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Cuda Programming

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CUDA by Example

CUDA by Example Book
Author : Jason Sanders,Edward Kandrot
Publisher : Addison-Wesley Professional
Release : 2010-07-19
ISBN : 0132180138
Language : En, Es, Fr & De

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Book Description :

CUDA is a computing architecture designed to facilitate the development of parallel programs. In conjunction with a comprehensive software platform, the CUDA Architecture enables programmers to draw on the immense power of graphics processing units (GPUs) when building high-performance applications. GPUs, of course, have long been available for demanding graphics and game applications. CUDA now brings this valuable resource to programmers working on applications in other domains, including science, engineering, and finance. No knowledge of graphics programming is required—just the ability to program in a modestly extended version of C. 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 include Parallel 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. http://developer.nvidia.com/object/cuda-by-example.html

CUDA Programming

CUDA Programming Book
Author : Shane Cook
Publisher : Newnes
Release : 2012-11-13
ISBN : 0124159338
Language : En, Es, Fr & De

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Book Description :

'CUDA Programming' offers a detailed guide to CUDA with a grounding in parallel fundamentals. It starts by introducing CUDA and bringing you up to speed on GPU parallelism and hardware, then delving into CUDA installation.

Professional CUDA C Programming

Professional CUDA C Programming Book
Author : John Cheng,Max Grossman,Ty McKercher
Publisher : John Wiley & Sons
Release : 2014-09-09
ISBN : 1118739329
Language : En, Es, Fr & De

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Book Description :

Break into the powerful world of parallel GPU programming with this down-to-earth, practical guide Designed for professionals across multiple industrial sectors, Professional CUDA C Programming presents CUDA -- a parallel computing platform and programming model designed to ease the development of GPU programming -- fundamentals in an easy-to-follow format, and teaches readers how to think in parallel and implement parallel algorithms on GPUs. Each chapter covers a specific topic, and includes workable examples that demonstrate the development process, allowing readers to explore both the "hard" and "soft" aspects of GPU programming. Computing architectures are experiencing a fundamental shift toward scalable parallel computing motivated by application requirements in industry and science. This book demonstrates the challenges of efficiently utilizing compute resources at peak performance, presents modern techniques for tackling these challenges, while increasing accessibility for professionals who are not necessarily parallel programming experts. The CUDA programming model and tools empower developers to write high-performance applications on a scalable, parallel computing platform: the GPU. However, CUDA itself can be difficult to learn without extensive programming experience. Recognized CUDA authorities John Cheng, Max Grossman, and Ty McKercher guide readers through essential GPU programming skills and best practices in Professional CUDA C Programming, including: CUDA Programming Model GPU Execution Model GPU Memory model Streams, Event and Concurrency Multi-GPU Programming CUDA Domain-Specific Libraries Profiling and Performance Tuning The book makes complex CUDA concepts easy to understand for anyone with knowledge of basic software development with exercises designed to be both readable and high-performance. For the professional seeking entrance to parallel computing and the high-performance computing community, Professional CUDA C Programming is an invaluable resource, with the most current information available on the market.

The CUDA Handbook

The CUDA Handbook Book
Author : Nicholas Wilt
Publisher : Addison-Wesley
Release : 2013-06-11
ISBN : 0133261506
Language : En, Es, Fr & De

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Book Description :

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 Memory Streams and events Models of execution, including the dynamic parallelism feature, new with CUDA 5.0 and SM 3.5 The streaming multiprocessors, including descriptions of all features through SM 3.5 Programming multiple GPUs Texturing 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, including Streaming workloads Reduction Parallel prefix sum (Scan) N-body Image Processing These algorithms cover the full range of potential CUDA applications.

Hands On GPU Programming with Python and CUDA

Hands On GPU Programming with Python and CUDA Book
Author : Dr. Brian Tuomanen
Publisher : Packt Publishing Ltd
Release : 2018-11-27
ISBN : 1788995228
Language : En, Es, Fr & De

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Book Description :

