Using the High-Level Based Program Interface to Facilitate the Large Scale Scientific Computing

Yizi Shang,Yuntao Ye,Dongdong Jia,Chuanchang Gao,Ling Shang,Guiming Lu

doi:10.1155/2014/914514

Abstract

This paper is to make further research on facilitating the large-scale scientific computing on the grid and the desktop grid platform. The related issues include the programming method, the overhead of the high-level program interface based middleware, and the data anticipate migration. The block based Gauss Jordan algorithm as a real example of large-scale scientific computing is used to evaluate those issues presented above. The results show that the high-level based program interface makes the complex scientific applications on large-scale scientific platform easier, though a little overhead is unavoidable. Also, the data anticipation migration mechanism can improve the efficiency of the platform which needs to process big data based scientific applications.

Highlights

The Grid is a generalized network computing system that is supposed to scale to Internet levels and handle data and computation seamlessly
The Globus [1,2,3] project is a multi-institutional research effort that seeks to enable the construction of computational grids providing pervasive, dependable, and consistent access to high performance computational resources, despite geographical distribution of both resources and users
The AppLeS project [4,5,6] focuses on the design and development of the Grid-enabled high performance schedulers for distributed applications

Summary

Introduction

The Grid is a generalized network computing system that is supposed to scale to Internet levels and handle data and computation seamlessly. The AppLeS project [4,5,6] focuses on the design and development of the Grid-enabled high performance schedulers for distributed applications. DIET [13] project is to develop a set of tools to build, deploy, and execute computational server daemons. It focuses on the development of the scalable middleware with initial efforts concentrated on distributing the scheduling problem across multiple agents. Ninf-G aims to support development and execution of Grid applications which will run efficiently on a large-scale computational Grid.

Objectives

Results

Conclusion