StarPU: a unified platform for task scheduling on heterogeneous multicore architectures

In the field of HPC, the current hardware trend is to design multiprocessor architectures featuring heterogeneous technologies such as specialized coprocessors (e.g. Cell/BE) or data‐parallel accelerators (e.g. GPUs). Approaching the theoretical performance of these architectures is a complex issue....

Full description

Saved in:
Bibliographic Details
Published in:Concurrency and computation 2011-02, Vol.23 (2), p.187-198
Main Authors: Augonnet, Cédric, Thibault, Samuel, Namyst, Raymond, Wacrenier, Pierre-André
Format: Article
Language:English
Subjects:
accelerator
Algorithms
Architecture
Computation
Computer Science
Distributed, Parallel, and Cluster Computing
GPU
Hardware
Libraries
Mathematical models
multicore
Run time (computers)
runtime system
Scheduling
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In the field of HPC, the current hardware trend is to design multiprocessor architectures featuring heterogeneous technologies such as specialized coprocessors (e.g. Cell/BE) or data‐parallel accelerators (e.g. GPUs). Approaching the theoretical performance of these architectures is a complex issue. Indeed, substantial efforts have already been devoted to efficiently offload parts of the computations. However, designing an execution model that unifies all computing units and associated embedded memory remains a main challenge. We therefore designed StarPU, an original runtime system providing a high‐level, unified execution model tightly coupled with an expressive data management library. The main goal of StarPU is to provide numerical kernel designers with a convenient way to generate parallel tasks over heterogeneous hardware on the one hand, and easily develop and tune powerful scheduling algorithms on the other hand. We have developed several strategies that can be selected seamlessly at run‐time, and we have analyzed their efficiency on several algorithms running simultaneously over multiple cores and a GPU. In addition to substantial improvements regarding execution times, we have obtained consistent superlinear parallelism by actually exploiting the heterogeneous nature of the machine. We eventually show that our dynamic approach competes with the highly optimized MAGMA library and overcomes the limitations of the corresponding static scheduling in a portable way. Copyright © 2010 John Wiley & Sons, Ltd.
ISSN:1532-0626
1532-0634
1532-0634
DOI:10.1002/cpe.1631