0000000000368016

AUTHOR

Bertrand Simon

showing 3 related works from this author

Online Scheduling of Task Graphs on Hybrid Platforms

2018

Modern computing platforms commonly include accelerators. We target the problem of scheduling applications modeled as task graphs on hybrid platforms made of two types of resources, such as CPUs and GPUs. We consider that task graphs are uncovered dynamically, and that the scheduler has information only on the available tasks, i.e., tasks whose predecessors have all been completed. Each task can be processed by either a CPU or a GPU, and the corresponding processing times are known. Our study extends a previous \(4\sqrt{m/k}\)-competitive online algorithm [2], where m is the number of CPUs and k the number of GPUs (\(m\ge k\)). We prove that no online algorithm can have a competitive ratio …

020203 distributed computingCompetitive analysisonline algorithmsComputer scienceHeuristicSchedulingSymmetric multiprocessor system02 engineering and technologyParallel computingUpper and lower boundsheterogeneous computingGraph020202 computer hardware & architectureScheduling (computing)task graphs0202 electrical engineering electronic engineering information engineeringOnline algorithm[INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC]
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Scheduling on Two Types of Resources: a Survey

2020

International audience; 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. Contrary to most existing approaches, we distinguish the allocation and the scheduling phases and we mainly focus on the allocation part of the problem: choose the most appropriate type of computing unit for each task. We address both off-line and on-line settings and design 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 for solving the allocation problem. Then, we refine the scheduling phase …

FOS: Computer and information sciences020203 distributed computingScheduleGeneral Computer ScienceComputer scienceDistributed computingmedia_common.quotation_subject0102 computer and information sciences02 engineering and technology01 natural sciencesTheoretical Computer ScienceScheduling (computing)Computer Science - Distributed Parallel and Cluster Computing010201 computation theory & mathematics0202 electrical engineering electronic engineering information engineeringQuality (business)Distributed Parallel and Cluster Computing (cs.DC)[INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC]Implementationmedia_common
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Online Scheduling of Task Graphs on Heterogeneous Platforms

2020

Modern computing platforms commonly include accelerators. We target the problem of scheduling applications modeled as task graphs on hybrid platforms made of two types of resources, such as CPUs and GPUs. We consider that task graphs are uncovered dynamically, and that the scheduler has information only on the available tasks, i.e., tasks whose predecessors have all been completed. Each task can be processed by either a CPU or a GPU, and the corresponding processing times are known. Our study extends a previous $4\sqrt{m/k}$ 4 m / k -competitive online algorithm by Amaris et al. [1] , where $m$ m is the number of CPUs and $k$ k the number of GPUs ( $m\geq k$ m ≥ k ). We prove that no online…

Discrete mathematics[INFO.INFO-CC]Computer Science [cs]/Computational Complexity [cs.CC]020203 distributed computingScheduleCompetitive analysisComputer scienceHeuristicSchedulingOnline algorithmsProcessor schedulingSymmetric multiprocessor system02 engineering and technologyUpper and lower boundsGraphScheduling (computing)Computational Theory and MathematicsHardware and ArchitectureSignal Processing0202 electrical engineering electronic engineering information engineeringTask analysisTask graphsHeterogeneous computingOnline algorithm[INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC]
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