CUDA by Example, written by two senior members of the CUDA software platform team, shows programmers how to employ this new technology. The authors introduce each area of CUDA development through working examples. After a concise introduction to the CUDA platform and architecture, as well as a quick-start guide to CUDA C, the book details the techniques and trade-offs associated with each key CUDA feature. You’ll discover when to use each CUDA C extension and how to write CUDA software that delivers truly outstanding performance.
Major topics covered includeParallel programming Thread cooperation Constant memory and events Texture memory Graphics interoperability Atomics Streams CUDA C on multiple GPUs Advanced atomics Additional CUDA resources
All the CUDA software tools you’ll need are freely available for download from NVIDIA.
Designed for professionals across multiple industrial sectors,Professional CUDA C Programming presents CUDA -- aparallel computing platform and programming model designed to easethe development of GPU programming -- fundamentals in aneasy-to-follow format, and teaches readers how to think in paralleland implement parallel algorithms on GPUs. Each chapter covers aspecific topic, and includes workable examples that demonstrate thedevelopment process, allowing readers to explore both the "hard"and "soft" aspects of GPU programming.
Computing architectures are experiencing a fundamental shifttoward scalable parallel computing motivated by applicationrequirements in industry and science. This book demonstrates thechallenges of efficiently utilizing compute resources at peakperformance, presents modern techniques for tackling thesechallenges, while increasing accessibility for professionals whoare not necessarily parallel programming experts. The CUDAprogramming model and tools empower developers to writehigh-performance applications on a scalable, parallel computingplatform: the GPU. However, CUDA itself can be difficult to learnwithout extensive programming experience. Recognized CUDAauthorities John Cheng, Max Grossman, and Ty McKercher guidereaders through essential GPU programming skills and best practicesin Professional CUDA C Programming, including:CUDA Programming ModelGPU Execution ModelGPU Memory modelStreams, Event and ConcurrencyMulti-GPU ProgrammingCUDA Domain-Specific LibrariesProfiling and Performance Tuning
The book makes complex CUDA concepts easy to understand foranyone with knowledge of basic software development with exercisesdesigned to be both readable and high-performance. For theprofessional seeking entrance to parallel computing and thehigh-performance computing community, Professional CUDA CProgramming is an invaluable resource, with the most currentinformation available on the market.
What You Need:
Node.js 8.xOperating system with bash-like shellOMQ (pronounced "Zero-M-Q") library, version 3.2 or higherElasticsearch version 5.0 or higherjq version 1.5 or higherRedis version 3.2 or higher
Authors Tomasz Nurkiewicz and Ben Christensen include concrete examples that use the RxJava library to solve real-world performance issues on Android devices as well as the server. You’ll learn how RxJava leverages parallelism and concurrency to help you solve today’s problems. This book also provides a preview of the upcoming 2.0 release.Write programs that react to multiple asynchronous sources of input without descending into "callback hell"Get to that aha! moment when you understand how to solve problems in the reactive wayCope with Observables that produce data too quickly to be consumedExplore strategies to debug and to test programs written in the reactive styleEfficiently exploit parallelism and concurrency in your programsLearn about the transition to RxJava version 2
Rust is an open source, safe, concurrent, practical language created by Mozilla. It runs blazingly fast, prevents segfaults, and guarantees safety. This book gets you started with essential software development by guiding you through the different aspects of Rust programming. With this approach, you can bridge the gap between learning and implementing immediately.
Beginning with an introduction to Rust, you’ll learn the basic aspects such as its syntax, data types, functions, generics, control flows, and more. After this, you’ll jump straight into building your first project, a Tetris game. Next you’ll build a graphical music player and work with fast, reliable networking software using Tokio, the scalable and productive asynchronous IO Rust library.
Over the course of this book, you’ll explore various features of Rust Programming including its SDL features, event loop, File I/O, and the famous GTK+ widget toolkit. Through these projects, you’ll see how well Rust performs in terms of concurrency—including parallelism, reliability, improved performance, generics, macros, and thread safety. We’ll also cover some asynchronous and reactive programming aspects of Rust.
By the end of the book, you’ll be comfortable building various real-world applications in Rust.What you will learn Compile and run the Rust projects using the Cargo-Rust Package manager Use Rust-SDL features such as the event loop, windows, infinite loops, pattern matching, and more Create a graphical interface using Gtk-rs and Rust-SDL Incorporate concurrency mechanism and multi-threading along with thread safety and locks Implement the FTP protocol using an Asynchronous I/O stack with the Tokio libraryWho this book is for
Concurrency is becoming more common in responsive and scalable application development, but it’s been extremely difficult to code. The detailed solutions in this cookbook show you how modern tools raise the level of abstraction, making concurrency much easier than before. Complete with ready-to-use code and discussions about how and why the solution works, you get recipes for using:async and await for asynchronous operationsParallel programming with the Task Parallel LibraryThe TPL Dataflow library for creating dataflow pipelinesCapabilities that Reactive Extensions build on top of LINQUnit testing with concurrent codeInterop scenarios for combining concurrent approachesImmutable, threadsafe, and producer/consumer collectionsCancellation support in your concurrent codeAsynchronous-friendly Object-Oriented ProgrammingThread synchronization for accessing data
C++ is a highly portable language and can be used to write both large-scale applications and performance-critical code. It has evolved over the last few years to become a modern and expressive language. This book will guide you through optimizing the performance of your C++ apps by allowing them to run faster and consume fewer resources on the device they're running on without compromising the readability of your code base.
The book begins by helping you measure and identify bottlenecks in a C++ code base. It then moves on by teaching you how to use modern C++ constructs and techniques. You'll see how this affects the way you write code. Next, you'll see the importance of data structure optimization and memory management, and how it can be used efficiently with respect to CPU caches. After that, you'll see how STL algorithm and composable Range V3 should be used to both achieve faster execution and more readable code, followed by how to use STL containers and how to write your own specialized iterators.
