Extraction of thermal workload signatures in multicore processors using least angle regression

Performance counters (PCs) embedded in microprocessor are frequently used to characterize workload and predict thermal behavior for multicore processors. These PCs are required to be highly accurate, very compact, and tunable to workload changes in real time. Traditionally these PCs are selected using correlation map or some sort of statistical trial-error techniques. These techniques have the disadvantage of requiring the large PC set regardless of the workload type which is computationally burden when scaling number of cores in processor. In this paper, we use the more recent algorithm of least-angle regression to choose specific set of PCs for definite workload characteristic and validate its accuracy by thermal modeling. It include only those PCs most correlated with thermal behavior of workload. Such PCs are considered as signatures to predict workload characteristic and to apply specific thermal management action. The PC sets are trained and tested on model using workloads from the PARSEC and SPEC CPU 2006 benchmarks.