Build real-world applications with Python 2.7, CUDA 9, and CUDA 10. We suggest the use of Python 2.7 over Python 3.x, since Python 2.7 has stable support across all the libraries we use in this book. Key Features Expand your background in GPU programming—PyCUDA, scikit-cuda, and Nsight Effectively use CUDA libraries such as cuBLAS, cuFFT, and cuSolver Apply GPU programming to modern data science applications Book Description Hands-On GPU Programming with Python and CUDA hits the ground running: you’ll start by learning how to apply Amdahl’s Law, use a code profiler to identify bottlenecks in your Python code, and set up an appropriate GPU programming environment. You’ll then see how to “query” the GPU’s features and copy arrays of data to and from the GPU’s own memory. As you make your way through the book, you’ll launch code directly onto the GPU and write full blown GPU kernels and device functions in CUDA C. You’ll get to grips with profiling GPU code effectively and fully test and debug your code using Nsight IDE. Next, you’ll explore some of the more well-known NVIDIA libraries, such as cuFFT and cuBLAS. With a solid background in place, you will now apply your new-found knowledge to develop your very own GPU-based deep neural network from scratch. You’ll then explore advanced topics, such as warp shuffling, dynamic parallelism, and PTX assembly. In the final chapter, you’ll see some topics and applications related to GPU programming that you may wish to pursue, including AI, graphics, and blockchain. By the end of this book, you will be able to apply GPU programming to problems related to data science and high-performance computing. What you will learn Launch GPU code directly from Python Write effective and efficient GPU kernels and device functions Use libraries such as cuFFT, cuBLAS, and cuSolver Debug and profile your code with Nsight and Visual Profiler Apply GPU programming to datascience problems Build a GPU-based deep neuralnetwork from scratch Explore advanced GPU hardware features, such as warp shuffling Who this book is for Hands-On GPU Programming with Python and CUDA is for developers and data scientists who want to learn the basics of effective GPU programming to improve performance using Python code. You should have an understanding of first-year college or university-level engineering mathematics and physics, and have some experience with Python as well as in any C-based programming language such as C, C++, Go, or Java.

Hands On GPU Programming with CUDA

Hands On GPU Programming with CUDA Book
Author : Jaegeun Han,Bharatkumar Sharma
Publisher : Unknown
Release : 2019-09-27
ISBN : 9781788996242
Language : En, Es, Fr & De

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Book Description :

Explore different GPU programming methods using libraries and directives, such as OpenACC, with extension to languages such as C, C++, and Python Key Features Learn parallel programming principles and practices and performance analysis in GPU computing Get to grips with distributed multi GPU programming and other approaches to GPU programming Understand how GPU acceleration in deep learning models can improve their performance Book Description Compute Unified Device Architecture (CUDA) is NVIDIA's GPU computing platform and application programming interface. It's designed to work with programming languages such as C, C++, and Python. With CUDA, you can leverage a GPU's parallel computing power for a range of high-performance computing applications in the fields of science, healthcare, and deep learning. Learn CUDA Programming will help you learn GPU parallel programming and understand its modern applications. In this book, you'll discover CUDA programming approaches for modern GPU architectures. You'll not only be guided through GPU features, tools, and APIs, you'll also learn how to analyze performance with sample parallel programming algorithms. This book will help you optimize the performance of your apps by giving insights into CUDA programming platforms with various libraries, compiler directives (OpenACC), and other languages. As you progress, you'll learn how additional computing power can be generated using multiple GPUs in a box or in multiple boxes. Finally, you'll explore how CUDA accelerates deep learning algorithms, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). By the end of this CUDA book, you'll be equipped with the skills you need to integrate the power of GPU computing in your applications. What you will learn Understand general GPU operations and programming patterns in CUDA Uncover the difference between GPU programming and CPU programming Analyze GPU application performance and implement optimization strategies Explore GPU programming, profiling, and debugging tools Grasp parallel programming algorithms and how to implement them Scale GPU-accelerated applications with multi-GPU and multi-nodes Delve into GPU programming platforms with accelerated libraries, Python, and OpenACC Gain insights into deep learning accelerators in CNNs and RNNs using GPUs Who this book is for This beginner-level book is for programmers who want to delve into parallel computing, become part of the high-performance computing community and build modern applications. Basic C and C++ programming experience is assumed. For deep learning enthusiasts, this book covers Python InterOps, DL libraries, and practical examples on performance estimation.

CUDA Programming

CUDA Programming Book
Author : Shane Cook
Publisher : Newnes
Release : 2012-12-28
ISBN : 0124159885
Language : En, Es, Fr & De

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Book Description :

If you need to learn CUDA but don't have experience with parallel computing, CUDA Programming: A Developer's Introduction offers a detailed guide to CUDA with a grounding in parallel fundamentals. It starts by introducing CUDA and bringing you up to speed on GPU parallelism and hardware, then delving into CUDA installation. Chapters on core concepts including threads, blocks, grids, and memory focus on both parallel and CUDA-specific issues. Later, the book demonstrates CUDA in practice for optimizing applications, adjusting to new hardware, and solving common problems. Comprehensive introduction to parallel programming with CUDA, for readers new to both Detailed instructions help readers optimize the CUDA software development kit Practical techniques illustrate working with memory, threads, algorithms, resources, and more Covers CUDA on multiple hardware platforms: Mac, Linux and Windows with several NVIDIA chipsets Each chapter includes exercises to test reader knowledge

