2015 IEEE 12th International Conferen on Embedded Software and Systems (ICESS)

Asymmetric chip multiprocessors (ACMPs) have multiple core types that are instruction-set compatible but optimized differently to trade performance and power in mobile devices. The challenge for ACMPs is to map the program to the best core type and thread count to achieve performance requirements under power constraints. This paper describes an empirical strategy, MONARCH, to automatically build estimation models that capture how a multithreaded program's performance scales with thread count and core type. We show that MONARCH's models are accurate and useful to find mappings that achieve performance goals while minimizing power