Automatic Energy Efficient Parallelization of Uniform Dependence Computations
Yun Zou, Sanjay V. Rajopadhye · 2015
Energy is now a critical concern in all aspects of computing. We address a class of programs that includes the so-called "stencil computations" that have already been optimized for speed. We target the energy expended in dynamic memory accesses, since most other components of the total energy are usually already reduced when optimizing for speed alone. For a standard shared memory multi-core processor, we seek to minimize the total number of off-chip memory accesses without sacrificing execution time. Our strategy uses two-level tiling with multiple pipelined passes. Because of the sophisticated tiling and parallelization, such codes are difficult to write by hand, especially for parametric tile sizes. They are also beyond the capability of current code generators because the schedules used are polynomial functions, more general than multidimensional schedules. We implement a parametric tiled code generator to support this strategy, and also develop a simple quantitative linear regression model for the energy consumed by a program. We experimentally validate our techniques on a set of benchmarks including those from the Polybench suite on two platforms. Our experiments show that about 78% (resp. 80%) of the dynamic memory energy consumption on an 8-core Xeon E5-2650 v2 (resp. 6-core Xeon E5-2620 v2) based machine can be avoided. This leads to a reduction in the total energy of the program by 2% to 14%.