Abstract
This paper will discuss how to exploit parallelism efficiently by improving the granularity of functional programs on a multiprocessor. Asymptotic complexity analyses of a function, to estimate the computation time and also the communication involved in sending the arguments and receiving the results from the remote processor, are found to be quite useful. It is shown how some parallel programs can be run more efficiently with the prior information of time complexities (in big-O notation) and relative time complexities of its sub-expressions with the help of analytical reasoning and some practical examples on the larger-grain distributed multiprocessor machine LAGER. Ordered scheduling of the processes, determined by the priorities based on the relative time complexities, shows further improvement on the run-time dynamic load balancing methods and better utilisation of the resources.
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Maheshwari, P. (1991). Partitioning and scheduling of parallel functional programs using complexity information. In: Dehne, F., Fiala, F., Koczkodaj, W.W. (eds) Advances in Computing and Information — ICCI '91. ICCI 1991. Lecture Notes in Computer Science, vol 497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-54029-6_178
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DOI: https://doi.org/10.1007/3-540-54029-6_178
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