Power-aware Performance Tuning of GPU Applications Through Microbenchmarking

Tuning GPU applications is a very challenging task as any source-code optimization can sensibly impact performance, power , and energy consumption of the GPU device. Such an impact also depends on t he GPU on which the application is run. This paper presents a suite of microbenchmarks that provides the actual characteristics of specific GPU device components (e.g., arithmetic instruction units, memories, etc.) in t erms of throughput, power , and energy consumption. It shows how t he suite can be combined to standard profiler information to efficiently drive the application tuning by considering the three design constraints (power, performance, energy consumption) and t he charact eristics of the target GPU device.