This project investigates innovations in the organization and use of computation and storage resources for future multicore processors. Such innovations are critical to maintaining growth in microprocessor performance, which is the driver of the computer industry.
Two specific directions for innovation will be investigated: (i) novel and efficient organization and use of computation resources within a multicore, and (ii) novel and efficient organization and use of cache resources within a multicore. The first research direction uses a newly proposed framework, Computation Reassignment, which transfers computation fragments to the most appropriate resources for execution, transparent to the higher level software. The proposed technique has the potential to greatly increase the efficiency with which the computation resources of a multicore processor execute a program, which can lead to better performance, better power efficiency, better reliability, and better thermal management. The second research direction investigates Cooperative Caching, a recent proposal for constructing an aggregate, globally managed cache via cooperation between the individual caches. The investigation of cooperative caching will include innovative cooperation policies to coordinate the combined cache resources of a multicore processor to achieve three simultaneous goals: (1) memory access latency reduction, (2) throughput, fairness and quality of service improvement and (3) performance and power management of multicore caches.
The research methodology makes extensive use of simulations using full-system, cycle-level simulators. The project uses a wide variety of benchmark programs including standard benchmark suites as well as other programs, including commercial workloads, written in a number of different programming languages and run on multiple, commercial operating systems. The simulation capabilities include models that allow a quantification of various forms of efficiency, including power and area models.
The research is expected to have a broader impact in several ways, including the training of graduate students, impact on commercial computer designs, enhancing the body of knowledge used to design powerful computers, as well as the development of pedagogical tools that will be useful in educating future computer scientists and engineers.