Multicore and GPU Programming

Multicore and GPU Programming Book
Author : Gerassimos Barlas
Publisher : Elsevier
Release : 2014-12-16
ISBN : 0124171400
Language : En, Es, Fr & De

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Book Description :

Multicore and GPU Programming offers broad coverage of the key parallel computing skillsets: multicore CPU programming and manycore "massively parallel" computing. Using threads, OpenMP, MPI, and CUDA, it teaches the design and development of software capable of taking advantage of today’s computing platforms incorporating CPU and GPU hardware and explains how to transition from sequential programming to a parallel computing paradigm. Presenting material refined over more than a decade of teaching parallel computing, author Gerassimos Barlas minimizes the challenge with multiple examples, extensive case studies, and full source code. Using this book, you can develop programs that run over distributed memory machines using MPI, create multi-threaded applications with either libraries or directives, write optimized applications that balance the workload between available computing resources, and profile and debug programs targeting multicore machines. Comprehensive coverage of all major multicore programming tools, including threads, OpenMP, MPI, and CUDA Demonstrates parallel programming design patterns and examples of how different tools and paradigms can be integrated for superior performance Particular focus on the emerging area of divisible load theory and its impact on load balancing and distributed systems Download source code, examples, and instructor support materials on the book's companion website

GPU Programming in MATLAB

GPU Programming in MATLAB Book
Author : Nikolaos Ploskas,Nikolaos Samaras
Publisher : Morgan Kaufmann
Release : 2016-08-25
ISBN : 0128051337
Language : En, Es, Fr & De

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Book Description :

GPU programming in MATLAB is intended for scientists, engineers, or students who develop or maintain applications in MATLAB and would like to accelerate their codes using GPU programming without losing the many benefits of MATLAB. The book starts with coverage of the Parallel Computing Toolbox and other MATLAB toolboxes for GPU computing, which allow applications to be ported straightforwardly onto GPUs without extensive knowledge of GPU programming. The next part covers built-in, GPU-enabled features of MATLAB, including options to leverage GPUs across multicore or different computer systems. Finally, advanced material includes CUDA code in MATLAB and optimizing existing GPU applications. Throughout the book, examples and source codes illustrate every concept so that readers can immediately apply them to their own development. Provides in-depth, comprehensive coverage of GPUs with MATLAB, including the parallel computing toolbox and built-in features for other MATLAB toolboxes Explains how to accelerate computationally heavy applications in MATLAB without the need to re-write them in another language Presents case studies illustrating key concepts across multiple fields Includes source code, sample datasets, and lecture slides

CUDA Fortran for Scientists and Engineers

CUDA Fortran for Scientists and Engineers Book
Author : Gregory Ruetsch,Massimiliano Fatica
Publisher : Elsevier
Release : 2013-09-11
ISBN : 0124169724
Language : En, Es, Fr & De

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Book Description :

CUDA Fortran for Scientists and Engineers shows how high-performance application developers can leverage the power of GPUs using Fortran, the familiar language of scientific computing and supercomputer performance benchmarking. The authors presume no prior parallel computing experience, and cover the basics along with best practices for efficient GPU computing using CUDA Fortran. To help you add CUDA Fortran to existing Fortran codes, the book explains how to understand the target GPU architecture, identify computationally intensive parts of the code, and modify the code to manage the data and parallelism and optimize performance. All of this is done in Fortran, without having to rewrite in another language. Each concept is illustrated with actual examples so you can immediately evaluate the performance of your code in comparison. Leverage the power of GPU computing with PGI’s CUDA Fortran compiler Gain insights from members of the CUDA Fortran language development team Includes multi-GPU programming in CUDA Fortran, covering both peer-to-peer and message passing interface (MPI) approaches Includes full source code for all the examples and several case studies Download source code and slides from the book's companion website

CUDA Application Design and Development

CUDA Application Design and Development Book
Author : Rob Farber
Publisher : Elsevier
Release : 2011-10-31
ISBN : 0123884268
Language : En, Es, Fr & De

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Book Description :

The book then details the thought behind CUDA and teaches how to create, analyze, and debug CUDA applications. Throughout, the focus is on software engineering issues: how to use CUDA in the context of existing application code, with existing compilers, languages, software tools, and industry-standard API libraries."--Pub. desc.

