Scheduling Chapel Tasks with Qthreads on Manycore

Evans, Noah; Olivier, Stephen Lecler; Barrett, Richard Frederick; Stelle, George

doi:10.1145/3095770.3095774

Cited by 4 publications

(2 citation statements)

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“…Modern parallel programming frameworks provide support for load-balancing, although often only within a shared memory domain. Language-level support for dynamic scheduling exists within OpenMP and has been studied within Chapel [15] and X10 [9]. Some parallel programming frameworks, such as Legion [3], also support work stealing within a shared memory environment.…”

Section: Related Workmentioning

confidence: 99%

Accelerated Work Stealing

Larkins

Snyder

Dinan

2019

Proceedings of the 48th International Conference on Parallel Processing

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Realizing scalable performance with irregular parallel applications is challenging on large-scale distributed memory clusters. These applications typically require continuous, dynamic load balancing to maintain efficiency. Work stealing is a common approach to dynamic distributed load balancing. However its use in conjunction with advanced network offload capabilities is not well understood. We present a distributed work-stealing system that is amenable to acceleration using the Portals 4 network programming interface. Our work shows that the structures provided by Portals to handle two-sided communication are general-purpose and can accelerate work stealing. We demonstrate the effectiveness of this approach using known benchmarks from computational chemistry and for performing unbalanced tree searches. Results show that Portals accelerated work-stealing can greatly reduce communication overhead, task acquisition time, and termination detection.

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Section: Related Workmentioning

confidence: 99%

Accelerated Work Stealing

Larkins

Snyder

Dinan

2019

Proceedings of the 48th International Conference on Parallel Processing

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“…This technique reduces inter-socket communication. Evans et al discuss scheduling strategies for Chapel tasks on manycore systems (Evans et al 2017). They compare two existing strategies Sherwood and Nemesis and introduce a novel strategy Distrib.…”

Section: Related Workmentioning

confidence: 99%

Blaze-Tasks

Pirkelbauer

Wilson

Peterson

et al. 2018

ACM Trans. Archit. Code Optim.

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Compared to threads, tasks are a more fine-grained alternative. The task parallel programming model offers benefits in terms of better performance portability and better load-balancing for problems that exhibit nonuniform workloads. A common scenario of task parallel programming is that a task is recursively decomposed into smaller sub-tasks. Depending on the problem domain, the number of created sub-tasks may be nonuniform, thereby creating potential for significant load imbalances in the system. Dynamic load-balancing mechanisms will distribute the tasks across available threads. The final result of a computation may be modeled as a reduction over the results of all sub-tasks.This article describes a simple, yet effective prototype framework, Blaze-Tasks, for task scheduling and task reductions on shared memory architectures. The framework has been designed with lock-free techniques and generic programming principles in mind. Blaze-Tasks is implemented entirely in C++17 and is thus portable. To load-balance the computation, Blaze-Tasks uses task stealing. To manage contention on a task pool, the number of lock-free attempts to steal a task depends on the distance between thief and pool owner and the estimated number of tasks in a victim's pool. This article evaluates the Blaze framework on Intel and IBM dual-socket systems using nine benchmarks and compares its performance with other task parallel frameworks. While Cilk outperforms Blaze on Intel on most benchmarks, the evaluation shows that Blaze is competitive with OpenMP and other library-based implementations. On IBM, the experiments show that Blaze outperforms other approaches on most benchmarks.

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