A spatially triggered dissipative resource distribution policy for SMT processors

Programs take on changing behavior at nmtime in a simultaneous multithreading （SMT） environment. How reasonably common resources are distributed among the threads significantly determines the throughput and fairness performance in SMT processors. Existing resource distribution methods either mainly rely on the front-end fetch policy, or make distribution decisions according to the limited information from the pipeline. It is difficult for them to efficiently catch the various resource requirements of the threads. This work presents a spatially triggered dissipative resource distribution （SDRD） policy for SMT processors, its two parts, the self-organization mechanism that is driven by the real-time instructions per cycle （IPC） performance and the introduction of chaos that tries to control the diversity Of trial resource distributions, work together to supply sustaining resource distribution optimization for changing program behavior. Simulation results show that SDRD with fine-grained diversity controlling is more effective than that with a coarse-grained one. And SDRD benefits much from its two well-coordinated parts, providing potential fairness gains as well as good throughput gains. Meanings and settings of important SDRD parameters are also discussed.

Programs take on changing behavior at runtime in a simultaneous multithreading (SMT) environment. How rea-sonably common resources are distributed among the threads significantly determines the throughput and fairness performance in SMT processors. Existing resource distribution methods either mainly rely on the front-end fetch policy, or make distribution decisions according to the limited information from the pipeline. It is difficult for them to efficiently catch the various resource requirements of the threads. This work presents a spatially triggered dissipative resource distribution (SDRD) policy for SMT processors. Its two parts, the self-organization mechanism that is driven by the real-time instructions per cycle (IPC) performance and the introduction of chaos that tries to control the diversity of trial resource distributions, work together to supply sustaining resource distribution optimization for changing program behavior. Simulation results show that SDRD with fine-grained diversity controlling is more effective than that with a coarse-grained one. And SDRD benefits much from its two well-coordinated parts, providing potential fairness gains as well as good throughput gains. Meanings and settings of important SDRD parameters are also discussed.