Bounds on the Energy Consumption of Computational Kernels

Andrew Gearhart
EECS Department, University of California, Berkeley
University of California, Berkeley, Technical Report No. UCB/EECS-2014-175, 2014


   Author={Gearhart, Andrew},

   Title={Bounds on the Energy Consumption of Computational Kernels},

   School={EECS Department, University of California, Berkeley},






As computing devices evolve with successive technology generations, many machines target either the mobile or high-performance computing/datacenter environments. In both of these form factors, energy consumption often represents the limiting factor on hardware and software efficiency. On mobile devices, limitations in battery technology may reduce possible hardware capability due to a tight energy budget. On the other hand, large machines such as datacenters and supercomputers have budgets directly related to energy consumption and small improvements in energy efficiency can significantly reduce operating costs. Such challenges have influenced research upon the impact of applications, operating and runtime systems upon energy consumption. Until recently, little consideration was given to the potential energy efficiency of algorithms themselves. A dominant idea within the high-performance computing (HPC) community is that applications can be decomposed into a set of key computational problems, called kernels. Via automatic performance tuning and new algorithms for many kernels, researchers have successfully demonstrated performance improvements on a wide variety of machines. Motivated by the large and increasingly growing dominant cost (in time and energy) of moving data, algorithmic improvements have been attained by proving lower bounds on the data movement required to solve a computational problem, and then developing communication-optimal algorithms that attain these bounds. This thesis extends previous research on communication bounds and computational kernels by presenting bounds on the energy consumption of a large class of algorithms. These bounds apply to sequential, distributed parallel and heterogeneous machine models and we detail methods to further extend these models to larger classes of machines. We argue that the energy consumption of computational kernels is usually predictable and can be modeled via linear models with a handful of terms. Thus, these energy models (and the accompanying bounds) may apply to many HPC applications when used in composition. Given energy bounds, we analyze the implications of such results under additional constraints, such as an upper bound on runtime, and also suggest directions for future research that may aid future development of a hardware/software co-tuning process. Further, we present a new model of energy efficiency, Cityscape, that allows hardware designers to quickly target areas for improvement in hardware attributes. We believe that combining our bounds with other models of energy consumption may provide a useful method for such co-tuning; i.e. to enable algorithm and hardware architects to develop provably energy-optimal algorithms on customized hardware platforms.
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