Managing Multi - Configurable Hardware via Dynamic Working Set Analysis

Microprocessors are designed to provide good average performance over a variety of workloads. This can lead to inefficiencies both in power and performance for individual programs and during individual phases within the same program. Microarchitectures with multi-configuration units (e.g. caches, predictors, instruction windows) are able to adapt dynamically to program behavior and enable/disable resources as needed. A key element of existing configuration algorithms is adjusting to program phase changes. This is typically done by "tuning" when a phase change is detected -i.e. sequencing through a series of trial configurations and selecting the best. We study algorithms that dynamically collect and analyze program working set information. To make this practical, we propose working set signatures -highly compressed working set representations (e.g. 32-128 bytes total). We describe algorithms that use working set signatures to 1) detect working set changes and trigger re-tuning; 2) identify recurring working sets and re-install saved optimal reconfigurations, thus avoiding the time-consuming tuning process; 3) estimate working set sizes to configure caches directly to the proper size, also avoiding the tuning process. We use reconfigurable instruction caches to demonstrate the performance of the proposed algorithms. When applied to reconfigurable instruction caches, an algorithm that identifies recurring phases achieves power savings and performance similar to the best algorithm reported to date, but with orders-of-magnitude savings in retunings.