Parallel Loop Self-scheduling Research Articles

Improvement of workload balancing using parallel loop self-scheduling on Intel Xeon Phi

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. This work aims to improve the workload balance by parallel loop self-scheduling scheme performed on Xeon Phi-based computer cluster. The proposed concept is implemented by hybrid MPI and OpenMP parallel programming in C language. Since parallel loop self-scheduling composes of static and dynamic allocation, weighting algorithm is adopted in the static part, while the well-known loop self-scheduling is adopted in dynamic part. The loop block is partitioned according to the weighting of MIC and HOST nodes. Accordingly, Xeon Phi with many-core is adopted to implement parallel loop self-scheduling. Finally, we test the performance in the experiments by four applicable problems: matrix multiplication, sparse matrix multiplication, Mandelbrot set and circuit meet. The experimental results indicate how to do the weight allocation and which scheduling method can achieve the best performance.

Designing parallel loop self-scheduling schemes using the hybrid MPI and OpenMP programming model for multi-core grid systems

Loop scheduling on parallel and distributed systems has been thoroughly investigated in the past. However, none of these studies considered the multi-core ar- chitecture feature for emerging grid systems. Although there have been many studies proposed to employ the hybrid MPI and OpenMP programming model to exploit dif- ferent levels of parallelism for a distributed system with multi-core computers, none of them were aimed at parallel loop self-scheduling. Therefore, this paper investigates how to employ the hybrid MPI and OpenMP model to design a parallel loop self- scheduling scheme adapted to the multi-core architecture for emerging grid systems. Three different featured applications are implemented and evaluated to demonstrate the effectiveness of the proposed scheduling approach. The experimental results show that the proposed approach outperforms the previous work for the three applications and the speedups range from 1.13 to 1.75.

On development of an efficient parallel loop self-scheduling for grid computing environments

The approaches to deal with scheduling and load balancing on PC-based cluster systems are famous and well-known. Self-scheduling schemes, which are suitable for parallel loops with independent iterations on cluster computer system, they have been designed in the past. In this paper, we propose a new scheme that can adjust the scheduling parameter dynamically on an extremely heterogeneous PC-based cluster and Grid computing environments in order to improve system performance. A Grid computing environment consists of multiple PC-based clusters is constructed using Globus Toolkit and MPICH-G2 middleware. The experimental results show that our scheduling can result in higher performance than other similar schemes on Grid computing environments.

An Enhanced Parallel Loop Self-Scheduling Scheme for Cluster Environments

Approaches for dealing with scheduling and load-balancing in PC-based cluster systems are famous and well known. In such environments, Self-Scheduling Schemes are suitable for parallel loops with independent iterations. However, while schemes such as FSS, GSS, and TSS fit most computer systems, they cannot provide good load-balancing. Chao-Tung Yang and Shun-Chi Chang proposed a parallel loop scheduling scheme for heterogeneous PC cluster systems in Yang and Chang [13]. Though the proposed scheme allows users to choose parameters before execution initialization, weaknesses in it motivated us to develop further improvements. For instance, using fixed and monotonous parameters can easily lead to invalid scheduling due to use of previously input information. Thus, in this paper we propose a new scheme that fits most widely available computer systems and allows the scheduling parameter to be adjusted dynamically in order to provide higher overall performance.

A Parallel Loop Self-Scheduling on Extremely Heterogeneous PC Clusters

Cluster computers are a viable and less expensive alternative to symmetric multiprocessor systems. However, a serious difficulty in concurrent programming of a cluster computer system is how to deal with scheduling and load balancing of such a system which may consist of heterogeneous computers Self-scheduling schemes suitable for parallel loops with independent iterations on heterogeneous computer clusters have been designed in the past These schemes, such as FSS, GSS and TSS, can achieve load balancing in SMP, even in a moderate heterogeneous environment, but are not suitable in extremely heterogeneous environments. In this paper, we propose a heuristic approach to solve parallel loop scheduling problems on an extremely heterogeneous PC cluster environment.