An Efficient Parallel Loop Self-scheduling on Grid Environments

Yang, Chao‐Tung; Cheng, Kuan-Wei; Li, Kuan-Ching

doi:10.1007/978-3-540-30141-7_15

Cited by 17 publications

(14 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Currently, there are many researches on predicting the task execution time in the grid environment and two methods are used to acquire it. 1) Static method [11,12]. This method just collects all kinds of computer's hardware configuration, such as CPU, memory, network bandwidth, etc, and then uses an equation to combine them; 2) Dynamic method.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

An Approach of Chunk-based Task Runtime Prediction for Self-Scheduling on Multi-core Desk Grid

Li¹,

Zhu²,

Ji³

et al. 2011

JCP

View full text Add to dashboard Cite

<span style="font-family: "Times New Roman","serif"; font-size: 10.5pt; mso-bidi-font-weight: bold; mso-fareast-language: ZH-CN; mso-fareast-font-family: 宋体; mso-font-kerning: 1.0pt; mso-bidi-font-size: 12.0pt; mso-ansi-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-US">Self-Scheduling is a dynamic and adaptive loop scheduling approach to reduce the total execution time for a task running in the cluster or grid environment. This paper focuses on how to use and optimize Self-scheduling technologies to allocate tasks reasonable and achieve better parallel performance. It introduces the prediction algorithms and proposes a novel Chunk-based Task Runtime Prediction (CTRP) algorithm according to the characters of desk grid and multi-core environment. Our experimental results show that our approach can predict the execution time more accurate and achieve better load balancing than that of others when most slave nodes’ load is changing frequently and rulelessly in the multi-core desk grid.</span>

show abstract

Section: Methodsmentioning

confidence: 99%

“…Self-Scheduling [11]: % partition of workload is dispatched according to their performance weighted by CPU clock in the first phase and the rest (100-%) of workload is dispatched according to known selfscheduling in the second phase.…”

Section: Related Workmentioning

confidence: 99%

An Approach of Chunk-based Task Runtime Prediction for Self-Scheduling on Multi-core Desk Grid

Li¹,

Zhu²,

Ji³

et al. 2011

JCP

View full text Add to dashboard Cite

show abstract

“…An adaptive chunk self-scheduling scheme is proposed (in [13]) to reduce the scheduling overhead. In [14,15], the authors have proposed new improved self-scheduling schemes named NGSS and ANGSS that are well-suited for grids. A two-phase scheme is proposed to solve parallel regular loop scheduling problem in heterogeneous grid computing environments in [16].…”

Section: Related Workmentioning

confidence: 99%

Scalable Loop Self-Scheduling Schemes for Large-Scale Clusters and Cloud Systems

Han

Chronopoulos

2016

Int J Parallel Prog

View full text Add to dashboard Cite

Cloud systems have demonstrated the powerful computation and storage capability in many scientific applications. In this paper, we propose a class of scalable distributed loop self-scheduling schemes to achieve good load balancing and scalability. We implemented these schemes on a large-scale cluster and on a heterogeneous cloud system. The schemes consider the distribution of the output data, which can help reduce communication overhead and improve scalability. We evaluated the schemes using four scientific computations: Mandelbrot set, adjoint convolution, matrix multiplication and quick sort. The results show that the new schemes achieve better load balancing, better scalability and better overall performance than standard distributed loop self-scheduling schemes.

show abstract

“…In [3,24], the authors enhanced their self-scheduling schemes for clusters and applied this approach to grid platforms. However, the dynamically changing characteristic of computing grids has not been investigated.…”

Section: Introductionmentioning

confidence: 99%

A performance-based parallel loop scheduling on grid environments

2007

Self Cite

View full text Add to dashboard Cite

The effectiveness of loop self-scheduling schemes has been shown on traditional multiprocessors in the past and computing clusters in the recent years. However, parallel loop scheduling has not been widely applied to computing grids, which are characterized by heterogeneous resources and dynamic environments. In this paper, a performance-based approach, taking the two characteristics above into consideration, is proposed to schedule parallel loop iterations on grid environments. Furthermore, we use a parameter, SWR, to estimate the proportion of the workload which can be scheduled statically, thus alleviating the effect of irregular workloads. Experimental results on a grid testbed show that the proposed approach can reduce the completion time for applications with regular or irregular workloads. Consequently, we claim that parallel loop scheduling can benefit applications on grid environments.

show abstract

An Efficient Parallel Loop Self-scheduling on Grid Environments

Cited by 17 publications

References 7 publications

An Approach of Chunk-based Task Runtime Prediction for Self-Scheduling on Multi-core Desk Grid

An Approach of Chunk-based Task Runtime Prediction for Self-Scheduling on Multi-core Desk Grid

Scalable Loop Self-Scheduling Schemes for Large-Scale Clusters and Cloud Systems

A performance-based parallel loop scheduling on grid environments

Contact Info

Product

Resources

About