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- W2016224013 abstract "Abstract This paper discusses how to exploit parallelism efficiently by improving the granularity of functional programs on a multiprocessor. The challenge is to partition a functional program (or a process) into appropriately sized subprocesses to make sure that the computation time of the local subprocess is at least greater than the communication overheads involved in sending other subprocesses for remote evaluation. 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 prior information on time complexities (in big- O notation) and relative time complexities of its subexpressions 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 over run-time dynamic load balancing methods as well as better utilization of resources." @default.
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- W2016224013 date "1995-04-01" @default.
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- W2016224013 title "Partitioning and Scheduling of Parallel Functional Programs for Larger Grain Execution" @default.
- W2016224013 doi "https://doi.org/10.1006/jpdc.1995.1056" @default.
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