Emergent heterogeneous systems must be optimized for both power and performance at exascale. Massive parallelism combined with complex memory hierarchies form a barrier to efficient application and architecture design. These challenges are exacerbated with GPUs as parallelism increases orders of magnitude and power consumption can easily double. We combine hardware performance counter data with machine learning and advanced analytics to model power-performance efficiency for modern GPU-based systems.
Paper available at IEEE.