Introduction to Parallel Processing

Advancements in microprocessor architecture, interconnection technology, and software development have fueled rapid growth in parallel and distributed computing. However, this development is only of practical benefit if it is accompanied by progress in the design, analysis and programming of parallel algorithms.

A didactic concept is undertaken for microelectronics, computer technology and communication engineering, which deals with the visualization of signals and processes in addition to graphical programming of signal processing systems. Through the utilization of a professional and globally supported software for metrology and control engineering, DasyLab, useful applications can be developed, modified and optimized.

"This book is required reading for anyone working with accelerator-based computing systems."
–From the Foreword by Jack Dongarra, University of Tennessee and Oak Ridge National Laboratory

Level Up Your Flask Microservices: Advanced Hands-On Pattern for Job Queue Management to Scale and Speed Up Workflows

by Robert Kuhn (Author), David Padua (Author)
This historical survey of parallel processing from 1980 to 2020 is a follow-up to the authors’ 1981 Tutorial on Parallel Processing, which covered the state of the art in hardware, programming languages, and applications

Commerce and research are being transformed by data-driven discovery and prediction. Skills required for data analytics at massive levels – scalable data management on and off the cloud, parallel algorithms, statistical modeling, and proficiency with a complex ecosystem of tools and platforms – span a variety of disciplines and are not easy to obtain through conventional curricula. Tour the basic techniques of data science, including both SQL and NoSQL solutions for massive data management (e.g., MapReduce and contemporaries), algorithms for data mining (e.g., clustering and association rule mining), and basic statistical modeling (e.g., linear and non-linear regression).