GPU Parallel Program Development Using CUDA

GPU Parallel Program Development Using CUDA Book
Author : Tolga Soyata
Publisher : CRC Press
Release : 2018-01-19
ISBN : 149875080X
Language : En, Es, Fr & De

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Book Description :

GPU Parallel Program Development using CUDA teaches GPU programming by showing the differences among different families of GPUs. This approach prepares the reader for the next generation and future generations of GPUs. The book emphasizes concepts that will remain relevant for a long time, rather than concepts that are platform-specific. At the same time, the book also provides platform-dependent explanations that are as valuable as generalized GPU concepts. The book consists of three separate parts; it starts by explaining parallelism using CPU multi-threading in Part I. A few simple programs are used to demonstrate the concept of dividing a large task into multiple parallel sub-tasks and mapping them to CPU threads. Multiple ways of parallelizing the same task are analyzed and their pros/cons are studied in terms of both core and memory operation. Part II of the book introduces GPU massive parallelism. The same programs are parallelized on multiple Nvidia GPU platforms and the same performance analysis is repeated. Because the core and memory structures of CPUs and GPUs are different, the results differ in interesting ways. The end goal is to make programmers aware of all the good ideas, as well as the bad ideas, so readers can apply the good ideas and avoid the bad ideas in their own programs. Part III of the book provides pointer for readers who want to expand their horizons. It provides a brief introduction to popular CUDA libraries (such as cuBLAS, cuFFT, NPP, and Thrust),the OpenCL programming language, an overview of GPU programming using other programming languages and API libraries (such as Python, OpenCV, OpenGL, and Apple’s Swift and Metal,) and the deep learning library cuDNN.

Multicore and GPU Programming

Multicore and GPU Programming Book
Author : Gerassimos Barlas
Publisher : Morgan Kaufmann
Release : 2022-05-05
ISBN : 0128141212
Language : En, Es, Fr & De

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Book Description :

Multicore and GPU Programming: An Integrated Approach, Second Edition offers broad coverage of key parallel computing tools, essential for multi-core CPU programming and many-core "massively parallel" computing. Using threads, OpenMP, MPI, CUDA and other state-of-the-art tools, the book teaches the design and development of software capable of taking advantage of modern computing platforms that incorporate CPUs, GPUs and other accelerators. Presenting material refined over more than two decades of teaching parallel computing, author Gerassimos Barlas minimizes the challenge of transitioning from sequential programming to mastering parallel platforms with multiple examples, extensive case studies, and full source code. By using this book, readers will better understand how to develop programs that run over distributed memory machines using MPI, create multi-threaded applications with either libraries or directives, write optimized applications that balance the workload between available computing resources, and profile and debug programs targeting parallel machines. Includes comprehensive coverage of all major multi-core and many-core programming tools and platforms, including threads, OpenMP, MPI, CUDA, OpenCL and Thrust. Covers the most recent versions of the above at the time of publication. Demonstrates parallel programming design patterns and examples of how different tools and paradigms can be integrated for superior performance. Updates in the second edition include the use of the C++17 standard for all sample code, a new chapter on concurrent data structures, a new chapter on OpenCL, and the latest research on load balancing. Includes downloadable source code, examples and instructor support materials on the book’s companion website.

Programming Massively Parallel Processors

Programming Massively Parallel Processors Book
Author : David B. Kirk,Wen-mei W. Hwu
Publisher : Newnes
Release : 2012-12-31
ISBN : 0123914183
Language : En, Es, Fr & De

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Book Description :

Programming Massively Parallel Processors: A Hands-on Approach, Second Edition, teaches students how to program massively parallel processors. It offers a detailed discussion of various techniques for constructing parallel programs. Case studies are used to demonstrate the development process, which begins with computational thinking and ends with effective and efficient parallel programs. This guide shows both student and professional alike the basic concepts of parallel programming and GPU architecture. Topics of performance, floating-point format, parallel patterns, and dynamic parallelism are covered in depth. This revised edition contains more parallel programming examples, commonly-used libraries such as Thrust, and explanations of the latest tools. It also provides new coverage of CUDA 5.0, improved performance, enhanced development tools, increased hardware support, and more; increased coverage of related technology, OpenCL and new material on algorithm patterns, GPU clusters, host programming, and data parallelism; and two new case studies (on MRI reconstruction and molecular visualization) that explore the latest applications of CUDA and GPUs for scientific research and high-performance computing. This book should be a valuable resource for advanced students, software engineers, programmers, and hardware engineers. New coverage of CUDA 5.0, improved performance, enhanced development tools, increased hardware support, and more Increased coverage of related technology, OpenCL and new material on algorithm patterns, GPU clusters, host programming, and data parallelism Two new case studies (on MRI reconstruction and molecular visualization) explore the latest applications of CUDA and GPUs for scientific research and high-performance computing

Programming in Parallel with CUDA

Programming in Parallel with CUDA Book
Author : Richard Ansorge
Publisher : Cambridge University Press
Release : 2022-06-02
ISBN : 1108479537
Language : En, Es, Fr & De

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Book Description :

A handy guide to speeding up scientific calculations with real-world examples including simulation, image processing and image registration.

