Task Scheduling Greedy Heuristics for GPU Heterogeneous Cluster Involving the Weights of the Processor

Modern GPUs are gradually used by more and more cluster computing systems as the high performance computing units due to their outstanding computational power, whereas bringing system-level (among different nodes) architectural heterogeneity to cluster. In this paper, based on MPI and CUDA programming model, we aim to investigate task scheduling for GPU heterogeneous cluster by taking into account the system-level heterogeneous characteristics and also involving the weights of the processor (both CPUs and GPUs). At first, based on our GPU heterogeneous cluster, we classify executing tasks to six major classifications according to their parallelism degrees, input data sizes, and processing workloads. Then, aiming to realize the approximately optimal mapping between tasks and computing resources, a task scheduling strategy is presented. In this paper, we present the WSLSA greedy heuristic which can involve the weights of the processor. Besides, we also define two measurement factors for the task assignments. One is the maximum value of total workloads for all task assignments to consider the maximum workloads for the GPU heterogeneity cluster. The other is the distribution of task assignments which can determine the load balance of the task assignments for the GPU heterogeneity cluster. The other is the distribution of task assignments which can determine the load balance of the task assignments for the GPU heterogeneity cluster.