Analytical Cost Metrics : Days of Future Past

Abstract

ANALYTICAL COST METRICS : DAYS OF FUTURE PASTFuture exascale high-performance computing (HPC) systems are expected to be increasingly heterogeneous, consisting of several multi-core CPUs and a large number of accelerators, specialpurpose hardware that will increase the computing power of the system in a very energy-efficient way. Specialized, energy-efficient accelerators are also an important component in many diverse systems beyond HPC: gaming machines, general purpose workstations, tablets, phones and other media devices. With Moore's law driving the evolution of hardware platforms towards exascale, the dominant performance metric (time efficiency) has now expanded to also incorporate power/energy efficiency.This work builds analytical cost models for cost metrics such as time, energy, memory access, and silicon area. These models are used to predict the performance of applications, for performance tuning, and chip design. The idea is to work with domain specific accelerators where analytical cost models can be accurately used for performance optimization. The performance optimization problems are formulated as mathematical optimization problems. This work explores the analytical cost modeling and mathematical optimization approach in a few ways. For stencil applications and GPU architectures, the analytical cost models are developed for execution time as well as energy.The models are used for performance tuning over existing architectures, and are coupled with silicon area models of GPU architectures to generate highly efficient architecture configurations.For matrix chain products, analytical closed form solutions for off-chip data movement are built and used to minimize the total data movement cost of a minimum op count tree.ii ACKNOWLEDGEMENTS I would like to thank my advisor, Sanjay Rajopadhye, for his guidance and support. He has been a continuous source of motivation. His brilliant ideas and farsightedness have awed me throughout my Ph.D. I learnt a lot from him and enjoyed being his student. I not only gained technical knowledge in the field but also learnt values of a good human being like patience, kindness, selflessness, compassion, and dedication. I feel honored to have the opportunity to work with him.