Program optimizations: The interplay between power, performance, and energy

We provide an analysis of the power and energy effects of program optimizations.The analysis relies on per application phase and per system component studies.We provide guidance on tradeoffs when tuning for performance, power, and energy.We identify energy and runtime correlations for optimizations on three architectures.Multi-objective optimizations require per component and application phase analysis. Practical considerations for future supercomputer designs will impose limits on both instantaneous power consumption and total energy consumption. Working within these constraints while providing the maximum possible performance, application developers will need to optimize their code for speed alongside power and energy concerns. This paper analyzes the effectiveness of several code optimizations including loop fusion, data structure transformations, and global allocations. A per component measurement and analysis of different architectures is performed, enabling the examination of code optimizations on different compute subsystems. Using an explicit hydrodynamics proxy application from the U.S. Department of Energy, LULESH, we show how code optimizations impact different computational phases of the simulation. This provides insight for simulation developers into the best optimizations to use during particular simulation compute phases when optimizing code for future supercomputing platforms. We examine and contrast both x86 and Blue Gene architectures with respect to these optimizations.