Moving on, you’ll get hands-on experience in making use of modern C++ metaprogramming and reflection to reduce boilerplate code as well as in working with proxy objects to perform optimizations under the hood. After that, you’ll learn concurrent programming and understand lock-free data structures. The book ends with an overview of parallel algorithms using STL execution policies, Boost Compute, and OpenCL to utilize both the CPU and the GPU.What you will learnBenefits of modern C++ constructs and techniquesIdentify hardware bottlenecks, such as CPU cache misses, to boost performanceWrite specialized data structures for performance-critical codeUse modern metaprogramming techniques to reduce runtime calculationsAchieve efficient memory management using custom memory allocatorsReduce boilerplate code using reflection techniquesReap the benefits of lock-free concurrent programmingPerform under-the-hood optimizations with preserved readability using proxy objectsGain insights into subtle optimizations used by STL algorithmsUtilize the Range V3 library for expressive C++ codeParallelize your code over CPU and GPU, without compromising readabilityWho this book is for
If you're a C++ developer looking to improve the speed of your code or simply wanting to take your skills up to the next level, then this book is perfect for you.
Author Anders Goransson demonstrates the advantages and disadvantages of each technique, with sample code and detailed explanations for using it efficiently. The first part of the book describes the building blocks of asynchronous processing, and the second part covers Android libraries and constructs for developing fast, responsive, and well-structured apps.Understand multithreading basics in Java and on the Android platformLearn how threads communicate within and between processesUse strategies to reduce the risk of memory leaksManage the lifecycle of a basic threadRun tasks sequentially in the background with HandlerThreadUse Java’s Executor Framework to control or cancel threadsHandle background task execution with AsyncTask and IntentServiceAccess content providers with AsyncQueryHandlerUse loaders to update the UI with new data
Find out about Observables, a unifying data type that simplifies concurrent code and eases the pain of callbacks. Learn how Schedulers change the concept of time itself, making asynchronous testing sane again. Find real-world examples for the browser and Node.js along the way: how about a real-time earthquake visualization in 20 lines of code, or a frantic shoot-'em-up space videogame? You'll also use Cycle.js - a modern, reactive, web framework - to make a new breed of web applications.
By the end of the book, you'll know how to think in a reactive way, and to use RxJS 5 to build complex programs and create amazing reactive user interfaces. You'll also understand how to integrate it with your existing projects and use it with the frameworks you already know. All the code in this new edition has been thoroughly revised and updated for RxJS 5, ES6, and Cycle.js Unified.
What You Need:
NodeJS 6.x and a modern web browser
Written by five leading OpenCL authorities, OpenCL Programming Guide covers the entire specification. It reviews key use cases, shows how OpenCL can express a wide range of parallel algorithms, and offers complete reference material on both the API and OpenCL C programming language.
Through complete case studies and downloadable code examples, the authors show how to write complex parallel programs that decompose workloads across many different devices. They also present all the essentials of OpenCL software performance optimization, including probing and adapting to hardware. Coverage includes
Understanding OpenCL’s architecture, concepts, terminology, goals, and rationale Programming with OpenCL C and the runtime API Using buffers, sub-buffers, images, samplers, and events Sharing and synchronizing data with OpenGL and Microsoft’s Direct3D Simplifying development with the C++ Wrapper API Using OpenCL Embedded Profiles to support devices ranging from cellphones to supercomputer nodes Case studies dealing with physics simulation; image and signal processing, such as image histograms, edge detection filters, Fast Fourier Transforms, and optical flow; math libraries, such as matrix multiplication and high-performance sparse matrix multiplication; and more Source code for this book is available at https://code.google.com/p/opencl-book-samples/
Reap the benefits of functional programming for your next project, whether you’re writing concurrent code, or building data- or math-intensive applications. With this comprehensive book, former F# team member Chris Smith gives you a head start on the fundamentals and walks you through advanced concepts of the F# language.Learn F#’s unique characteristics for building applicationsGain a solid understanding of F#’s core syntax, including object-oriented and imperative stylesMake your object-oriented code better by applying functional programming patternsUse advanced functional techniques, such as tail-recursion and computation expressionsTake advantage of multi-core processors with asynchronous workflows and parallel programmingUse new type providers for interacting with web services and information-rich environmentsLearn how well F# works as a scripting language
This book is for developers who would like to master the C++ STL and make full use of its components. Prior C++ knowledge is assumed.What You Will LearnMake your own iterator types, allocators, and thread pools.Master every standard container and every standard algorithm.Improve your code by replacing new/delete with smart pointers.Understand the difference between monomorphic algorithms, polymorphic algorithms, and generic algorithms.Learn the meaning and applications of vocabulary type, product type and sum type.In Detail
Modern C++ has come a long way since 2011. The latest update, C++17, has just been ratified and several implementations are on the way.
This book is your guide to the C++ standard library, including the very latest C++17 features.
The book starts by exploring the C++ Standard Template Library in depth. You will learn the key differences between classical polymorphism and generic programming, the foundation of the STL. You will also learn how to use the various algorithms and containers in the STL to suit your programming needs. The next module delves into the tools of modern C++. Here you will learn about algebraic types such as std::optional, vocabulary types such as std::function, smart pointers, and synchronization primitives such as std::atomic and std::mutex. In the final module, you will learn about C++'s support for regular expressions and file I/O.
By the end of the book you will be proficient in using the C++17 standard library to implement real programs, and you'll have gained a solid understanding of the library's own internals.Style and approach
This book takes a concise but comprehensive approach to explaining and applying the C++ STL, one feature at a time.
This book will show you how to exploit different parallel architectures to improve your code's performance, scalability, and resilience. You'll learn about seven concurrency models: threads and locks, functional programming, separating identity and state, actors, sequential processes, data parallelism, and the lambda architecture.
Learn about the perils of traditional threads and locks programming and how to overcome them through careful design and by working with the standard library. See how actors enable software running on geographically distributed computers to collaborate, handle failure, and create systems that stay up 24/7/365. Understand why shared mutable state is the enemy of robust concurrent code, and see how functional programming together with technologies such as Software Transactional Memory (STM) and automatic parallelism help you tame it.
