Improvement of Workload Balancing Using Parallel Loop Self-Scheduling on Xeon Phi

In this paper, we will examine how to improve workload balancing on a computing cluster by a parallel loop self-scheduling scheme. We use hybrid MPI and OpenMP parallel programming in C language. The block partition loop is according to the performance weighting of compute nodes. This study implements parallel loop self-scheduling use Xeon Phi, with its characteristics to improve workload balancing between heterogeneous nodes. The parallel loop self-scheduling is composed of the static and dynamic allocation. A weighting algorithm is adopted in the static part while the well-known loop self-scheduling scheme is adopted in the dynamic part. In recent years, Intel promotes its new product Xeon Phi coprocessor, which is similar to the x86 architecture coprocessor. It has about 60 cores and can be regarded as a single computing node, with the computing power that cannot be ignored. In our experiment, we will use a plurality of computing nodes. We compute four applications, i.e., Matrix multiplication, sparse matrix multiplication, Mandelbrot set computation, and the circuit satisfiability problem. Our results will show how to do the weight allocation and how to choose a scheduling scheme to achieve the best performance in the parallel loop self-scheduling.