Parallel

Two women. Two worlds. One goal: to stay alive. All Fara wants is to earn enough tips to pay the bills, somehow learn to stand up for herself, and eat a good cheeseburger. What she gets is a federal agent who is convinced she is hiding a dangerous device in her tiny apartment; and a new and unexplainable ability that she really could do without. All Blu wants is to restore democracy to her world, preferably without dying in the process. What she gets is the unwanted attention of two of the most lethal leaders of Jurisdiction; one who wants nothing more than to kill her, and the other...well...it's complicated. Neither woman had 'run for my life' on their to-do list, but you don't always get what you want.

What if you woke up to a new life every day? A collision of universes leaves Abby living two lives at once - and sharing them with her own double. Two worlds. Two guys. Two selves. How will she stay in control?

GNU Parallel is a UNIX shell tool for running jobs in parallel. Learn how to use GNU Parallel from the developer of GNU Parallel.

Western thinking is failing because it was not designed to deal with change In this provocative masterpiece of creative thinking, Edward de Bono argues for a game-changing new way to think. For thousands of years we have followed the thinking system designed by the Greek philosophers Socrates, Plato and Aristotle, based on analysis and argument. But if we are to flourish in today’s rapidly changing world we need to free our minds of these ‘boxes’ and embrace a more flexible and nimble model. Parallel Thinking is an invaluable insight into the word of creativity; de Bono unveils unique methods of brainstorming and explains preconceived ideas of what creativity involves and is. This book is not about philosophy; it is about the practical (and parallel) thinking required to get things done in an ever-changing world.

Designed for introductory parallel computing courses at the advanced undergraduate or beginning graduate level, Elements of Parallel Computing presents the fundamental concepts of parallel computing not from the point of view of hardware, but from a more abstract view of algorithmic and implementation patterns. The aim is to facilitate the teaching of parallel programming by surveying some key algorithmic structures and programming models, together with an abstract representation of the underlying hardware. The presentation is friendly and informal. The content of the book is language neutral, using pseudocode that represents common programming language models. The first five chapters present core concepts in parallel computing. SIMD, shared memory, and distributed memory machine models are covered, along with a brief discussion of what their execution models look like. The book also discusses decomposition as a fundamental activity in parallel algorithmic design, starting with a naive example, and continuing with a discussion of some key algorithmic structures. Important programming models are presented in depth, as well as important concepts of performance analysis, including work-depth analysis of task graphs, communication analysis of distributed memory algorithms, key performance metrics, and a discussion of barriers to obtaining good performance. The second part of the book presents three case studies that reinforce the concepts of the earlier chapters. One feature of these chapters is to contrast different solutions to the same problem, using select problems that aren't discussed frequently in parallel computing textbooks. They include the Single Source Shortest Path Problem, the Eikonal equation, and a classical computational geometry problem: computation of the two-dimensional convex hull. After presenting the problem and sequential algorithms, each chapter first discusses the sources of parallelism then surveys parallel algorithms.

It’s tough to argue with R as a high-quality, cross-platform, open source statistical software product—unless you’re in the business of crunching Big Data. This concise book introduces you to several strategies for using R to analyze large datasets, including three chapters on using R and Hadoop together. You’ll learn the basics of Snow, Multicore, Parallel, Segue, RHIPE, and Hadoop Streaming, including how to find them, how to use them, when they work well, and when they don’t. With these packages, you can overcome R’s single-threaded nature by spreading work across multiple CPUs, or offloading work to multiple machines to address R’s memory barrier. Snow: works well in a traditional cluster environment Multicore: popular for multiprocessor and multicore computers Parallel: part of the upcoming R 2.14.0 release R+Hadoop: provides low-level access to a popular form of cluster computing RHIPE: uses Hadoop’s power with R’s language and interactive shell Segue: lets you use Elastic MapReduce as a backend for lapply-style operations