You'll learn about the untapped potential within every GPU and how GPGPU software can unleash it. You'll see how to use MapReduce to harness massive clusters to solve previously intractable problems, and how, in concert with Stream Processing, big data can be tamed.
With an understanding of the strengths and weaknesses of each of the different models and hardware architectures, you'll be empowered to tackle any problem with confidence.
What You Need:
The example code can be compiled and executed on *nix, OS X, or Windows. Instructions on how to download the supporting build systems are given in each chapter.
Python Parallel Programming Cookbook is intended for software developers who are well versed with Python and want to use parallel programming techniques to write powerful and efficient code. This book will help you master the basics and the advanced of parallel computing.What You Will LearnSynchronize multiple threads and processes to manage parallel tasksImplement message passing communication between processes to build parallel applicationsProgram your own GPU cards to address complex problemsManage computing entities to execute distributed computational tasksWrite efficient programs by adopting the event-driven programming modelExplore the cloud technology with DJango and Google App EngineApply parallel programming techniques that can lead to performance improvementsIn Detail
Parallel programming techniques are required for a developer to get the best use of all the computational resources available today and to build efficient software systems. From multi-core to GPU systems up to the distributed architectures, the high computation of programs throughout requires the use of programming tools and software libraries. Because of this, it is becoming increasingly important to know what the parallel programming techniques are. Python is commonly used as even non-experts can easily deal with its concepts.
This book will teach you parallel programming techniques using examples in Python and will help you explore the many ways in which you can write code that allows more than one process to happen at once. Starting with introducing you to the world of parallel computing, it moves on to cover the fundamentals in Python. This is followed by exploring the thread-based parallelism model using the Python threading module by synchronizing threads and using locks, mutex, semaphores queues, GIL, and the thread pool.
Next you will be taught about process-based parallelism where you will synchronize processes using message passing along with learning about the performance of MPI Python Modules. You will then go on to learn the asynchronous parallel programming model using the Python asyncio module along with handling exceptions. Moving on, you will discover distributed computing with Python, and learn how to install a broker, use Celery Python Module, and create a worker.
You will also understand the StarCluster framework, Pycsp, Scoop, and Disco modules in Python. Further on, you will learn GPU programming with Python using the PyCUDA module along with evaluating performance limitations. Next you will get acquainted with the cloud computing concepts in Python, using Google App Engine (GAE), and building your first application with GAE. Lastly, you will learn about grid computing concepts in Python and using PyGlobus toolkit, GFTP and GASS COPY to transfer files, and service monitoring in PyGlobus.Style and approach
A step-by-step guide to parallel programming using Python, with recipes accompanied by one or more programming examples. It is a practically oriented book and has all the necessary underlying parallel computing concepts.
Phoenix is the long-awaited web framework based on Elixir, the highly concurrent language that combines a beautiful syntax with rich metaprogramming. The authors, who developed the earliest production Phoenix applications, will show you how to create code that's easier to write, test, understand, and maintain.
The best way to learn Phoenix is to code, and you'll get to attack some interesting problems. Start working with controllers, views, and templates within the first few pages. Build an in-memory repository, and then back it with an Ecto database layer. Learn to use change sets and constraints that keep readers informed and your database integrity intact. Craft your own interactive application based on the channels API for the real-time, high-performance applications that this ecosystem made famous. Write your own authentication components called plugs, and even learn to use the OTP layer for monitored, reliable services. Organize your code with umbrella projects so you can keep your applications modular and easy to maintain.
This is a book by developers and for developers, and we know how to help you ramp up quickly. Any book can tell you what to do. When you've finished this one, you'll also know why to do it.
What You Need:
To work through this book, you will need a computer capable of running Erlang 17 or better, Elixir 1.1, or better, Phoenix 1.0 or better, and Ecto 1.0 or better. A rudimentary knowledge of Elixir is also highly recommended.
Clojure joins the flexibility and agility of Lisp with the reach, stability, and performance of Java. Combine Clojure's tools for maximum effectiveness as you work with immutable data, functional programming, and safe concurrency to write programs that solve real-world problems.
Start by reading and understanding Clojure syntax and see how Clojure is evaluated. From there, find out about the sequence abstraction, which combines immutable collections with functional programming to create truly reusable data transformation code. Clojure is a functional language; learn how to write programs in a functional style, and when and how to use recursion to your advantage. Discover Clojure's unique approach to state and identity, techniques for polymorphism and open systems using multimethods and protocols, and how to leverage Clojure's metaprogramming capabilities via macros. Finally, put all the pieces together in a real program.
New to this edition is coverage of Clojure's spec library, one of the most interesting new features of Clojure for describing both data and functions. You can use Clojure spec to validate data, destructure data, explain invalid data, and generate large numbers of tests to verify the correctness of your code.
With this book, you'll learn how to think in Clojure, and how to take advantage of its combined strengths to build powerful programs quickly.
What You Need:Java 6 or higherClojure 1.9
Get ready to program in a whole new way. Functional Programming in Java will help you quickly get on top of the new, essential Java 8 language features and the functional style that will change and improve your code. This short, targeted book will help you make the paradigm shift from the old imperative way to a less error-prone, more elegant, and concise coding style that's also a breeze to parallelize. You'll explore the syntax and semantics of lambda expressions, method and constructor references, and functional interfaces. You'll design and write applications better using the new standards in Java 8 and the JDK.
Lambda expressions are lightweight, highly concise anonymous methods backed by functional interfaces in Java 8. You can use them to leap forward into a whole new world of programming in Java. With functional programming capabilities, which have been around for decades in other languages, you can now write elegant, concise, less error-prone code using standard Java. This book will guide you though the paradigm change, offer the essential details about the new features, and show you how to transition from your old way of coding to an improved style.
In this book you'll see popular design patterns, such as decorator, builder, and strategy, come to life to solve common design problems, but with little ceremony and effort. With these new capabilities in hand, Functional Programming in Java will help you pick up techniques to implement designs that were beyond easy reach in earlier versions of Java. You'll see how you can reap the benefits of tail call optimization, memoization, and effortless parallelization techniques.
