Work-stealing with configurable scheduling strategies

Wimmer, Martin; Cederman, Daniel; Träff, Jesper Larsson; Tsigas, Philippas

doi:10.1145/2517327.2442562

Cited by 12 publications

(13 citation statements)

References 21 publications

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“…All these schedulers can be viewed as cooperative schedulers because they don't treat threads as competing for the same resources but schedule them cooperatively with the goal of maximizing throughput. Other work has shown that scheduling based on priorities [Imam and Sarkar 2015;Wimmer et al 2013Wimmer et al , 2014 can improve throughput in some applications.…”

Section: Related Workmentioning

confidence: 99%

Fairness in responsive parallelism

Muller

Westrick

Acar

2019

Proc. ACM Program. Lang.

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Research on parallel computing has historically revolved around compute-intensive applications drawn from traditional areas such as high-performance computing. With the growing availability and usage of multicore chips, applications of parallel computing now include interactive parallel applications that mix compute-intensive tasks with interaction, e.g., with the user or more generally with the external world. Recent theoretical work on responsive parallelism presents abstract cost models and type systems for ensuring and reasoning about responsiveness and throughput of such interactive parallel programs.In this paper, we extend prior work by considering a crucial metric: fairness. To express rich interactive parallel programs, we allow programmers to assign priorities to threads and instruct the scheduler to obey a notion of fairness. We then propose the fairly prompt scheduling principle for executing such programs; the principle specifies the schedule for multithreaded programs on multiple processors. For such schedules, we prove theoretical bounds on the execution and response times of jobs of various priorities. In particular, we bound the amount, i.e., stretch, by which a low-priority job can be delayed by higher-priority work. We also present an algorithm designed to approximate the fairly prompt scheduling principle on multicore computers, implement the algorithm by extending the Standard ML language, and present an empirical evaluation.

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Section: Related Workmentioning

confidence: 99%

Fairness in responsive parallelism

Muller

Westrick

Acar

2019

Proc. ACM Program. Lang.

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“…Some programming models expose an API that allows programmers to specify on which NUMA node/socket a collection of tasks should be executed (e.g., OmpSs [4]). Configurable work stealers that can be customized with scheduling hints have also been developed [71]. A more extreme option is to allow the application programmer to attach a custom work-stealing function to the application [48].…”

Section: State Of the Artmentioning

confidence: 99%

Trends in Data Locality Abstractions for HPC Systems

Unat

Dubey

Hoefler

et al. 2017

IEEE Trans. Parallel Distrib. Syst.

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Abstract-The cost of data movement has always been an important concern in high performance computing (HPC) systems. It has now become the dominant factor in terms of both energy consumption and performance. Support for expression of data locality has been explored in the past, but those efforts have had only modest success in being adopted in HPC applications for various reasons. However, with the increasing complexity of the memory hierarchy and higher parallelism in emerging HPC systems, locality management has acquired a new urgency. Developers can no longer limit themselves to low-level solutions and ignore the potential for productivity and performance portability obtained by using locality abstractions. Fortunately, the trend emerging in recent literature on the topic alleviates many of the concerns that got in the way of their adoption by application developers. Data locality abstractions are available in the forms of libraries, data structures, languages and runtime systems; a common theme is increasing productivity without sacrificing performance. This paper examines these trends and identifies commonalities that can combine various locality concepts to develop a comprehensive approach to expressing and managing data locality on future large-scale high-performance computing systems.

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“…These early results have led to many more algorithms [1-3, 13, 16, 22, 26, 30, 49]. More recent papers showed that priority-based schedulers can improve performance in practice [34,63,64]. Our weighted-dag model builds on the model of Muller and Acar [45], who developed an algorithm for scheduling blocking parallel programs to hide latency, but did not consider responsiveness.…”

Section: Related Workmentioning

confidence: 99%

Responsive parallel computation: bridging competitive and cooperative threading

2017

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Competitive and cooperative threading are widely used abstractions in computing. In competitive threading, threads are scheduled preemptively with the goal of minimizing response time, usually of interactive applications. In cooperative threading, threads are scheduled non-preemptively with the goal of maximizing throughput or minimizing the completion time, usually in compute-intensive applications, e.g. scientific computing, machine learning and AI. Although both of these forms of threading rely on the same abstraction of a thread, they have, to date, remained largely separate forms of computing. Motivated by the recent increase in the mainstream use of multicore computers, we propose a threading model that aims to unify competitive and cooperative threading. To this end, we extend the classic graph-based cost model for cooperative threading to allow for competitive threading, and describe how such a cost model may be used in a programming language by presenting a language and a corresponding cost semantics. Finally, we show that the cost model and the semantics are realizable by presenting an operational semantics for the language that specifies the behavior of an implementation, as well as an implementation and a small empirical evaluation. CCS Concepts • Software and its engineering → Parallel programming languages; Concurrent programming languages; Functional languages

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Work-stealing with configurable scheduling strategies

Cited by 12 publications

References 21 publications

Fairness in responsive parallelism

Fairness in responsive parallelism

Trends in Data Locality Abstractions for HPC Systems

Responsive parallel computation: bridging competitive and cooperative threading

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