Multicore power proxies using least-angle regression

The use of performance counters (PCs) to develop per-core power proxies for multicore processors is now well established. These proxies are typically obtained using traditional linear regression techniques. These techniques have the disadvantage of requiring the full PC set regardless of the workload run by the multicore processor. Typically a computationally expensive principal component analysis is conducted to find the PCs most correlated with each workload. In this paper, we use the more recent algorithm of least-angle regression to efficiently develop power proxies that include only PCs most relevant to the workload. Such PCs can be considered workload signatures and used to categorize the workload and to trigger specific power management action. Our new power proxies are trained and tested on workloads from the PARSEC and SPEC CPU 2006 benchmarks with an average error of less than 3%.