Java 8 will change the way you write applications. If you're eager to take advantage of the new features in the language, this is the book for you.
What you need:
Java 8 with support for lambda expressions and the JDK is required to make use of the concepts and the examples in this book.
This book is for web developers, programmers, and enterprise developers. Basic knowledge of the Go language is assumed. Experience with back-end application development is not necessary, but may help understand the motivation behind some of the recipes.What You Will LearnTest your application using advanced testing methodologiesDevelop an awareness of application structures, interface design, and toolingCreate strategies for third-party packages, dependencies, and vendoringGet to know tricks on treating data such as collectionsHandle errors and cleanly pass them along to calling functionsWrap dependencies in interfaces for ease of portability and testingExplore reactive programming design patterns in GoIn Detail
Go (a.k.a. Golang) is a statically-typed programming language first developed at Google. It is derived from C with additional features such as garbage collection, type safety, dynamic-typing capabilities, additional built-in types, and a large standard library.
This book takes off where basic tutorials on the language leave off. You can immediately put into practice some of the more advanced concepts and libraries offered by the language while avoiding some of the common mistakes for new Go developers.
The book covers basic type and error handling. It explores applications that interact with users, such as websites, command-line tools, or via the file system. It demonstrates how to handle advanced topics such as parallelism, distributed systems, and performance tuning. Lastly, it finishes with reactive and serverless programming in Go.Style and approach
This guide is a handy reference for developers to quickly look up Go development patterns. It is a companion to other resources and a reference that will be useful long after reading it through the first time. Each recipe includes working, simple, and tested code that can be used as a reference or foundation for your own applications.
The purpose of this book is to describe, in an accessible fashion, the various concepts underlying mobile location-based services. These range from general application-related ideas to technical aspects. Each chapter starts with a high level of abstraction and drills down to the technical details. Contributors examine each application from all necessary perspectives, namely, requirements, services, data, and scalability. An illustrative example begins early in the book and runs throughout, serving as a reference.
· This book defines the LBS field and identifies its capabilities, challenges, and technologies.
· The contributors are recognized experts from academia and industry.
· Coverage includes navigation systems, middleware, interoperability, standards, and mobile communications.
· A sample application, the "find-friend" application, is used throughout the book to integrate the concepts discussed in each chapter.
Written by an Intel engineer with over two decades of parallel and concurrent programming experience, this book will help you:
Understand parallelism and concurrencyExplore differences between programming for shared-memory and distributed-memoryLearn guidelines for designing multithreaded applications, including testing and tuningDiscover how to make best use of different threading libraries, including Windows threads, POSIX threads, OpenMP, and Intel Threading Building BlocksExplore how to implement concurrent algorithms that involve sorting, searching, graphs, and other practical computations
The Art of Concurrency shows you how to keep algorithms scalable to take advantage of new processors with even more cores. For developing parallel code algorithms for concurrent programming, this book is a must.
The book explores complexity in big distributed systems and in the natural processes in physics and chemistry, building a platform for understanding how self-organization in big distributed systems can be achieved. It goes beyond the theoretical description of self-organization to present principles for designing self-organizing systems, and concludes by showing the need for a paradigm shift in the development of large-scale systems from strictly deterministic to non-deterministic and adaptive.Analyzes the effect of self-organization applied to computer cloudsFurthers research on principles of self-organization of computing and communication systems inspired by a wealth of self-organizing processes and phenomena in nature and societyPresents a unique analysis of the field, with solutions and case studies
Scientists and technical professionals can use OpenACC to leverage the immense power of modern GPUs without the complexity traditionally associated with programming them. OpenACC™ for Programmers is one of the first comprehensive and practical overviews of OpenACC for massively parallel programming.
This book integrates contributions from 19 leading parallel-programming experts from academia, public research organizations, and industry. The authors and editors explain each key concept behind OpenACC, demonstrate how to use essential OpenACC development tools, and thoroughly explore each OpenACC feature set.
Throughout, you’ll find realistic examples, hands-on exercises, and case studies showcasing the efficient use of OpenACC language constructs. You’ll discover how OpenACC’s language constructs can be translated to maximize application performance, and how its standard interface can target multiple platforms via widely used programming languages.
Each chapter builds on what you’ve already learned, helping you build practical mastery one step at a time, whether you’re a GPU programmer, scientist, engineer, or student. All example code and exercise solutions are available for download at GitHub.Discover how OpenACC makes scalable parallel programming easier and more practical Walk through the OpenACC spec and learn how OpenACC directive syntax is structured Get productive with OpenACC code editors, compilers, debuggers, and performance analysis tools Build your first real-world OpenACC programs Exploit loop-level parallelism in OpenACC, understand the levels of parallelism available, and maximize accuracy or performance Learn how OpenACC programs are compiled Master OpenACC programming best practices Overcome common performance, portability, and interoperability challenges Efficiently distribute tasks across multiple processors Register your product at informit.com/register for convenient access to downloads, updates, and/or corrections as they become available.
How do you move an existing application to AWS, or design your application so that it scales effectively? How much storage will you require? Programming Amazon EC2 not only helps you get started, it will also keep you going once you're successfully positioned in the cloud. This book is a must-read for application architects, developers, and administrators.Determine your application's lifecycle and identify the AWS tools you needLearn how to build and run your application as part of the development processMigrate simple web applications to the cloud with EC2, Amazon Simple Storage Service, and CloudFront content deliveryMeet traffic demand with EC2's Auto Scaling and Elastic Load BalancingDecouple your application using Simple Queue Service, Simple Notification Service, and other toolsUse the right tools to minimize downtime, improve uptime, and manage your decoupled system
"Jurg and Flavia have done a great job in this book building a practical guide on how to build real systems using AWS."
