Computer Sciences Dept.

A Case for an Over-provisioned Multicore System: Energy Efficient Processing of Multithreaded Programs

Koushik Chakraborty, Philip Wells and Gurindar Sohi

Technology scaling has provided system designers with an exploding transistor budget, far more than what was available when the core principles behind many existing commodity microprocessors were envisioned. With this tremendous growth, however, comes a whole new set of engineering challenges involving power density, thermal efficiency, programmability and so on. In this paper, we study another important trend in high performance microprocessors: the reduction in the Simultaneously Active Fraction (SAF) --- the fraction of the entire chip resources that can be active simultaneously, given a target power envelope. As the improvement in the energy efficiency of individual transistor devices is lagging behind the growth in their integration capacity, we find that the SAF is monotonically decreasing for each successive technology generation.

Given this increasing constraint on the SAF, we examine the utility of temporarily suspending computation on a core as a means for reducing the SAF, and hence, remain within the confines of cost-effective cooling and power delivery. We investigate a SAF aware over-provisioned multicore system (OPMS), where only a subset of the available cores are employed to perform active computation at any given time, by allowing the individual cores to transition between active and inactive state. Though several possible directions for utilizing such an over-provisioned system are possible, this paper focuses on energy efficient dynamic task redistribution. In particular, this paper examines the use of Computation Spreading---a recently proposed technique for runtime specialization of homogeneous multicores---in an OPMS. We show several benefits for such an OPMS design, including reductions in energy, runtime, and superior thermal characteristics. Overall, our technique improves the energy-delay product of the commercial workloads we examine by 5--20%.

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