Prior research on multiprocessor interconnection networks has primarily focused on the network topology, switching mechanism, and message routing algorithm to maximize network performance. Very little work has been done to study the impact of the communication properties of parallel applications. Communication pattern, message generation frequency, and message size are the three attributes to quantify any communication. The proposed research is aimed at characterizing these communication workloads and analyzing their impact on multiprocessor performance. The research has two major phases. In the first phase, traffic profiling of a wide range of parallel applications will be conducted by collecting execution traces from parallel machines and also from an execution-driven simulator. The communication traces will be analyzed to determine different types of traffic patterns, rate of communication, and volume of communication. The second phase of the project will use these realistic traffic properties for the performance analysis of interconnection networks via simulation and analytic models. In-depth simulation of known unicast and collective routing algorithms on various topologies will be performed with these workloads. Analytical models capturing wormhole switching, virtual channel flow control, routing mechanism and the realistic workloads will be developed. The main contribution of the project is characterizing application workloads to be used for different architectural and algorithmic research. For example, evaluation with these workloads will quantify the actual performance advantages of adaptive routing algorithms, will identify the potential bottlenecks of existing communication mechanisms, and will provide insight for developing application-specific routing algorithms. Next, the new mathematical tools will be more accurate in predicting the realistic communication traffic. The techniques and tools developed in this research can be used in understandi ng and evaluating the interplay between parallel architectures and algorithms to maximize the performance of existing machines and to design better machines in the future.
