Collective Value of QoS: A Performance Measure Framework for Distributed Heterogeneous Networks
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
Parallel Processing Approaches to Image Correlation
IEEE Transactions on Computers
On detecting parallelism in software
Journal of Systems and Software
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This paper examines measures for evaluating the performance of algorithms for single instruction stream-multiple data stream (SIMD) machines. The SIMD mode of parallelism involves using a large number of processors synchronized together. All processors execute the same instruction at the same time; however, each processor operates on a different data item. The complexity of parallel algorithms is, in general, a function of the machine size (number of processors), problem size, and type of interconnection network used to provide communications among the processors. Measures which quantify the effect of changing the machine-size/problem-size/network-type relationships are therefore needed. A number of such measures are presented and are applied to an example SIMD algorithm from the image processing problem domain. The measures discussed and compared include execution time, speed, parallel efficiency, overhead ratio, processor utilization, redundancy, cost effectiveness, speed-up of the parallel algorithm over the corresponding serial algorithm, and an additive measure called "sprice" which assigns a weighted value to computations and processors.