Improving scheduling of tasks in a heterogeneous environment

Bajaj, Rashmi; Agrawal, Dharma P.

doi:10.1109/tpds.2004.1264795

Cited by 241 publications

(127 citation statements)

References 22 publications

Supporting

Mentioning

117

Contrasting

Order By: Relevance

“…Within the same level, the task with the highest average computation cost has the highest priority. If the number of tasks in a level is greater than the number of available processors, the fine-grain tasks are merged into a coarse-grain task [17] until the number of tasks is equal to the number of processors. Then the tasks are sorted in reverse order (largest task first) based on average computation time.…”

Section: Levelized Min Time (Lmt) Algorithmmentioning

confidence: 99%

“…Because of its key importance on performance, the task scheduling problem in general has been extensively studied and various heuristics have been proposed in the literature [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] . These heuristics are classified into a variety of categories such as list scheduling algorithms, clustering algorithms, guided random search methods and task duplication based algorithms.…”

Section: Introdutionmentioning

confidence: 99%

“…In task duplication based algorithms [15][16][17] , tasks are duplicated on more than one processor to reduce the waiting time of the dependent tasks. It is an interesting approach that has been blended with both list scheduling and clustering-based techniques by various researchers.…”

Section: Introdutionmentioning

confidence: 99%

“…It is an interesting approach that has been blended with both list scheduling and clustering-based techniques by various researchers. Critical Path Fast Duplication (CPFD) [15] , Heterogeneous Critical Node First (HCNF) [16] and Task duplication Algorithm for Network of Heterogeneous system (TANH) [17] are a few algorithms proposed in the literature for heterogeneous system using task duplication.…”

Section: Introdutionmentioning

confidence: 99%

See 3 more Smart Citations

Low Complexity Performance Effective Task Scheduling Algorithm for Heterogeneous Computing Environments

Ilavarasan¹,

Thambidurai²

2007

J. of Computer Science

152

View full text Add to dashboard Cite

Abstract:A heterogeneous computing environment is a suite of heterogeneous processors interconnected by high-speed networks, thereby promising high speed processing of computationally intensive applications with diverse computing needs. Scheduling of an application modeled by Directed Acyclic Graph (DAG) is a key issue when aiming at high performance in this kind of environment. The problem is generally addressed in terms of task scheduling, where tasks are the schedulable units of a program. The task scheduling problems have been shown to be NP-complete in general as well as several restricted cases. In this study we present a simple scheduling algorithm based on list scheduling, namely, low complexity Performance Effective Task Scheduling (PETS) algorithm for heterogeneous computing systems with complexity O (e) (p+ log v), which provides effective results for applications represented by DAGs. The analysis and experiments based on both randomly generated graphs and graphs of some real applications show that the PETS algorithm substantially outperforms the existing scheduling algorithms such as Heterogeneous Earliest Finish Time (HEFT), Critical-Path-On a Processor (CPOP) and Levelized Min Time (LMT), in terms of schedule length ratio, speedup, efficiency, running time and frequency of best results.Key words: DAG, task graph, task scheduling, heterogeneous computing system, schedule length, speedup, efficiency INTRODUTIONA growing emphasis on concurrent processing of jobs has lead to an increased acceptance of heterogeneous computing environments and the availability of a network of processors makes a costeffective utilization of underlying parallelism for applications like weather modeling, image processing, real-time and distributed database systems. A wellknown strategy behind efficient execution of a huge application on a heterogeneous computing environment is to partition it into multiple independent tasks and schedule such tasks over a set of available processors. A task-partitioning algorithm takes care of efficiently dividing an application into tasks of appropriate grain size and an abstract model of such a partitioned application can be represented by a Directed Acyclic Graph (DAG). Each task of a DAG corresponds to a sequence of operations and a directed edge represents the precedence constraints between the tasks. Each task can be executed on a processor and the directed edge shows transfer of relevant data from one processor to another. Task scheduling can be performed at compile-time or at run-time. When the characteristics of an application, which includes execution times of tasks on different processors, the data size of the communication between tasks and the task dependencies, are known a priori, it is represented with a static model. The objective of task scheduling is to map the tasks on the processors and order their execution so that task precedence requirements are satisfied and a minimum overall completion time is obtained. The problem of scheduling of tasks with required precedence rela...

show abstract

Section: Levelized Min Time (Lmt) Algorithmmentioning

confidence: 99%

Section: Introdutionmentioning

confidence: 99%

Section: Introdutionmentioning

confidence: 99%

Section: Introdutionmentioning

confidence: 99%

See 2 more Smart Citations

Low Complexity Performance Effective Task Scheduling Algorithm for Heterogeneous Computing Environments

Ilavarasan¹,

Thambidurai²

2007

J. of Computer Science

152

View full text Add to dashboard Cite

show abstract

“…The goal of scheduling algorithm is to map tasks to processors and order their executions to satisfy task precedence relationship and make minimum schedule length (makespan). Since the general scheduling algorithm is NP-complete [1], many research efforts have been made in this rich field, i.e., list scheduling [2,3], cluster-based algorithms [4], genetic algorithms [5], simulated annealing algorithms [6], duplication-based algorithms [7], fuzzy scheduling [8], branch and bound algorithm [9], etc.. Among these scheduling algorithms, list-scheduling algorithm has been shown to have a good trade-off between performance and cost, and static scheduling…”

Section: Introductionmentioning

confidence: 99%

Edge Scheduling Algorithms in Parallel and Distributed Systems

Han

Wang

2006 International Conference on Parallel Processing (ICPP'06)

View full text Add to dashboard Cite

show abstract

Prion-Protein-Specific Aptamer Reduces PrPSc Formation

Proske¹,

Gilch²,

Wopfner³

et al. 2002

ChemBioChem

141

View full text Add to dashboard Cite

The critical initial event in the pathophysiology of transmissible spongiform encephalopathies (TSEs) appears to be the conversion of the cellular prion protein (PrP C ) into the abnormal isoform PrP Sc . This isoform forms high-molecular-weight protease K (PK) resistant aggregates that accumulate in the central nervous system of affected individuals. We have selected nuclease-resistant 2'-amino-2'-deoxypyrimidine-modified RNA aptamers which recognize a peptide comprising amino acid residues 90 ± 129 of the human prion protein with high specificity. This domain of prion proteins is thought to be functionally important for the conversion of PrP C into its pathogenic isoform PrP Sc and is highly homologous among prion proteins of various species including mouse, hamster, and man. Consequently, aptamer DP7 binds to the full-length human, mouse, and hamster prion protein. At low concentrations in the growth medium of persistently prion-infected neuroblastoma cells, aptamer DP7 significantly reduced the relative proportion of de novo synthesized PK-resistant PrP Sc within only 16 h. These findings may open the door towards a rational development of a new class of drugs for the therapy or prophylaxis of prion diseases.

show abstract

Improving scheduling of tasks in a heterogeneous environment

Cited by 241 publications

References 22 publications

Low Complexity Performance Effective Task Scheduling Algorithm for Heterogeneous Computing Environments

Low Complexity Performance Effective Task Scheduling Algorithm for Heterogeneous Computing Environments

Edge Scheduling Algorithms in Parallel and Distributed Systems

Prion-Protein-Specific Aptamer Reduces PrPSc Formation

Contact Info

Product

Resources

About