Collision Graph based Communication Scheduling for Parallel Systems

Applications such as image processing, uid mechanics, and geophysical data analysis are examples of problems that require the high computing performance provided by multi-processor systems. Such performance depends highly on the inter-processor communication time resulting from the allocating tasks to the individual processors. This research focuses on the development of techniques that reduce the communication overhead by intelligently scheduling message transmissions within a tightly-coupled processor network. A collision graph is used to represent the message traac in the system. Using this model, static scheduling algorithms are developed to reduce the communication overhead. Since a priori knowledge about the network, required in static approaches, may not always be available or accurate, dynamic scheduling is also considered. A novel hybrid static-dynamic scheduling approach is presented which operates in a dynamic environment, yet uses known communication pattern information. Results show signiicant improvement over baseline techniques.