Performance Analysis of Software Enabled Accelerator Library for Intel Architecture

Abstract

With the advancement of the General Purpose Graphics Processing Unit (GPGPU) for parallel processing, the Central Processing Unit (CPU) based architecture industry focused its development on software accelerator based performance maximizer so as to provide the parallel environment in a software way. This set of accelerator libraries support all stages of data science or data analysis pipelines viz. preprocessing, transformation, analysis, modelling, validation, and decision-making. The recent trends of large dataset based computation models are to use all the available features to create rule-bases for validation rather than feature summarization or reduction based on subset selection statistical techniques. Deep learning allows these computational models that are composed of multiple processing layers to learn and train from the large dataset with multiple levels of abstraction without reduction in features. Intel's Data Analytics Acceleration Library (Intel DAAL) enables applications to process large datasets to make faster and better predictions with available Intel processors in a lab environment. In this paper, we will discuss the enabling factors of Intel's DAAL and how it optimizes the performance in a Python computational environment with and without Anaconda frameworks.