This book examines the present and future of soft computer techniques. It explains how to use the latest technological tools, such as multicore processors and graphics processing units, to implement highly efficient intelligent system methods using a general purpose computer.

High Performance Computing: Programming and Applications presents techniques that address new performance issues in the programming of high performance computing (HPC) applications. Omitting tedious details, the book discusses hardware architecture concepts and programming techniques that are the most pertinent to application developers for achievi

Parallel and High Performance Computing offers techniques guaranteed to boost your code’s effectiveness. Summary Complex calculations, like training deep learning models or running large-scale simulations, can take an extremely long time. Efficient parallel programming can save hours—or even days—of computing time. Parallel and High Performance Computing shows you how to deliver faster run-times, greater scalability, and increased energy efficiency to your programs by mastering parallel techniques for multicore processor and GPU hardware. About the technology Write fast, powerful, energy efficient programs that scale to tackle huge volumes of data. Using parallel programming, your code spreads data processing tasks across multiple CPUs for radically better performance. With a little help, you can create software that maximizes both speed and efficiency. About the book Parallel and High Performance Computing offers techniques guaranteed to boost your code’s effectiveness. You’ll learn to evaluate hardware architectures and work with industry standard tools such as OpenMP and MPI. You’ll master the data structures and algorithms best suited for high performance computing and learn techniques that save energy on handheld devices. You’ll even run a massive tsunami simulation across a bank of GPUs. What's inside Planning a new parallel project Understanding differences in CPU and GPU architecture Addressing underperforming kernels and loops Managing applications with batch scheduling About the reader For experienced programmers proficient with a high-performance computing language like C, C++, or Fortran. About the author Robert Robey works at Los Alamos National Laboratory and has been active in the field of parallel computing for over 30 years. Yuliana Zamora is currently a PhD student and Siebel Scholar at the University of Chicago, and has lectured on programming modern hardware at numerous national conferences. Table of Contents PART 1 INTRODUCTION TO PARALLEL COMPUTING 1 Why parallel computing? 2 Planning for parallelization 3 Performance limits and profiling 4 Data design and performance models 5 Parallel algorithms and patterns PART 2 CPU: THE PARALLEL WORKHORSE 6 Vectorization: FLOPs for free 7 OpenMP that performs 8 MPI: The parallel backbone PART 3 GPUS: BUILT TO ACCELERATE 9 GPU architectures and concepts 10 GPU programming model 11 Directive-based GPU programming 12 GPU languages: Getting down to basics 13 GPU profiling and tools PART 4 HIGH PERFORMANCE COMPUTING ECOSYSTEMS 14 Affinity: Truce with the kernel 15 Batch schedulers: Bringing order to chaos 16 File operations for a parallel world 17 Tools and resources for better code

Developing software for current and especially for future architectures will require knowledge about parallel programming techniques of applications and library p- grammers. Multi-core processors are already available today, and processors with a dozen and more cores are on the horizon. The major driving force in hardware development, the game industry, has - ready shown interest in using parallel programming paradigms, such as OpenMP for further developments. Therefore developers have to be supported in the even more complex task of programming for these new architectures. HLRS has a long-lasting tradition of providing its user community with the most up-to-date software tools. Additionally, important research and development projects are worked on at the center: among the software packages developed are the MPI correctness checker Marmot, the OpenMP validation suite and the M- implementations PACX-MPI and Open MPI. All of these software packages are - ing extended in the context of German and European community research projects, such as ParMA, the InterActive European Grid (I2G) project and the German C- laborative Research Center (Sonderforschungsbereich 716). Furthermore, ind- trial collaborations, i.e. with Intel and Microsoft allow HLRS to get its software production-grade ready. In April 2007, a European project on Parallel Programming for Multi-core - chitectures, in short ParMA was launched, with a major focus on providing and developing tools for parallel programming.

The book discusses the fundamentals of high-performance computing. The authors combine visualization, comprehensibility, and strictness in their material presentation, and thus influence the reader towards practical application and learning how to solve real computing problems. They address both key approaches to programming modern computing systems: multithreading-based parallelizing in shared memory systems, and applying message-passing technologies in distributed systems. The book is suitable for undergraduate and graduate students, and for researchers and practitioners engaged with high-performance computing systems. Each chapter begins with a theoretical part, where the relevant terminology is introduced along with the basic theoretical results and methods of parallel programming, and concludes with a list of test questions and problems of varying difficulty. The authors include many solutions and hints, and often sample code.

Write efficient, clean, and powerful Scala code and create high-performing applications that your users will loveAbout This Book*This is the first book that explores Scala performance techniques in depth, including how to benchmark your performance so you can understand where to make gains*It provides a first-principles examination of what performance means in a Scala context*This book was written by industry experts Vincent Theron and Michael DiamantWho This Book Is ForIf you are a Scala developer with experience in programming Scala applications and know the basics in Scala, syntax, and frameworks such as Lift or Play, this book is for you. This book will also be useful if you are a Java developer who is interested in switching to Scala, but you don't want to give up the performance of Java code. No knowledge of anything outside Scala is required.What You Will Learn*Find out about performance and how to evaluate the behavior of an application*Analyze the performance of your application on JVM*Use Scala features to achieve a high performance benchmark for your application*Enhance the performance of your application with the Collection API*Explore asynchronous programming to achieve concurrency and parallelism*Achieve a deeper understanding of high performance using advanced toolsIn DetailScala is a statically and strongly typed language that tries to elegantly blend both functional and object-oriented paradigms. It has experienced growing popularity in the past few years as both an appealing and pragmatic choice to write production-ready software in the functional paradigm. Scala lets you solve problems with less code than the alternatives. However, this programmatic gain can come at the cost of performance if you aren't careful.Scala High Performance Programming is written to arm you with the knowledge you need to create highly efficient, clean Scala applications. Starting with the basics of understanding what performance is in a Scala context, we'll look at how to benchmark your performance so you can see the results of your optimizations in action. We'?ll also take a deep dive into type specialization, concurrency, and parallel programming. By the end of the book, you'll be able to code efficient, optimized, solutions in Scala.

This book will help you understand what "programming for performance" means, and use effective coding patterns and techniques to optimize your .NET applications. You will begin by understanding what "high performance coding" means, and the different performance concerns. You will see how CLR works and get an understanding of concepts such as memory management, garbage collection, and thread life cycles. You will proceed to learn about the theoretical and practical concepts of PLINQ programming. You will also see what Big Data is, and how to architect a Big Data solution to manipulate large datasets. Finally, you will learn how to launch and analyze a profile session and execute tests against a code block or application for performance analysis. By the end of this book, you will have a complete understanding of efficient programming using high-performance techniques, and will able to write highly optimized applications.