LAUSR

SpMV is a vital computing operation of many scientific, engineering, economic and social applications, increasingly being used to develop timely intelligence for the design and management of smart societies. Several factors affect the performance of SpMV computations, such as matrix characteristics, storage formats, software and hardware platforms. The complexity of the computer systems is on the rise with the increasing number of cores per processor, different levels of caches, processors per node and high speed interconnect. There is an ever-growing need for new optimization techniques and efficient ways of exploiting parallelism. In this paper, we propose ZAKI, a data-driven, machine-learning approach and tool, to predict the optimal number of processes for SpMV computations of an arbitrary sparse matrix on a distributed memory machine. The aim herein is to allow application scientists to automatically obtain the best configuration, and hence the best performance, for the execution of SpMV computations. We train and test the tool using nearly 2000 real world matrices obtained from 45 application domains including computational fluid dynamics (CFD), computer vision, and robotics. The tool uses three machine learning methods, decision trees, random forest, gradient boosting, and is evaluated in depth. A discussion on the applicability of our proposed tool to energy efficiency optimization of SpMV computations is given. This is the first work where the sparsity structure of matrices have been exploited to predict the optimal number of processes for a given matrix in distributed memory environments by using different base and ensemble machine learning methods.