--Werner Vogels, VP & CTO at Amazon.com
The primary requirements of modern-day applications are scalability, speed, and making the most use of hardware. Kotlin meets these requirements with its immense support for concurrency. Many concurrent primitives of Kotlin, such as channels and suspending functions, are designed to be non-blocking and efficient. This allows for new approaches to concurrency and creates unique challenges for the design and implementation of concurrent code. Learning Concurrency in Kotlin addresses those challenges with real-life examples and exercises that take advantage of Kotlin's primitives. Beginning with an introduction to Kotlin's coroutines, you will learn how to write concurrent code and understand the fundamental concepts needed to be able to write multithreaded software in Kotlin. You'll explore how to communicate between and synchronize your threads and coroutines to write asynchronous applications that are collaborative. You'll also learn how to handle errors and exceptions, as well as how to leverage multi-core processing. In addition to this, you’ll delve into how coroutines work internally, allowing you to see the bigger picture. Throughout the book you'll build an Android application – an RSS reader – designed and implemented according to the different topics covered in the bookWhat you will learnUnderstand Kotlin’s approach to concurrencyImplement sequential and asynchronous suspending functionsCreate suspending data sources that are resumed on demandExplore the best practices for error handlingUse channels to communicate between coroutinesUncover how coroutines work under the hoodWho this book is for
If you’re a Kotlin or Android developer interested in learning how to program concurrently to enhance the performance of your applications, this is the book for you.
The first part of this thesis introduces tools and techniques for deterministic parallel programming, including means for encapsulating nondeterminism via powerful commutative building blocks, as well as a novel framework for executing sequential iterative loops in parallel, which lead to deterministic parallel algorithms that are efficient both in theory and in practice. The second part of this thesis introduces Ligra, the first high-level shared memory framework for parallel graph traversal algorithms. The framework allows programmers to express graph traversal algorithms using very short and concise code, delivers performance competitive with that of highly-optimized code, and is up to orders of magnitude faster than existing systems designed for distributed memory. This part of the thesis also introduces Ligra+, which extends Ligra with graph compression techniques to reduce space usage and improve parallel performance at the same time, and is also the first graph processing system to support in-memory graph compression.
The third and fourth parts of this thesis bridge the gap between theory and practice in parallel algorithm design by introducing the first algorithms for a variety of important problems on graphs and strings that are efficient both in theory and in practice. For example, the thesis develops the first linear-work and polylogarithmic-depth algorithms for suffix tree construction and graph connectivity that are also practical, as well as a work-efficient, polylogarithmic-depth, and cache-efficient shared-memory algorithm for triangle computations that achieves a 2–5x speedup over the best existing algorithms on 40 cores.
This is a revised version of the thesis that won the 2015 ACM Doctoral Dissertation Award.
This book allows existing programmers, statisticians and data scientists to learn the Julia and take its advantage while building applications with complex numerical and scientific computations. Basic knowledge of mathematics is needed to understand the various methods that will be used or created in the book to exploit the capabilities for which Julia is made.What You Will LearnUnderstand Julia's ecosystem and create simple programsMaster the type system and create your own types in JuliaUnderstand Julia's type system, annotations, and conversionsDefine functions and understand meta-programming and multiple dispatchCreate graphics and data visualizations using JuliaBuild programs capable of networking and parallel computationDevelop real-world applications and use connections for RDBMS and NoSQLLearn to interact with other programming languages–C and Python—using JuliaIn Detail
Julia is a highly appropriate language for scientific computing, but it comes with all the required capabilities of a general-purpose language. It allows us to achieve C/Fortran-like performance while maintaining the concise syntax of a scripting language such as Python. It is perfect for building high-performance and concurrent applications. From the basics of its syntax to learning built-in object types, this book covers it all.
This book shows you how to write effective functions, reduce code redundancies, and improve code reuse. It will be helpful for new programmers who are starting out with Julia to explore its wide and ever-growing package ecosystem and also for experienced developers/statisticians/data scientists who want to add Julia to their skill-set.
The book presents the fundamentals of programming in Julia and in-depth informative examples, using a step-by-step approach. You will be taken through concepts and examples such as doing simple mathematical operations, creating loops, metaprogramming, functions, collections, multiple dispatch, and so on.
By the end of the book, you will be able to apply your skills in Julia to create and explore applications of any domain.Style and approach
This book demonstrates the basics of Julia along with some data structures and testing tools that will give you enough material to get started with the language from an application standpoint.
The book is aimed at Python developers who want to improve the performance of their application. Basic knowledge of Python is expectedWhat You Will LearnWrite efficient numerical code with the NumPy and Pandas librariesUse Cython and Numba to achieve native performanceFind bottlenecks in your Python code using profilersWrite asynchronous code using Asyncio and RxPyUse Tensorflow and Theano for automatic parallelism in PythonSet up and run distributed algorithms on a cluster using Dask and PySparkIn Detail
Python is a versatile language that has found applications in many industries. The clean syntax, rich standard library, and vast selection of third-party libraries make Python a wildly popular language.
Python High Performance is a practical guide that shows how to leverage the power of both native and third-party Python libraries to build robust applications.
The book explains how to use various profilers to find performance bottlenecks and apply the correct algorithm to fix them. The reader will learn how to effectively use NumPy and Cython to speed up numerical code. The book explains concepts of concurrent programming and how to implement robust and responsive applications using Reactive programming. Readers will learn how to write code for parallel architectures using Tensorflow and Theano, and use a cluster of computers for large-scale computations using technologies such as Dask and PySpark.
By the end of the book, readers will have learned to achieve performance and scale from their Python applications.Style and approach
A step-by-step practical guide filled with real-world use cases and examples
Most programming languages can really complicate things, especially with regard to unsafe memory access. The burden on you, the programmer, lies across two domains: understanding the modern machine and your language's pain-points. This book will teach you to how to manage program performance on modern machines and build fast, memory-safe, and concurrent software in Rust. It starts with the fundamentals of Rust and discusses machine architecture concepts. You will be taken through ways to measure and improve the performance of Rust code systematically and how to write collections with confidence. You will learn about the Sync and Send traits applied to threads, and coordinate thread execution with locks, atomic primitives, data-parallelism, and more.
