Large Scale Scientific Computing

The aim of this book is to find a solution to make large scientific computing in an easy way. To do that, research on Gauss Jordan algorithm is made and a new parallel programming paradigm is presented. The new parallel version can achieve maximum degree parallelism between operations. Also the Gauss Jordan algorithm as an excellent example is used to evaluate different experimental environments and tools. Experiments with YML, OmniRPC and XtremWeb on Grid and Desktop Grid environments testify YML, which is a workflow based programming lanaguage, can be a good solution for end users to make large scale scientific computing for its series of good features such as higher level interface, component reuse and acceptable overhead. According to analysis made in this book and the common features of Clouds possessed, YML-PC, a reference architecture based on workflow for building scientific Private Clouds is proposed. YML-PC inherits those good features YML possessed and some other key technologies such as data persistence , available time prediction and evaluation on heterogeneous computing nodes . Some evaluations are made on Grids, Desktop Grids and Private Clouds.

Autorentext

Ling Shang received the PhD degree in computer science from university Lille1,France and the PhD degree in hydro-informatics from Hohai university,China. His interests are in the area of large scale scientific computing on Grid and Cloud platforms. Now he is devoted to apply Cloud technologies to the areas of hydro-informatics and SNS based media.