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On the Complexity of Conditional DAG Scheduling in Multiprocessor Systems

Abstract : As parallel processing became ubiquitous in modern computing systems, parallel task models have been proposed to describe the structure of parallel applications. The workflow scheduling problem has been studied extensively over past years, focusing on multiprocessor systems and distributed environments (e.g. grids, clusters). In workflow scheduling, applications are modeled as directed acyclic graphs (DAGs). DAGs have also been introduced in the real-time scheduling community to model the execution of multi-threaded programs on a multi-core architecture. The DAG model assumes, in most cases, a fixed DAG structure capturing only straight-line code. Only recently, more general models have been proposed. In particular, the conditional DAG model allows the presence of control structures such as conditional (if-then-else) constructs. While first algorithmic results have been presented for the conditional DAG model, the complexity of schedulability analysis remains wide open. We perform a thorough analysis on the worst-case makespan (latest completion time) of a conditional DAG task under list scheduling (a.k.a. fixed-priority scheduling). We show several hardness results concerning the complexity of the optimization problem on multiple processors, even if the conditional DAG has a well-nested structure. For general conditional DAG tasks, the problem is intractable even on a single processor. Complementing these negative results, we show that certain practice-relevant DAG structures are very well tractable.
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https://hal.inria.fr/hal-03087716
Contributor : Marie-France Sagot <>
Submitted on : Thursday, December 24, 2020 - 10:51:15 AM
Last modification on : Tuesday, December 29, 2020 - 3:29:46 AM

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Alberto Marchetti-Spaccamela, Nicole Megow, Jens Schlöter, Martin Skutella, Leen Stougie. On the Complexity of Conditional DAG Scheduling in Multiprocessor Systems. IPDPS 2020 - IEEE International Parallel and Distributed Processing Symposium, May 2020, New Orleans / Virtual, United States. pp.1061-1070, ⟨10.1109/IPDPS47924.2020.00112⟩. ⟨hal-03087716⟩

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