Parallel computing on any desktop

Marowka, Ami

doi:10.1145/1284621.1284622

Cited by 43 publications

(23 citation statements)

References 6 publications

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“…As the sub-task execution is less than the intermediate deadline, the stealing operation is successful. Similarly, the intermediate deadline for the sub-tasks in ω 2 is computed and the result is d (2) s . For the same reasons, the stealing operation is also successful in ω 2 .…”

Section: Schedulability Analysismentioning

confidence: 99%

“…2 A deque is a special type of queue which also works as a stack. 3 Two or more cores executing a migrating task share a copy of this task.…”

Section: Endnotesmentioning

confidence: 99%

“…At compile time, intra-task parallelism can be extracted from application loops by using programming frameworks such as OpenMP [2]. These frameworks resort to dynamic scheduling strategies *Correspondence: crrm@isep.ipp.pt CISTER/INESC-TEC, ISEP, Polytechnic Institute of Porto, Porto, Portugal in order to schedule application tasks.…”

Section: Introductionmentioning

confidence: 99%

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Real-time semi-partitioned scheduling of fork-join tasks using work-stealing

et al. 2017

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This paper extends the work presented in Maia et al. (Semi-partitioned scheduling of fork-join tasks using work-stealing, 2015) where we address the semi-partitioned scheduling of real-time fork-join tasks on multicore platforms. The proposed approach consists of two phases: an offline phase where we adopt a multi-frame task model to perform the task-to-core mapping so as to improve the schedulability and the performance of the system and an online phase where we use the work-stealing algorithm to exploit tasks' parallelism among cores with the aim of improving the system responsiveness. The objective of this work is twofold: (1) to provide an alternative scheduling technique that takes advantage of the semi-partitioned properties to accommodate fork-join tasks that cannot be scheduled in any pure partitioned environment and (2) to reduce the migration overheads which has been shown to be a traditional major source of non-determinism for global scheduling approaches. In this paper, we consider different allocation heuristics and we evaluate the behavior of two of them when they are integrated within our approach. The simulation results show an improvement up to 15% of the proposed heuristic over the state-of-the-art in terms of the average response time per task set.

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Section: Schedulability Analysismentioning

confidence: 99%

“…2 A deque is a special type of queue which also works as a stack. 3 Two or more cores executing a migrating task share a copy of this task.…”

Section: Endnotesmentioning

confidence: 99%

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Real-time semi-partitioned scheduling of fork-join tasks using work-stealing

et al. 2017

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“…However, for some of these languages, the addition of concurrent features has only been optimized for special purposes hardware [5]. Even Java has a comparable level of feature support for concurrency to that of Ada [6], although it doesn't seem to be widely applied.…”

Section: Concurrent Languagesand Multi-threaded Applicationsmentioning

confidence: 99%

Multi-core + multi-tasking = multi-opportunity?

Nyberg¹

2007

Proceedings of the 2007 ACM International Conference on SIGAda Annual International Conference

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“…Frameworks such as Cilk [3], Intel's Threading Building Blocks [4], Java Fork/Join [5], OpenMP [6], Microsoft's Task Parallel Library [7], StackThreads/MP [8] or Paraffin [9] provide a model where the programmer divides the application in numerous potentially parallel computing units 1 , which are then dynamically assigned to worker threads in the cores by the frameworks' runtime, considering the actual load in the system. To deal with the load balancing of these parallel units in the worker threads (and thus in the cores), the work-stealing algorithm [10] is currently one of the most widely-used, although it may not perform better in all cases [11].…”

Section: Introductionmentioning

confidence: 99%

A parallel programming model for ada

Ali¹,

Pinho²

2011

Proceedings of the 2011 ACM Annual International Conference on Special Interest Group on the Ada Programming Language

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Over the last three decades, computer architects have been able to achieve an increase in performance for single processors by, e.g., increasing clock speed, introducing cache memories and using instruction level parallelism. However, because of power consumption and heat dissipation constraints, this trend is going to cease. In recent times, hardware engineers have instead moved to new chip architectures with multiple processor cores on a single chip. With multi-core processors, applications can complete more total work than with one core alone.To take advantage of multi-core processors, parallel programming models are proposed as promising solutions for more effectively using multi-core processors. This paper discusses some of the existent models and frameworks for parallel programming, leading to outline a draft parallel programming model for Ada.

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Parallel computing on any desktop

Abstract: Parallelization lets applications exploit the high throughput of new multicore processors, and the OpenMP parallel programming model helps developers create multithreaded applications.

Cited by 43 publications

References 6 publications

Real-time semi-partitioned scheduling of fork-join tasks using work-stealing

Real-time semi-partitioned scheduling of fork-join tasks using work-stealing

Multi-core + multi-tasking = multi-opportunity?

A parallel programming model for ada

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