Scheduling of Linear Algebra Kernels on Multiple Heterogeneous Resources
Langue
en
Communication dans un congrès
Ce document a été publié dans
International Conference on High Performance Computing, Data, and Analytics (HiPC 2016), 2016-12-19, Hyderabad. 2016-12
IEEE
Résumé en anglais
In this paper, we consider task-based dense linear algebra applications on a single heterogeneous node which contains regular CPU cores and a set of GPU devices. Efficient scheduling strategies are crucial in this context ...Lire la suite >
In this paper, we consider task-based dense linear algebra applications on a single heterogeneous node which contains regular CPU cores and a set of GPU devices. Efficient scheduling strategies are crucial in this context in order to achieve good and portable performance. HeteroPrio, a resource-centric dynamic scheduling strategy has been introduced in a previous work and evaluated for the special case of nodes with exactly two types of resources. However, this restriction can be limiting, for example on nodes with several types of accelerators, but not only this. Indeed, an interesting approach to increase resource usage is to group several CPU cores together, which allows to use intra-task parallelism. We propose a generalization of HeteroPrio to the case with several classes of heterogeneous workers. We provide extensive evaluation of this algorithm with Cholesky factorization, both through simulation and actual execution, compared with HEFT-based scheduling strategy, the state of the art dynamic scheduling strategy for heterogeneous systems. Experimental evaluation shows that our approach is efficient even for highly heterogeneous configurations and significantly outperforms HEFT-based strategy.< Réduire
Mots clés en anglais
Cholesky Factorization
StarPU
Resource Aggregation
Simulation
Task-based Scheduling
Heterogeneous Platforms
Linear Algebra
Project ANR
Solveurs pour architectures hétérogènes utilisant des supports d'exécution - ANR-13-MONU-0007
Origine
Importé de halUnités de recherche