RT Journal Article
T1 Heuristics for concurrent task scheduling on GPUs
A1 López Albelda, Bernabé
A1 Lázaro Muñoz, Antonio José
A1 González-Linares, José María
A1 Guil-Mata, Nicolás
K1 Programación de ordenadores
K1 Software
AB 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.
PB Wiley
YR 2019
FD 2019
LK https://hdl.handle.net/10630/37993
UL https://hdl.handle.net/10630/37993
LA eng
NO López-Albelda B, Lázaro-Muñoz AJ, González-Linares JM, Guil N. Heuristics for concurrent task scheduling onGPUs. Concurrency Computat Pract Exper. 2020;32:e5571. https://doi.org/10.1002/cpe.5571
NO This work has been supported by the Ministry of Education of Spain (TIN2016-80920-R) and the Junta de Andalucía of Spain (TIC-1692). We also thank Nvidia for hardware donations within its GPU Grant Program.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026