Linear-Time Approximation Schemes for Scheduling Malleable Parallel Tasks

Jansen, Klaus; Porkolab, Lorant

doi:10.1007/s00453-001-0085-8

Cited by 69 publications

(49 citation statements)

References 10 publications

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“…Using dual-approximation techniques, Mounie et al [22] presented a 1.5-approximation algorithm for moldable tasks while assuming that the total area of a task is a non-decreasing function of the allocated processors. Also for moldable tasks, Jansen and Porkolab [18] presented, for any > 0, a (1+ )-approximation scheme when the number of processors is a fixed constant.…”

Section: Approximation Algorithmsmentioning

confidence: 99%

“…Figure 1 shows an example of applying both scheduling paradigms to a same set of tasks under a single-resource constraint. Compared to some more sophisticated scheduling algorithms that could guarantee better theoretical bounds (e.g., [22,18]), list-based and pack-based algorithms often produce simple yet efficient schedules that can be easily implemented by practical runtime systems. Furthermore, these algorithms can be adopted to the online and/or heterogeneous scheduling environment with minimal change, thus offering more general applicability to a wide range of scheduling scenarios.…”

Section: Introductionmentioning

confidence: 99%

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Scheduling Parallel Tasks under Multiple Resources: List Scheduling vs. Pack Scheduling

Sun

Elghazi

Gainaru

et al. 2018

2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS)

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Scheduling in High-Performance Computing (HPC) has been traditionally centered around computing resources (e.g., processors/cores). The ever-growing amount of data produced by modern scientific applications start to drive novel architectures and new computing frameworks to support more efficient data processing, transfer and storage for future HPC systems. This trend towards data-driven computing demands the scheduling solutions to also consider other resources (e.g., I/O, memory, cache) that can be shared amongst competing applications. In this paper, we study the problem of scheduling HPC applications while exploring the availability of multiple types of resources that could impact their performance. The goal is to minimize the overall execution time, or makespan, for a set of moldable tasks under multiple-resource constraints. Two scheduling paradigms, namely, list scheduling and pack scheduling, are compared through both theoretical analyses and experimental evaluations. Theoretically, we prove, for several algorithms falling in the two scheduling paradigms, tight approximation ratios that increase linearly with the number of resource types. As the complexity of direct solutions grows exponentially with the number of resource types, we also design a strategy to indirectly solve the problem via a transformation to a single-resource-type problem, which can significantly reduce the algorithms' running times without compromising their approximation ratios. Experiments conducted on Intel Knights Landing with two resource types (processor cores and high-bandwidth memory) and simulations designed on more resource types confirm the benefit of the transformation strategy and show that pack-based scheduling, despite having a worse theoretical bound, offers a practically promising and easy-toimplement solution, especially when more resource types need to be managed.

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Section: Approximation Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Scheduling Parallel Tasks under Multiple Resources: List Scheduling vs. Pack Scheduling

Sun

Elghazi

Gainaru

et al. 2018

2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS)

View full text Add to dashboard Cite

show abstract

“…Malleable Scheduling Quite some research has been dedicated to malleable scheduling for independent tasks,see [2,6,15,16,23,25,26,30]. Notably, Turek et al [30] were the first to describe a general method to employ algorithms for independent parallel tasks to obtain similar results for the malleable case.…”

Section: Related Workmentioning

confidence: 99%

Scheduling and packing malleable and parallel tasks with precedence constraints of bounded width

2012

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We study the problems of non-preemptively scheduling and packing malleable and parallel tasks with precedence constraints to minimize the makespan. In the scheduling variant, we allow the free choice of processors; in packing, each task must be assigned to a contiguous subset. Malleable tasks can be processed on different numbers of processors with varying processing times, while parallel tasks require a fixed number of processors.For precedence constraints of bounded width, we resolve the complexity status of the problem with any number of processors and any width bound. We present an FPTAS based on Dilworth's decomposition theorem for the NP-hard problem variants, and exact efficient algorithms for all remaining special cases. For our positive results, we do not require the otherwise common monotonous penalty assumption on the processing times of malleable tasks, whereas our hardness results hold even when assuming this restriction. We complement our results by showing that these problems are all strongly NP-hard under precedence constraints which form a tree.

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“…One representative example is [48], which reports success in optimal multiprocessor scheduling for periodic systems with deadlines, precedence, and exclusion constraints. A more recent one is [31], which shows how to approximate optimal scheduling using linear programming.…”

Section: Static Schedulingmentioning

confidence: 99%

What to Make of Multicore Processors for Reliable Real-Time Systems?

Baker

2010

Reliable Software Technologiey – Ada-Europe 2010

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Now that multicore microprocessors have become a commodity, it is natural to think about employing them in all kinds of computing, including high-reliability embedded real-time systems. Appealing aspects of this development include the ability to process more instructions per second and more instructions per watt. However, not all problems are amenable to parallel decomposition, and for those that are, designing a correct scalable solution can be difficult. If there are deadlines or other hard timing constraints the difficulty becomes much greater.This paper reviews some of what is known about multiprocessor scheduling of task systems with deadlines, including recent advances in the analysis of arbitrary sporadic task systems under fixed-priority and earliest-deadline first scheduling polices. It also examines critically the foundations of these theoretical results, including assumptions about task independence and worst-case execution time estimates, with a view toward their practical applicability.

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Linear-Time Approximation Schemes for Scheduling Malleable Parallel Tasks

Cited by 69 publications

References 10 publications

Scheduling Parallel Tasks under Multiple Resources: List Scheduling vs. Pack Scheduling

Scheduling Parallel Tasks under Multiple Resources: List Scheduling vs. Pack Scheduling

Scheduling and packing malleable and parallel tasks with precedence constraints of bounded width

What to Make of Multicore Processors for Reliable Real-Time Systems?

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