This book aims to achieve the following goals: (1) to provide a high-level survey of key analytics models and algorithms without going into mathematical details; (2) to analyze the usage patterns of these models; and (3) to discuss opportunities for accelerating analytics workloads using software, hardware, and system approaches. The book first describes 14 key analytics models (exemplars) that span data mining, machine learning, and data management domains. For each analytics exemplar, we summarize its computational and runtime patterns and apply the information to evaluate parallelization and acceleration alternatives for that exemplar. Using case studies from important application domains such as deep learning, text analytics, and business intelligence (BI), we demonstrate how various software and hardware acceleration strategies are implemented in practice.

This book is intended for both experienced professionals and students who are interested in understanding core algorithms behind analytics workloads. It is designed to serve as a guide for addressing various open problems in accelerating analytics workloads, e.g., new architectural features for supporting analytics workloads, impact on programming models and runtime systems, and designing analytics systems.

Introduction
Overview of Analytics Exemplars
Accelerating Analytics
Accelerating Analytics in Practice: Case Studies
Architectural Desiderata for Analytics
Bibliography

Authors’ Biographies

Rajesh Bordawekar, IBM T.J. Watson Research Center
Rajesh Bordawekar is a Research Staff Member at the IBM T. J. Watson Research Center. His current interest is exploring software-hardware co-design of analytics workloads. He works at the intersection of high-performance computing, analytics, and data management domains. He has published over 40 technical publications and issued 14 patents. He has also presented tutorials at top conferences including ISCA, ASPLOS, and PPoPP. Recently, he has been investigating how GPUs could be used for accelerating key analytics kernels in text analytics, data management, graph analytics, and deep learning. As part of this work, he collaborates closely with the IBM Power Systems, and various analytics and database product teams.

Bob Blainey, IBM CloudLab
Bob Blainey is an IBM Fellow and the chief architect of the IBM CloudLab. Bob has been with IBM for over 25 years, with a consistent focus on deep optimization of software for IBM systems, and now IBM’s cloud. He spent many years working on programming languages, compilers, tools, and algorithms for parallelism and for high performance on systems, at the microprocessor, node, and cluster level. Bob is now leading a team focused on the invention of the next generation of IBM’s cloud infrastructure, driving leadership efficiency, scale, and performance.

Ruchir Puri, IBM T.J. Watson Research Center
Ruchir Puri is an IBM Fellow at IBM Thomas J Watson Research Center where he leads research efforts in system design and acceleration. Most recently, he led the design methodology innovations for IBM’s Power and zEnterprise microprocessors. Dr. Puri has received numerous accolades including the highest technical position at IBM, the IBM Fellow, which was awarded for his transformational role in microprocessor design methodology. In addition, he has received “Best of IBM” awards in both 2011 and 2012 and IBM Corporate Award from IBM’s CEO, and several IBM Outstanding Technical Achievement awards. Dr. Puri is a Fellow of the IEEE, an ACM Distinguished Speaker and IEEE Distinguished Lecturer. He is also a member of IBM Academy of Technology and IBM Fellow leadership team and was appointed an IBM Master Inventor in 2010. Dr. Puri is a recipient of Semiconductor Research Corporation (SRC) outstanding mentor award and has been an adjunct professor in the Department of Electrical Engineering at Columbia University, and was also honored with the John Von-Neumann Chair at Institute of Discrete Mathematics at Bonn University, Germany. Dr. Puri is also a recipient of the 2014 Asian American Engineer of the Year Award.About the Author(s)