Online power-performance adaptation of multithreaded programs using hardware event-based prediction

With high-end systems featuring multicore/multithreaded proces-sors and high component density, power-aware high-performance multithreading libraries become a critical element of the system software stack. Online power and performance adaptation of mul-tithreaded code from within user-level runtime libraries is a rela-tively new and unexplored area of research. We present a user-level library framework for nearly optimal online adaptation of multi-threaded codes for low-power, high-performance execution. Our framework operates by regulating concurrency and changing the processors/threads configuration as the program executes. It is in-novative in that it uses fast, runtime performance prediction derived from hardware event-driven profiling, to select thread granularities that achieve nearly optimal energy-efficiency points. The use of predictors substantially reduces the runtime cost of granularity con-trol and program adaptation. Our framework achieves performance and ED2 (energy-delay-squared) levels which are: i) comparable to or better than those of oracle-derived offline predictors; ii) sig-nificantly better than those of online predictors using exhaustive or localized linear search. The complete prediction and adaptation framework is implemented on a real multi-SMT system with In-tel Hyperthreaded processors and embeds adaptation capabilities in OpenMP programs