Controlling Parallelism for Larger Grain Execution of Functional Programs Using Complexity Information

This paper will discuss some issues of parallelism in functional programs and how to exploit it efficiently by improving the granularity of such programs on a multiprocessor. The challenge is to partition a functional program (or a process) into appropriately-sized sub-processes to make sure that the computation time of the local sub-process is at least greater than the communication overheads involved in sending other sub-processes for remote evaluation. It is shown how some parallel programs can be run more efficiently with the prior information of time complexities (in big-0 notation) and relative time complexities of its sub-expressions with the help of some practical examples on the larger-grain distributed multiprocessor machine LAGER.