The book will show you how to efficiently embed Rust in C++ code and explore the functionalities of various crates for multithreaded applications. It explores implementations in depth. You will know how a mutex works and build several yourself. You will master radically different approaches that exist in the ecosystem for structuring and managing high-scale systems.
By the end of the book, you will feel comfortable with designing safe, consistent, parallel, and high-performance applications in Rust.What you will learn Probe your programs for performance and accuracy issues Create your own threading and multi-processing environment in Rust Use coarse locks from Rust’s Standard library Solve common synchronization problems or avoid synchronization using atomic programming Build lock-free/wait-free structures in Rust and understand their implementations in the crates ecosystem Leverage Rust’s memory model and type system to build safety properties into your parallel programs Understand the new features of the Rust programming language to ease the writing of parallel programsWho this book is for
This book is aimed at software engineers with a basic understanding of Rust who want to exploit the parallel and concurrent nature of modern computing environments, safely.
In its second edition, the book retains the lucidity of the first edition and has added new material to reflect the advances in parallel computers. It is designed as text for the final year undergraduate students of computer science and engineering and information technology. It describes the principles of designing parallel computers and how to program them. This second edition, while retaining the general structure of the earlier book, has added two new chapters, ‘Core Level Parallel Processing’ and ‘Grid and Cloud Computing’ based on the emergence of parallel computers on a single silicon chip popularly known as multicore processors and the rapid developments in Cloud Computing. All chapters have been revised and some chapters are re-written to reflect the emergence of multicore processors and the use of MapReduce in processing vast amounts of data.
The new edition begins with an introduction to how to solve problems in parallel and describes how parallelism is used in improving the performance of computers. The topics discussed include instruction level parallel processing, architecture of parallel computers, multicore processors, grid and cloud computing, parallel algorithms, parallel programming, compiler transformations, operating systems for parallel computers, and performance evaluation of parallel computers.
– From the Foreword by Craig Mundie, Chief Research and Strategy Officer, Microsoft Corporation
Author Joe Duffy has risen to the challenge of explaining how to write software that takes full advantage of concurrency and hardware parallelism. In Concurrent Programming on Windows, he explains how to design, implement, and maintain large-scale concurrent programs, primarily using C# and C++ for Windows.
Duffy aims to give application, system, and library developers the tools and techniques needed to write efficient, safe code for multicore processors. This is important not only for the kinds of problems where concurrency is inherent and easily exploitable—such as server applications, compute-intensive image manipulation, financial analysis, simulations, and AI algorithms—but also for problems that can be speeded up using parallelism but require more effort—such as math libraries, sort routines, report generation, XML manipulation, and stream processing algorithms.
Concurrent Programming on Windows has four major sections: The first introduces concurrency at a high level, followed by a section that focuses on the fundamental platform features, inner workings, and API details. Next, there is a section that describes common patterns, best practices, algorithms, and data structures that emerge while writing concurrent software. The final section covers many of the common system-wide architectural and process concerns of concurrent programming.
This is the only book you’ll need in order to learn the best practices and common patterns for programming with concurrency on Windows and .NET.
In three separate sections, ZooKeeper contributors Flavio Junqueira and Benjamin Reed introduce the principles of distributed systems, provide ZooKeeper programming techniques, and include the information you need to administer this service.Learn how ZooKeeper solves common coordination tasksExplore the ZooKeeper API’s Java and C implementations and how they differUse methods to track and react to ZooKeeper state changesHandle failures of the network, application processes, and ZooKeeper itselfLearn about ZooKeeper’s trickier aspects dealing with concurrency, ordering, and configurationUse the Curator high-level interface for connection managementBecome familiar with ZooKeeper internals and administration tools
Complete with case studies that illustrate how Hadoop solves specific problems, this book helps you:
Use the Hadoop Distributed File System (HDFS) for storing large datasets, and run distributed computations over those datasets using MapReduceBecome familiar with Hadoop's data and I/O building blocks for compression, data integrity, serialization, and persistenceDiscover common pitfalls and advanced features for writing real-world MapReduce programsDesign, build, and administer a dedicated Hadoop cluster, or run Hadoop in the cloudUse Pig, a high-level query language for large-scale data processingTake advantage of HBase, Hadoop's database for structured and semi-structured dataLearn ZooKeeper, a toolkit of coordination primitives for building distributed systems
If you have lots of data -- whether it's gigabytes or petabytes -- Hadoop is the perfect solution. Hadoop: The Definitive Guide is the most thorough book available on the subject.
"Now you have the opportunity to learn about Hadoop from a master-not only of the technology, but also of common sense and plain talk."-- Doug Cutting, Hadoop Founder, Yahoo!
As Internet continues to grow and provide practical connectivity between users of computers it has become possible to consider use of computing resources which are far apart and connected by Wide Area Networks.
Instead of using only local computing power it has become practical to access computing resources widely distributed. In some cases between different countries in other cases between different continents.
This idea of using computer power is similar to the well known electric power utility technology. Hence the name of this distributed computing technology is the Grid Computing.
Initially grid computing was used by technologically advanced scientific users.
They used grid computing to experiment with large scale problems which required high performance computing facilities and collaborative work.
In the next stage of development the grid computing technology has become effective and economically attractive for large and medium size commercial companies.
It is expected that eventually the grid computing style of providing computing power will become universal reaching every user in industry and business.
