Scheduling schemes for data farming

The use of order statistics to arrive at a scheduling regime is shown to be applicable to data farms running on second-generation parallel processors. Uniform and decreasing task-size scheduling regimes are examined. Experimental timings and a further simulation for large-scale effects were used to exercise the scheduling regimes. The paper also considers a number of other scheduling schemes for data farms. It is shown that a method previously used for loop scheduling is preferable, particularly as a form of automatic and generalised scheduling for data farming where there is a data-dependent workload