October 2000
Intermediate to advanced
288 pages
9h 22m
English
book
Solutions to Parallel and Distributed Computing Problems: Lessons from Biological Sciences
by Albert Y. Zomaya, Fikret Ercal, Stephan Olariu
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6.6 STATIC MATCHING AND SCHEDULING FOR META-TASKS
6.6.1 Introduction
This study implemented 11 different static meta-task mapping heuristics, including 2 evolutionary approaches, so a comparison could be made of their performance using a common simulated HC environment [6]. Section 6.6.2 presents information about how the ETC matrices were generated. Descriptions of the 11 heuristics implemented appear in Section 6.6.3. Last, a sampling of results from the experiments are shown in Section 6.6.4.
6.6.2. ETC Matrices
For the simulation studies, characteristics of the ETC matrices were varied in an attempt to represent a range of possible HC environments. The ETC matrices used were generated using the following method. Initially, a |T| × 1 baseline column vector, B, of floating-point values is created. Let 
b be the upper bound of the range of possible values within the baseline vector. The baseline column vector is generated by repeatedly selecting a uniform random number, 
, and 
for 0 ≤ i < |T|. Next, the rows of the ETC matrix are constructed. Each element ETC(i, j) in row i of the ETC matrix is created by taking the baseline value, B(i), and multiplying it by a uniform random number, ...
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