Chrysso: an integrated power manager for constrained many-core processors
Document typeConference report
PublisherAssociation for Computing Machinery (ACM)
Rights accessOpen Access
Modern microprocessors are increasingly power-constrained as a result of slowed supply voltage scaling (end of Dennard scaling) in conjunction with the transistor density scaling (Moore's Law). Existing many-core power management techniques such as chip-wide/per-core DVFS, and core and cache adaptation are quite effective in isolation at moderate to high power budgets. However, for future many-core chip, the existing techniques do not scale well to large core counts, small time slices and stringent power budgets. We need a new solution that combines different adaptation and reconfiguration techniques. In this paper, we present Chrysso, an integrated, scalable and low-overhead power management framework. Chrysso consists of a three-step process: leveraging simple analytical performance and power models, pruning the search space early using local Pareto front generation, followed by global utility-based power allocation. This ensures scalable and effective dynamic adaptation of many-core processors at short time scales along multiple axes, including core, cache and per-core DVFS adaptations. By integrating multiple power management techniques into a common methodology, Chrysso provides significant performance improvements over isolated mechanisms within a given power budget without power-gating cores. On a 64-core system, Chrysso improves system throughput by 1.6× and 1.9× over core-gating at stringent power envelops for multi-program (SPEC) and multi-threaded (PARSEC) workloads, respectively.
CitationJha, S., Heirman, W., Falcón, A., Carlson, T., Van Craeynest, K., Tubella, J., González, A., Eeckhout, Lieven. Chrysso: an integrated power manager for constrained many-core processors. A: ACM International Conference on Computing Frontiers. "Proceedings of the 12th ACM International Conference on Computing Frontiers, CF 2015". Ischia: Association for Computing Machinery (ACM), 2015.