Semi-Dynamic Multiprocessor Scheduling with an Asymptotically Optimal Performance Ratio

Fujita, Satoshi

doi:10.1587/transfun.e92.a.1764

Cited by 3 publications

(4 citation statements)

References 13 publications

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“…The synchronization granularity, h, for the experiments was in the range of 100, 200, : : : , 3000 iterations. Next, the corresponding theoretical optimal synchronization granularity values were estimated using Equation (11), and these values were denoted by h t opt . To produce more accurate results, the experiments were repeated 10 times for each computational kernel, and the average of the actual parallel execution times is reported.…”

Section: Experimental Results Using the Gigabit Ethernet Interconnectmentioning

confidence: 99%

“…These values are denoted by h p opt . Next, the corresponding theoretical optimal synchronization granularity values were estimated using Equation (11), and these values were denoted by h t opt . The theoretical and practical optimal synchronization granularity values are summarized in Table IV.…”

Section: Experimental Results Using the Gigabit Ethernet Interconnectmentioning

confidence: 99%

“…The self‐scheduling algorithms were initially designed for loops without data dependencies, with a target on single‐core processors and cluster systems . Recent research results have been reported for designing loop self‐scheduling methods for multi‐core, graphics processing unit, grid, and cloud systems . The loop self‐scheduling algorithms have recently been shown to efficiently be applicable for parallelizing loops with data dependencies, by inserting synchronization points at specific intervals throughout the execution of the work assigned to each processor .…”

Section: Introductionmentioning

confidence: 99%

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Towards the optimal synchronization granularity for dynamic scheduling of pipelined computations on heterogeneous computing systems

Riakiotakis

Ciorba

Andronikos

et al. 2012

Concurrency and Computation

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Loops are the richest source of parallelism in scientific applications. A large number of loop scheduling schemes have therefore been devised for loops with and without data dependencies (modeled as dependence distance vectors) on heterogeneous clusters. The loops with data dependencies require synchronization via cross-node communication. Synchronization requires fine-tuning to overcome the communication overhead and to yield the best possible overall performance. In this paper, a theoretical model is presented to determine the granularity of synchronization that minimizes the parallel execution time of loops with data dependencies when these are parallelized on heterogeneous systems using dynamic self-scheduling algorithms. New formulas are proposed for estimating the total number of scheduling steps when a threshold for the minimum work assigned to a processor is assumed. The proposed model uses these formulas to determine the synchronization granularity that minimizes the estimated parallel execution time. The accuracy of the proposed model is verified and validated via extensive experiments on a heterogeneous computing system. The results show that the theoretically optimal synchronization granularity, as determined by the proposed model, is very close to the experimentally observed optimal synchronization granularity, with no deviation in the best case, and within 38.4% in the worst case.

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Section: Experimental Results Using the Gigabit Ethernet Interconnectmentioning

confidence: 99%

Section: Experimental Results Using the Gigabit Ethernet Interconnectmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Towards the optimal synchronization granularity for dynamic scheduling of pipelined computations on heterogeneous computing systems

Riakiotakis

Ciorba

Andronikos

et al. 2012

Concurrency and Computation

View full text Add to dashboard Cite

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“…There are also results focusing on loops with dependencies [11]. Recent research results have been reported on loop self-scheduling methods for multi-core, graphics processing units, grids, and cloud systems [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26] and [27].…”

Section: Introductionmentioning

confidence: 99%

Scalable Loop Self-Scheduling Schemes Implemented on Large-Scale Clusters

Han

Chronopoulos

2013

2013 IEEE International Symposium on Parallel &Amp; Distributed Processing, Workshops and PHD Forum

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Loops are the largest source of parallelism in many scientific applications. Parallelization of irregular loop applications is a challenging problem to achieve scalable performance on large-scale multi-core clusters. Previous research proposed an effective Master-Worker model on clusters for distributed selfscheduling schemes that apply to parallel loops with independent iterations. However, this model has not been applied to large-scale clusters. In this paper, we present an extension of the distributed self-scheduling schemes implemented in a hierarchical Master-Worker model. Our experiments with different self-scheduling schemes demonstrate good scalability when scaling upto 8,192 processors.

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