Coordinated management of multiple interacting resources in chip multiprocessors: A machine learning approach

—Efficient sharing of system resources is critical to obtaining high utilization and enforcing system-level performance objectives on chip multiprocessors (CMPs). Although several proposals that address the management of a single microarchitectural resource have been published in the literature, coordinated management of multiple interacting resources on CMPs remains an open problem. We propose a framework that manages multiple shared CMP resources in a coordinated fashion to enforce higher-level performance objectives. We formulate global resource allocation as a machine learning problem. At runtime, our resource management scheme monitors the execution of each application, and learns a predictive model of system performance as a function of allocation decisions. By learning each application’s performance response to different resource distributions, our approach makes it possible to anticipate the system-level performance impact of allocation decisions at runtime with little run...

• Type: Conference
• Year: 2008
• Where: MICRO
• Authors: Ramazan Bitirgen, Engin Ipek, José F. Martínez
• Keywords: Allocation Decisions, Hardware, MICRO 2008, Performance, System-level Performance