EAGER: Foundations for Predictive Resource Management in Next-Generation Multicore Processor Systems

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Future multicore processor systems will have a growing amount of system-wide shared resources. However, shared resources will present significant undesirable asymmetry. For example, the capabilities of processor cores, cache access latency, and memory access cost will differ depending on the time and the location of their usage. If such asymmetry is not properly managed, the full potential of the multicore computing paradigm will not be achieved. This exploratory research will investigate a novel predictive resource management framework called MAESTRO. The proposed framework automatically learns asymmetry in the system and useful application behavior; the learned knowledge is accumulated and refined; and resource management decisions, such as cache capacity allocation, are made in a predictive manner by exploiting the accumulated knowledge. It is expected that MAESTRO's predictive strategies with detailed system and application knowledge will be a more effective solution to new multicore resource management problems than conventional reactive strategies with limited knowledge. The PI will validate this expectation with solid system prototyping and by studying two target resource management problems. The project has the potential to impact the way future computer systems are designed and managed. It is inter-disciplinary by nature and requires understating of applications, computer architecture, OS and machine learning. Students working on this project will receive rigorous inter-disciplinary training.