* Written by academic and industrial experts who have developed or used grid computing
* Many proposed solutions have been tested in real life applications
* Covers most essential and technically relevant issues in grid computing
Intel® Xeon PhiTM Processor High-Performance Programming is useful even before you ever program a system with an Intel Xeon Phi processor. To help ensure that your applications run at maximum efficiency, the authors emphasize key techniques for programming any modern parallel computing system whether based on Intel Xeon processors, Intel Xeon Phi processors, or other high-performance microprocessors. Applying these techniques will generally increase your program performance on any system and prepare you better for Intel Xeon Phi processors.A practical guide to the essentials for programming Intel Xeon Phi processorsDefinitive coverage of the Knights Landing architecturePresents best practices for portable, high-performance computing and a familiar and proven threads and vectors programming modelIncludes real world code examples that highlight usages of the unique aspects of this new highly parallel and high-performance computational productCovers use of MCDRAM, AVX-512, Intel® Omni-Path fabric, many-cores (up to 72), and many threads (4 per core)Covers software developer tools, libraries and programming modelsCovers using Knights Landing as a processor and a coprocessor
Distributed Computing with Go gives developers with a good idea how basic Go development works the tools to fulfill the true potential of Golang development in a world of concurrent web and cloud applications. Nikhil starts out by setting up a professional Go development environment. Then you’ll learn the basic concepts and practices of Golang concurrent and parallel development.
You’ll find out in the new few chapters how to balance resources and data with REST and standard web approaches while keeping concurrency in mind. Most Go applications these days will run in a data center or on the cloud, which is a condition upon which the next chapter depends. There, you’ll expand your skills considerably by writing a distributed document indexing system during the next two chapters. This system has to balance a large corpus of documents with considerable analytical demands.
Another use case is the way in which a web application written in Go can be consciously redesigned to take distributed features into account. The chapter is rather interesting for Go developers who have to migrate existing Go applications to computationally and memory-intensive environments. The final chapter relates to the rather onerous task of testing parallel and distributed applications, something that is not usually taught in standard computer science curricula.What you will learn Gain proficiency with concurrency and parallelism in Go Learn how to test your application using Go's standard library Learn industry best practices with technologies such as REST, OpenAPI, Docker, and so on Design and build a distributed search engine Learn strategies on how to design a system for web scaleWho this book is for
This book is for developers who are familiar with the Golang syntax and have a good idea of how basic Go development works. It would be advantageous if you have been through a web application product cycle, although it’s not necessary.
The emphasis of the book is on the modeling and optimization of emerging parallel and distributed embedded systems in relation to the three key design metrics of performance, power and dependability.
Key features:Includes an embedded wireless sensor networks case study to help illustrate the modeling and optimization of distributed embedded systems. Provides an analysis of multi-core/many-core based embedded systems to explain the modeling and optimization of parallel embedded systems.Features an application metrics estimation model; Markov modeling for fault tolerance and analysis; and queueing theoretic modeling for performance evaluation.Discusses optimization approaches for distributed wireless sensor networks; high-performance and energy-efficient techniques at the architecture, middleware and software levels for parallel multicore-based embedded systems; and dynamic optimization methodologies.Highlights research challenges and future research directions.
The book is primarily aimed at researchers in embedded systems; however, it will also serve as an invaluable reference to senior undergraduate and graduate students with an interest in embedded systems research.
Dr. Florian Skopik is one of the leading researchers in Smart Grid security, having organized and led research consortia and panel discussions in this field. Smart Grid Security will provide the first truly holistic view of leading edge Smart Grid security research. This book does not focus on vendor-specific solutions, instead providing a complete presentation of forward-looking research in all areas of Smart Grid security. The book will enable practitioners to learn about upcoming trends, scientists to share new directions in research, and government and industry decision-makers to prepare for major strategic decisions regarding implementation of Smart Grid technology.Presents the most current and leading edge research on Smart Grid security from a holistic standpoint, featuring a panel of top experts in the field.Includes coverage of risk management, operational security, and secure development of the Smart Grid.Covers key technical topics, including threat types and attack vectors, threat case studies, smart metering, smart home, e- mobility, smart buildings, DERs, demand response management, distribution grid operators, transmission grid operators, virtual power plants, resilient architectures, communications protocols and encryption, as well as physical security.
Service Computing: Concept, Method and Technologypresents the concept of service computing and a proposed reference architecture for service computing research before proceeding to introduce two underlying technologies: Web services and service-oriented architecture. It also presents the authors’ latest research findings on hot topics such as service discovery, recommendation, composition, verification, service trust, dynamic configuration and big data service. Some new models and methods are proposed including three service discovery methods based on semantics and skyline technologies, two service recommendation methods using graph mining and QoS prediction, two service composition methods with graph planning and one service verification method using π calculus and so on. Moreover, this book introduces JTang, an underlying platform supporting service computing, which is a product of the authors’ last ten years of research and development. Systematically reviews all the research on service computingIntroduces state-of-art research works on service computing and provides a road map for future directionsBridges the gap between service computing theory and practiceProvides guidance for both industry and academia
• Covers cutting-edge research in HPC on complexenvironments, following an international collaboration of membersof the ComplexHPC
• Explains how to efficiently exploit heterogeneous andhierarchical architectures and distributed systems
• Twenty-three chapters and over 100 illustrations coverdomains such as numerical analysis, communication and storage,applications, GPUs and accelerators, and energy efficiency
The CUDA Handbook begins where CUDA by Example (Addison-Wesley, 2011) leaves off, discussing CUDA hardware and software in greater detail and covering both CUDA 5.0 and Kepler. Every CUDA developer, from the casual to the most sophisticated, will find something here of interest and immediate usefulness. Newer CUDA developers will see how the hardware processes commands and how the driver checks progress; more experienced CUDA developers will appreciate the expert coverage of topics such as the driver API and context migration, as well as the guidance on how best to structure CPU/GPU data interchange and synchronization.
The accompanying open source code–more than 25,000 lines of it, freely available at www.cudahandbook.com–is specifically intended to be reused and repurposed by developers.
Designed to be both a comprehensive reference and a practical cookbook, the text is divided into the following three parts:
Part I, Overview, gives high-level descriptions of the hardware and software that make CUDA possible.
Part II, Details, provides thorough descriptions of every aspect of CUDA, including
The source code accompanying Part II is presented as reusable microbenchmarks and microdemos, designed to expose specific hardware characteristics or highlight specific use cases.
Part III, Select Applications, details specific families of CUDA applications and key parallel algorithms, includingStreaming workloads
Reduction Parallel prefix sum (Scan)
N-body Image Processing These algorithms cover the full range of potential CUDA applications.
