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Book Description

Optimize code for multi-core processors with Intel’s Parallel Studio

Parallel programming is rapidly becoming a “must-know” skill for developers. Yet, where to start? This teach-yourself tutorial is an ideal starting point for developers who already know Windows C and C++ and are eager to add parallelism to their code. With a focus on applying tools, techniques, and language extensions to implement parallelism, this essential resource teaches you how to write programs for multicore and leverage the power of multicore in your programs. Sharing hands-on case studies and real-world examples, the authors examine the challenges of each project and show you how to overcome them.

• Explores conversion of serial code to parallel
• Focuses on implementing Intel Parallel Studio
• Highlights the benefits of using parallel code
• Addresses error and performance optimization of code
• Includes real-world scenarios that illustrate the techniques of advanced parallel programming situations

Parallel Programming with Intel Parallel Studio dispels any concerns of difficulty and gets you started creating faster code with Intel Parallel Studio.

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Book Description

The emergence of the cloud and modern, fast corporate networks demands that you perform judicious balancing of computational loads. Practical Load Balancing presents an entire analytical framework to increase performance not just of one machine, but of your entire infrastructure.

Practical Load Balancing starts by introducing key concepts and the tools you’ll need to tackle your load-balancing issues. You’ll travel through the IP layers and learn how they can create increased network traffic for you. You’ll see how to account for persistence and state, and how you can judge the performance of scheduling algorithms.

You’ll then learn how to avoid performance degradation and any risk of the sudden disappearance of a service on a server. If you’re concerned with running your load balancer for an entire network, you’ll find out how to set up your network topography, and condense each topographical variety into recipes that will serve you in different situations. You’ll also learn about individual servers, and load balancers that can perform cookie insertion or improve your SSL throughput.

You’ll also explore load balancing in the modern context of the cloud. While load balancers need to be configured for high availability once the conditions on the network have been created, modern load balancing has found its way into the cloud, where good balancing is vital for the very functioning of the cloud, and where IPv6 is becoming ever more important.

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Book Description

Learn from legendary Japanese Ruby hacker Masatoshi Seki in this first English-language book on his own Distributed Ruby library. You’ll find out about distributed computing, advanced Ruby concepts and techniques, and the philosophy of the Ruby way—straight from the source.

dRuby has been part of the Ruby standard library for more than a decade, yet few know the true power of the gem. Completely written in Ruby, dRuby enables you to communicate between distributed Ruby processes as if there were no boundaries between processes. This is one of the few books that covers distributed and parallel programming for Ruby developers.

The dRuby Book has been completely updated and expanded from its Japanese version, with three new chapters written by Masatoshi-san. You’ll find out about the design concepts of the dRuby library, and walk through step-by-step tutorial examples. By building various distributed applications, you’ll master distributed programming as well as advanced Ruby techniques such as multithreading, object references, garbage collection, and security. Then you’ll graduate to advanced techniques for using dRuby with Masatoshi-san’s other libraries, such as eRuby and Rinda—the Ruby version of the Linda distributed tuplespace system. In the three new chapters, you’ll see how to integrate dRuby and eRuby, get a thorough grounding in parallel programming concepts with Rinda, and create a full text search system using Drip.

Step by step, you’ll gain mastery of dRuby and distributed computing. Download Now »

Book Description

Techniques for Optimizing Multiprocessor Implementations of Signal Processing Applications

An indispensable component of the information age, signal processing is embedded in a variety of consumer devices, including cell phones and digital television, as well as in communication infrastructure, such as media servers and cellular base stations. Multiple programmable processors, along with custom hardware running in parallel, are needed to achieve the computation throughput required of such applications.

Reviews important research in key areas related to the multiprocessor implementation of multimedia systems
Embedded Multiprocessors: Scheduling and Synchronization, Second Edition presents architectures and design methodologies for parallel systems in embedded digital signal processing (DSP) applications. It discusses application modeling techniques for multimedia systems, the incorporation of interprocessor communication costs into multiprocessor scheduling decisions, and a modeling methodology (the synchronization graph) for multiprocessor system performance analysis. The book also applies the synchronization graph model to develop hardware and software optimizations that can significantly reduce the interprocessor communication overhead of a given schedule.

Chronicles recent activity dealing with single-chip multiprocessors and dataflow models
This edition updates the background material on existing embedded multiprocessors Download Now »

Book Description

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. You’ll learn the basics of Snow, Multicore, Parallel, and some Hadoop-related tools, 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

Table of Contents
Chapter 1 Getting Started
Chapter 2 snow
Chapter 3 multicore
Chapter 4 parallel
Chapter 5 A Primer on MapReduce and Hadoop Download Now »

Book Description

Spring Batch in Action is an in-depth guide to writing batch applications using Spring Batch. Written for developers who have basic knowledge of Java and the Spring lightweight container, the book provides both a best-practices approach to writing batch jobs and comprehensive coverage of the Spring Batch framework.

Spring Batch in Action is a thorough, in-depth guide to writing efficient batch applications. Starting with the basics, it discusses the best practices of batch jobs along with details of the Spring Batch framework. You’ll learn by working through dozens of practical, reusable examples in key areas like monitoring, tuning, enterprise integration, and automated testing.

No prior batch programming experience is required. Basic knowledge of Java and Spring is assumed.

What’s Inside

• Batch programming from the ground up
• Implementing data components
• Handling errors during batch processing
• Automating tedious tasks

Table of Contents
PART 1 BACKGROUND
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