Parallel Computing for Data Science

Dr. Norman Matloff is a professor of computer science at the University of California, Davis, where he was a founding member of the Department of Statistics. He is a statistical consultant and a former database software developer. He has published numerous articles in prestigious journals, such as the ACM Transactions on Database Systems, ACM Transactions on Modeling and Computer Simulation, Annals of Probability, Biometrika, Communications of the ACM, and IEEE Transactions on Data Engineering. He earned a PhD in pure mathematics from UCLA, specializing in probability/functional analysis and statistics.

Introduction to Parallel Processing in R. "Why Is My Program So Slow?": Obstacles to Speed. Principles of Parallel Loop Scheduling. The Shared Memory Paradigm: A Gentle Introduction through R. The Shared Memory Paradigm: C Level. The Shared Memory Paradigm: GPUs. Thrust and Rth. The Message Passing Paradigm. MapReduce Computation. Parallel Sorting and Merging. Parallel Prefix Scan. Parallel Matrix Operations. Inherently Statistical Approaches: Subset Methods. Appendices.