Rather than run through all possible scenarios, this pragmatic operations guide calls out what works, as demonstrated in critical deployments.Get a high-level overview of HDFS and MapReduce: why they exist and how they workPlan a Hadoop deployment, from hardware and OS selection to network requirementsLearn setup and configuration details with a list of critical propertiesManage resources by sharing a cluster across multiple groupsGet a runbook of the most common cluster maintenance tasksMonitor Hadoop clusters—and learn troubleshooting with the help of real-world war storiesUse basic tools and techniques to handle backup and catastrophic failure
The growth in grid databases, coupled with the utility ofparallel query processing, presents an important opportunity tounderstand and utilize high-performance parallel databaseprocessing within a major database management system (DBMS). Thisimportant new book provides readers with a fundamentalunderstanding of parallelism in data-intensive applications, anddemonstrates how to develop faster capabilities to support them. Itpresents a balanced treatment of the theoretical and practicalaspects of high-performance databases to demonstrate how parallelquery is executed in a DBMS, including concepts, algorithms,analytical models, and grid transactions.
High-Performance Parallel Database Processing and GridDatabases serves as a valuable resource for researchers workingin parallel databases and for practitioners interested in buildinga high-performance database. It is also a much-needed,self-contained textbook for database courses at the advancedundergraduate and graduate levels.
With the coming of the parallel computing era, computer scientists have turned their attention to designing programming models that are suited for high-performance parallel computing and supercomputing systems. Programming parallel systems is complicated by the fact that multiple processing units are simultaneously computing and moving data. This book offers an overview of some of the most prominent parallel programming models used in high-performance computing and supercomputing systems today.
The chapters describe the programming models in a unique tutorial style rather than using the formal approach taken in the research literature. The aim is to cover a wide range of parallel programming models, enabling the reader to understand what each has to offer. The book begins with a description of the Message Passing Interface (MPI), the most common parallel programming model for distributed memory computing. It goes on to cover one-sided communication models, ranging from low-level runtime libraries (GASNet, OpenSHMEM) to high-level programming models (UPC, GA, Chapel); task-oriented programming models (Charm++, ADLB, Scioto, Swift, CnC) that allow users to describe their computation and data units as tasks so that the runtime system can manage computation and data movement as necessary; and parallel programming models intended for on-node parallelism in the context of multicore architecture or attached accelerators (OpenMP, Cilk Plus, TBB, CUDA, OpenCL). The book will be a valuable resource for graduate students, researchers, and any scientist who works with data sets and large computations.
Timothy Armstrong, Michael G. Burke, Ralph Butler, Bradford L. Chamberlain, Sunita Chandrasekaran, Barbara Chapman, Jeff Daily, James Dinan, Deepak Eachempati, Ian T. Foster, William D. Gropp, Paul Hargrove, Wen-mei Hwu, Nikhil Jain, Laxmikant Kale, David Kirk, Kath Knobe, Ariram Krishnamoorthy, Jeffery A. Kuehn, Alexey Kukanov, Charles E. Leiserson, Jonathan Lifflander, Ewing Lusk, Tim Mattson, Bruce Palmer, Steven C. Pieper, Stephen W. Poole, Arch D. Robison, Frank Schlimbach, Rajeev Thakur, Abhinav Vishnu, Justin M. Wozniak, Michael Wilde, Kathy Yelick, Yili Zheng
This book is for Python developers who have developed Python programs for data processing and now want to learn how to write fast, efficient programs that perform CPU-intensive data processing tasks.What You Will LearnGet an introduction to parallel and distributed computingSee synchronous and asynchronous programmingExplore parallelism in PythonDistributed application with CeleryPython in the CloudPython on an HPC clusterTest and debug distributed applicationsIn Detail
CPU-intensive data processing tasks have become crucial considering the complexity of the various big data applications that are used today. Reducing the CPU utilization per process is very important to improve the overall speed of applications.This book will teach you how to perform parallel execution of computations by distributing them across multiple processors in a single machine, thus improving the overall performance of a big data processing task. We will cover synchronous and asynchronous models, shared memory and file systems, communication between various processes, synchronization, and more.Style and Approach
This example based, step-by-step guide will show you how to make the best of your hardware configuration using Python for distributing applications.
Case studies demonstrate the development process, detailing computational thinking and ending with effective and efficient parallel programs. Topics of performance, floating-point format, parallel patterns, and dynamic parallelism are covered in-depth.
For this new edition, the authors have updated their coverage of CUDA, including coverage of newer libraries, such as CuDNN, moved content that has become less important to appendices, added two new chapters on parallel patterns, and updated case studies to reflect current industry practices.Teaches computational thinking and problem-solving techniques that facilitate high-performance parallel computingUtilizes CUDA version 7.5, NVIDIA's software development tool created specifically for massively parallel environmentsContains new and updated case studiesIncludes coverage of newer libraries, such as CuDNN for Deep Learning
Learn real-life strategies from the people who built Elixir and use it successfully at scale. See how Ben Marx and Bleacher Report maintain one of the highest-traffic Elixir applications by selling the concept to management and delivering on that promise. Find out how Bruce Tate and icanmakeitbetter hire and train Elixir engineers, and the techniques they've employed to design and ensure code consistency since Elixir's early days. Explore customer challenges in deploying and monitoring distributed applications with Elixir creator Jose Valim and Plataformatec.
Make a business case and build a team before you finish your first prototype. Once you're in development, form strategies for organizing your code and learning the constraints of the runtime and ecosystem. Convince stakeholders, both business and technical, about the value they can expect. Prepare to make the critical early decisions that will shape your application for years to come. Manage your deployment with all of the knobs and gauges that good DevOps teams demand. Decide between the many options available for deployment, and how to best prepare yourself for the challenges of running a production application.
This book picks up where most Elixir books leave off. It won't teach you to program Elixir, or any of its tools. Instead, it guides you through the broader landscape and shows you a holistic approach to adopting the language.
What You Need:
This book works with any version of Elixir.