Generic algorithms for scheduling applications on heterogeneous platforms

• Published in: Concurrency and computation Vol. 31; no. 15; pp. 1 - n/a
• Main Authors: Amaris, Marcos, Lucarelli, Giorgio, Mommessin, Clément, Trystram, Denis
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc 10.08.2019; Wiley
• Subjects: Accelerators; Algorithms; approximation algorithms; Computation; Computer Science; Computer simulation; Distributed, Parallel, and Cluster Computing; Graphical representations; heterogeneity; Linear programming; Lower bounds; Microprocessors; mixed CPU/GPU benchmark; precedence constraints; Production scheduling; scheduling; Task scheduling
• ISSN: 1532-0626; 1532-0634
• DOI: 10.1002/cpe.4647

Summary We study the problem of executing an application represented by a precedence task graph on a parallel machine composed of standard computing cores and accelerators. Both off‐line and on‐line settings are addressed by proposing generic scheduling approaches. In the first case, we establish strong lower bounds on the worst‐case performance of a known approach based on Linear Programming and replace the greedy List Scheduling policy used in this approach by a better task ordering. Although this modification leads to the same approximability guarantees, it performs much better in practice. We also extend this algorithm to more types of computing units, achieving an approximation ratio which depends on the number of different types. In the on‐line case, tasks arrive in any order which respects the precedence relations and the scheduler has to take irrevocable decisions about their allocation and execution. We propose the first on‐line scheduling algorithm taking into account precedences, which is based on adequate rules for selecting the type of processor where to allocate the tasks. Finally, all the previous algorithms have been experimented on a large number of simulations built on actual libraries, assessing their good practical behavior with respect to the state‐of‐the‐art solutions and baseline algorithms.