a model that takes into account the device capabilities, workload characteristics, constraints, and objective functions. In our model, GPU tasks scheduling is reformulated as a flow shop scheduling problem, which allow us to apply and compare well‐known heuristics already developed in the operations research field. In addition, we develop a new heuristic, specifically focused on executing GPU commands, that achieves better scheduling results than previous ones. It leverages on a precise GPU command execution model for both computation and data transfers to carry out more advantageous scheduling decisions. A comprehensive evaluation, showing the suitability and robustness of this new approach, is conducted in three different NVIDIA architectures (Kepler, Maxwell, and Pascal). Results confirm the proposed heuristic achieves the best results in more than 90% of the experiments. Furthermore, a comparison has been made with MPS (Multi‐Process Service), the NVIDIA API that deals with the execution of concurrent tasks, which shows that our solution obtains speed‐ups ranging from 1.15 to 1.20.

High‐performance computing for computational science

“…López‐Albelda et al 6 presented a task scheduling model for GPUs, which is based on the flow shop scheduling problem. The model can predict the execution times of tasks, which use CUDA streams to launch multiple independent kernels to execute in the same GPU context.…”