The concurrent graph: basic technology for irregular problems

Taylor, Stephen; Watts, Jerrell; Rieffel, Marc A.; Palmer, Michael E.

doi:10.1109/88.494601

Cited by 20 publications

(13 citation statements)

References 7 publications

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“…The techniques were implemented in the context of the Concurrent Graph Library, an applications framework which has been successfully applied to a number of large-scale irregular problems [29]. The chapter first presents an overview of the functionality of the Concurrent Graph Library, then gives details on the specific instantiations of the load balancing phases outlined in the previous chapter.…”

Section: Chapter 3 Implementationmentioning

confidence: 99%

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A practical approach to dynamic load balancing

Watts

Taylor

1998

IEEE Trans. Parallel Distrib. Syst.

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150

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Section: Chapter 3 Implementationmentioning

confidence: 99%

“…Hawk is a three-dimensional concurrent DSMC application based on techniques developed by Bird [24,29]. It was written by Marc Rieffel of the Scalable Concurrent Programming Laboratory in conjunction with researchers from the Intel Corporation and the Philips Laboratory at Edwards Air Force Base.…”

Section: Descriptionmentioning

confidence: 99%

A practical approach to dynamic load balancing

Watts

Taylor

1998

IEEE Trans. Parallel Distrib. Syst.

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150

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“…Other authors consider that the irregularity factor is due to the dynamism of pointer-based data-structures [20][21][22]. There are others even that consider algorithms to be irregular due to input dependent communication patterns [23] or irregular data distribution among the processors [24].…”

Section: Irregular Algorithmsmentioning

confidence: 99%

A pattern language for parallelizing irregular algorithms

Monteiro

2010

Proceedings of the 2010 Workshop on Parallel Programming Patterns

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“…The algorithm is scalable and requires only local knowledge of the load distribution in a system. In addition, the direction and the amount of work are determined by the concept of heat diffusion process [6].…”

Section: Introductionmentioning

confidence: 99%

Concurrent DSMC Method Using Dynamic Domain Decomposition

Wu¹,

Tseng²

2003

AIP Conference Proceedings

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Abstract. In the current study, a parallel two-dimensional direct simulation Monte Carlo method is reported, which incorporates a multi-level graph-partitioning technique to dynamically decompose the computational domain. The current DSMC method is implemented on an unstructured mesh using particle ray-tracing technique, which takes the advantages of the cell connectivity information. Standard Message Passage Interface (MPI) is used to communicate data between processors. In addition, different strategies applying the Stop at Rise (SAR) [7] scheme is utilized to determine when to adapt the workload distribution among processors. Corresponding analysis of parallel performance is reported using the results of a high-speed driven cavity flow on IBM-SP2 parallel machines (memory-distributed, CPU 160 MHz, RAM 256 MB each) up to 64 processors. Small, medium and large problems, based on the number of particles and cells, are simulated. Results, applying SAR scheme every two time steps, show that parallel efficiency is 57%, 90% and 107% for small, medium and large problems, respectively, at 64 processors. In general, benefits of applying SAR scheme at larger periods decrease gradually with increasing problem size. Detailed time analysis shows that degree of imbalance levels off very rapidly at a relatively low value (30%~40%) with increasing number of processors applying dynamic load balancing, while it, at a value of 5~6 times larger, increases with increasing number of processors without dynamic load balancing. At the end, the completed code is applied to compute a near-continuum gas flow to demonstrate its superior computational capability.

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The concurrent graph: basic technology for irregular problems

Cited by 20 publications

References 7 publications

A practical approach to dynamic load balancing

A practical approach to dynamic load balancing

A pattern language for parallelizing irregular algorithms

Concurrent DSMC Method Using Dynamic Domain Decomposition

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