Triplet: A clustering scheduling algorithm for heterogeneous systems

Cirou, B.; Jeannot, Emmanuel

doi:10.1109/icppw.2001.951956

Cited by 57 publications

(44 citation statements)

References 13 publications

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“…Triplet [31] is a clustering algorithm for heterogeneous architectures. It proceeds, first, by clustering tasks while assuming an unbounded number of clusters and, then, a second clustering of these first clusters is performed to merge them on actual processors.…”

Section: Cluster Regrouping On Physical Processorsmentioning

confidence: 99%

Parallelizing with BDSC, a resource-constrained scheduling algorithm for shared and distributed memory systems

Khaldi¹,

Jouvelot²,

Ancourt³

2015

Parallel Computing

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We introduce a new parallelization framework for scientific computing based on BDSC, an efficient automatic scheduling algorithm for parallel programs in the presence of resource constraints on the number of processors and their local memory size. BDSC extends Yang and Gerasoulis's Dominant Sequence Clustering (DSC) algorithm; it uses sophisticated cost models and addresses both shared and distributed parallel memory architectures. We describe BDSC, its integration within the PIPS compiler infrastructure and its application to the parallelization of four well-known scientific applications: Harris, ABF, equake and IS. Our experiments suggest that BDSC's focus on efficient resource management leads to significant parallelization speedups on both shared and distributed memory systems, improving upon DSC results, as shown by the comparison of the sequential and parallelized versions of these four applications running on both OpenMP and MPI frameworks.

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Section: Cluster Regrouping On Physical Processorsmentioning

confidence: 99%

Parallelizing with BDSC, a resource-constrained scheduling algorithm for shared and distributed memory systems

Khaldi¹,

Jouvelot²,

Ancourt³

2015

Parallel Computing

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“…In Triplet algorithm (B. Cirou, 2001), task groups, each of which consists of three tasks, named as "triplet" according to data size to be transferred among tasks and out degree of each task. Then a cluster is generated by merging two triplets according to its execution time and data transfer time on the fastest processor and the slowest processor.…”

Section: Related Workmentioning

confidence: 99%

“…If we use one of conventional cluster assignment methods such as CHP(C. Boeres, 2004), triplet (B. Cirou, 2001), and FCS(S. Chingchit, 1999), almost all processors may be assigned to clusters because they try to achieve the maximum task parallelism to obtain the minimized schedule length. Thus, the third objective function may not be achieved by those cluster assignment strategies.…”

Section: Introductionmentioning

confidence: 99%

On the Effect of Applying the Task Clustering for Identical Processor Utilization to Heterogeneous Systems

Kanemitsu¹,

Lee²,

Nakazato³

et al. 2012

Grid Computing - Technology and Applications, Widespread Coverage and New Horizons

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“…These heuristics are designed using different design principles and with different motivation. On this basis we can roughly classify these heuristics into: list scheduling (e.g., HEFT [3] including its variations [19], HBMCT [4], GDL(DLS) [20], PCT [21], CPOP [3], ETF [22], BIL [23]), workflow-based scheduling (e.g., WBA [24], ILS [25]), guided search scheduling (e.g., GA [26], SA [27]), clustering based scheduling(e.g., Triplet [28] Figure 1: The HEFT Heuristic task duplication based scheduling (e.g., TDS [29], STDS [30], LDBS [31]). An extensive list of references of DAG scheduling heuristics can also be found in [12].…”

Section: Background and Related Workmentioning

confidence: 99%

A Monte-Carlo Approach for Full-Ahead Stochastic DAG Scheduling

Zheng

Sakellariou

2012

2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops &Amp; PhD Forum

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Abstract-In most heterogeneous computing systems, there is a need for solutions that can cope with the unavoidable uncertainty in individual task execution times, when scheduling DAGs. When such uncertainties occur, static DAG scheduling approaches may suffer, and some rescheduling may be necessary. Assuming that the uncertainty in task execution times is modelled in a stochastic manner, then we may be able to use this information to improve static DAG scheduling considerably. In this paper, a novel DAG scheduling approach is proposed to solve this stochastic scheduling problem, based on a Monte-Carlo method. The approach is built on the top of a classic static scheduling heuristic and evaluated through extensive simulation. Empirical results show that a significant improvement on average application performance can be achieved by the proposed approach at a reasonable execution time cost.

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Triplet: A clustering scheduling algorithm for heterogeneous systems

Cited by 57 publications

References 13 publications

Parallelizing with BDSC, a resource-constrained scheduling algorithm for shared and distributed memory systems

Parallelizing with BDSC, a resource-constrained scheduling algorithm for shared and distributed memory systems

On the Effect of Applying the Task Clustering for Identical Processor Utilization to Heterogeneous Systems

A Monte-Carlo Approach for Full-Ahead Stochastic DAG Scheduling

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