Benchmarking and Comparison of the Task Graph Scheduling Algorithms

The problem of scheduling a parallel program represented by a weighted directed acyclic graph (DAG) to a set of homogeneous processors for minimizing the completion time of the program has been extensively studied. The NP-completeness of the problem has stimulated researchers to propose a myriad of...

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Vydané v:	Journal of parallel and distributed computing Ročník 59; číslo 3; s. 381 - 422
Hlavní autori:	Kwok, Yu-Kwong, Ahmad, Ishfaq
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	San Diego, CA Elsevier Inc 01.12.1999 Elsevier
Predmet:	Applied sciences benchmarks Computer science; control theory; systems Computer systems and distributed systems. User interface Exact sciences and technology multiprocessors parallel processing performance evaluation scalability scheduling Software task graphs performance evaluation scheduling benchmarks multiprocessors parallel processing scalability task graphs Performance evaluation Multiprocessor Parallel program Parallel processing NP complete problem Bench mark Scheduling Algorithm Acyclic graph Directed graph Completion time
ISSN:	0743-7315, 1096-0848
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Popis
Shrnutí:	The problem of scheduling a parallel program represented by a weighted directed acyclic graph (DAG) to a set of homogeneous processors for minimizing the completion time of the program has been extensively studied. The NP-completeness of the problem has stimulated researchers to propose a myriad of heuristic algorithms. While most of these algorithms are reported to be efficient, it is not clear how they compare against each other. A meaningful performance evaluation and comparison of these algorithms is a complex task and it must take into account a number of issues. First, most scheduling algorithms are based upon diverse assumptions, making the performance comparison rather meaningless. Second, there does not exist a standard set of benchmarks to examine these algorithms. Third, most algorithms are evaluated using small problem sizes, and, therefore, their scalability is unknown. In this paper, we first provide a taxonomy for classifying various algorithms into distinct categories according to their assumptions and functionalities. We then propose a set of benchmarks that are based on diverse structures and are not biased toward a particular scheduling technique. We have implemented 15 scheduling algorithms and compared them on a common platform by using the proposed benchmarks, as well as by varying important problem parameters. We interpret the results based upon the design philosophies and principles behind these algorithms, drawing inferences why some algorithms perform better than others. We also propose a performance measure called scheduling scalability (SS) that captures the collective effectiveness of a scheduling algorithm in terms of its solution quality, the number of processors used, and the running time.
ISSN:	0743-7315 1096-0848
DOI:	10.1006/jpdc.1999.1578