Check-load interval analysis for balancing distributed SPMD applications

This paper aims at improving the performance of parallel applications running on nondedicated distributed platforms through a dynamic toad balancer which is kept hidden to the programmer. The support periodically checks the status of the platform and, if necessary, redistributes portions of the data domain from overloaded to underloaded nodes. Various experimental results pointed out the dependence of the performance on the frequency of checking the load status of the platform. Since at implementation time the user has not enough information to choose the best frequency of activation, we propose a performance model that is able to autonomously select at runtime a checkload interval close to the optimum. This model works for Single Program Multiple Data (SPMD) regular computations. The analytical values have been validated through a comparison with experimental results obtained on a cluster of nondedicated workstations. The experiments in various scenarios demonstrate that the model is very accurate, and is able to single out the range of check-load frequency that diminish the execution time of SPMD distributed applications.