Execution time prediction for parallel data processing tasks

Abstract

Nowadays a wide range of highly efficient hardware components are available as possible building blocks for parallel distributed systems, however many questions arise on the software side. There is no common solution for optimal distribution of co-operating tasks, and performance prediction is also an open issue. Efforts are focused on creating and making use of mathematical models in a precise domain, namely applications making moderate computation effort on a relatively large amount of data. The possibilities to predict and to minimize execution times are investigated in a cluster of workstations environment, where the data transfer system is expected to become the performance bottleneck. The use of the presented generic model is shown on the example of a parallel integer sorting algorithm: formulas are built up to provide the expected execution times and to approximate the optimal cluster size. Finally, the predicted and the measured execution times of the sorting algorithm are compared for different problem and cluster sizes.