Computer systems are becoming increasingly complex both from hardware and software perspectives. Designing these systems is becoming increasingly challenging due to the difficulty in evaluating performance and power of the systems before the system is built. But unless performance and power can be quickly estimated during early design space analysis, it is impossible to identify good design points and design the right systems. Simulation is the de facto method of presilicon performance analysis in the microprocessor and computer system design community, however, often it takes days, weeks and sometimes months to simulate a few design choices using common simulators. The proposed research involves investigating performance evaluation methodologies for effective design of next generation computing systems.
Through this project, a workload distiller will be developed to capture essential properties of workloads and create miniature program sequences to help evaluate performance and power during presilicon design exploration. Specifically, the following objectives will be pursued: (i) creation of efficient and manageable benchmarks, (ii) capture of the essence of emerging workloads for power and performance modeling, (iii) development of a methodology to create scalable benchmarks for performance estimation of futuristic systems and workloads, (iv) development of a benchmarking methodology for multicore systems, (v) development of benchmark similarity metrics and clustering techniques for understanding workloads, and (vi) development of a methodology for predicting performance of applications using benchmark similarity metrics. The proposed scalable benchmarks and the evaluation methodologies for multicore systems will help designers during the design of next generation computer systems. In addition, this research will result in training several graduate students in an area that is critical to maintaining our nation's edge in the design of computer systems.
