A Load Distribution Algorithm Based on Profiling for Heterogeneous GPU Clusters

Clusters of GPUs are becoming commonly used to execute computationally demanding applications. Due to the frequent changes in GPU architecture, many clusters contain heterogeneous types of GPUs, leading to the problem of load distribution among the machines. In this work, we propose a load distribution algorithm for scientific applications executed in heterogeneous GPU clusters. The algorithm finds a distribution of data that minimizes the execution time of the application, by guaranteeing that all GPUs spend the same amount of time processing its assigned kernels and data. We use the algorithm to execute the simulation of large scale neuronal networks. We show that the algorithm effectively balances the load among the GPUs and reduces the execution time of the application.