Efficient scheduling of scientific workflows in a high performance computing cluster.

ABSTRACTThe scientific computing community, especially academia is clearly in need of technology to handle and organize the 1-100+ Terabyte datasets coming from computer simulations and scientific instrumentation. In this paper we briefly describe GrayWulf, an exemplar cluster for data intensive applications using SQL Server and HPC Clusters. One of the key software components of GrayWulf is Trident, a scientific workflow workbench that performs automatic scheduling of workflows across the cluster. We examine the challenges of scheduling workflows on GrayWulf, algorithms to improve performance, and present early results from applying Trident to schedule data loading workflows on GrayWulf for an actual e-Science project