Abstract

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. 1 Introduction A processor or data farm [ 1 ] is a programming paradigm involving message-passing in which a single task is repeatedly executed in parallel on a collection of initial data. Data-farming is a commonly-used paradigm in parallel processing [ 2 ] and appears in numerous guises: some (network-of-workstations) NOW-based [ 3 ] ; some based on dedicated multicomputers [ 4...

Citations