Embedded Software Design and Programming of Multiprocessor System on Chip

Embedded Software Design and Programming of Multiprocessor System on Chip Book
Author : Katalin Popovici,Frédéric Rousseau,Ahmed A. Jerraya,Marilyn Wolf
Publisher : Springer Science & Business Media
Release : 2010-03-03
ISBN : 9781441955678
Language : En, Es, Fr & De

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Book Description :

Current multimedia and telecom applications require complex, heterogeneous multiprocessor system on chip (MPSoC) architectures with specific communication infrastructure in order to achieve the required performance. Heterogeneous MPSoC includes different types of processing units (DSP, microcontroller, ASIP) and different communication schemes (fast links, non standard memory organization and access). Programming an MPSoC requires the generation of efficient software running on MPSoC from a high level environment, by using the characteristics of the architecture. This task is known to be tedious and error prone, because it requires a combination of high level programming environments with low level software design. This book gives an overview of concepts related to embedded software design for MPSoC. It details a full software design approach, allowing systematic, high-level mapping of software applications on heterogeneous MPSoC. This approach is based on gradual refinement of hardware/software interfaces and simulation models allowing to validate the software at different abstraction levels. This book combines Simulink for high level programming and SystemC for the low level software development. This approach is illustrated with multiple examples of application software and MPSoC architectures that can be used for deep understanding of software design for MPSoC.

Parallel and Concurrent Programming in Haskell

Parallel and Concurrent Programming in Haskell Book
Author : Simon Marlow
Publisher : "O'Reilly Media, Inc."
Release : 2013-07-12
ISBN : 1449335926
Language : En, Es, Fr & De

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Book Description :

If you have a working knowledge of Haskell, this hands-on book shows you how to use the language’s many APIs and frameworks for writing both parallel and concurrent programs. You’ll learn how parallelism exploits multicore processors to speed up computation-heavy programs, and how concurrency enables you to write programs with threads for multiple interactions. Author Simon Marlow walks you through the process with lots of code examples that you can run, experiment with, and extend. Divided into separate sections on Parallel and Concurrent Haskell, this book also includes exercises to help you become familiar with the concepts presented: Express parallelism in Haskell with the Eval monad and Evaluation Strategies Parallelize ordinary Haskell code with the Par monad Build parallel array-based computations, using the Repa library Use the Accelerate library to run computations directly on the GPU Work with basic interfaces for writing concurrent code Build trees of threads for larger and more complex programs Learn how to build high-speed concurrent network servers Write distributed programs that run on multiple machines in a network

Trends in Functional Programming

Trends in Functional Programming Book
Author : Zoltan Horvath
Publisher : Intellect Books
Release : 2013-10-10
ISBN : 184150405X
Language : En, Es, Fr & De

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Book Description :

Volume 10 in the Trends in Functional Programming (TFP) series presents some of the latest research results in the implementation of functional programming languages and the practice of functional programming. It contains a peer-reviewed selection of the best articles presented at the 2009 Tenth Symposium on Trends in Functional Programming held in Komárno, Slovakia. TFP 2009 was co-located with the Third Central European Functional Programming School (CEFP 2009) and organized by the Department of Programming Languages and Compilers, Faculty of Informatics, Eötvös Loránd University, Budapest and the Selye János University, Komárno.TFP brings together international researchers, students and industry professionals dedicated to promoting new research directions and to investigating the relationship between functional programming and other branches of Computer Science. This TFP volume includes some of the latest trends of functional programming, and it is an essential part of any modern programming languages library.

Programming Massively Parallel Processors

Programming Massively Parallel Processors Book
Author : David B. Kirk,Wen-mei W. Hwu
Publisher : Elsevier
Release : 2010-02-22
ISBN : 9780123814739
Language : En, Es, Fr & De

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Book Description :

Programming Massively Parallel Processors discusses the basic concepts of parallel programming and GPU architecture. Various techniques for constructing parallel programs are explored in detail. Case studies demonstrate the development process, which begins with computational thinking and ends with effective and efficient parallel programs. This book describes computational thinking techniques that will enable students to think about problems in ways that are amenable to high-performance parallel computing. It utilizes CUDA (Compute Unified Device Architecture), NVIDIA's software development tool created specifically for massively parallel environments. Studies learn how to achieve both high-performance and high-reliability using the CUDA programming model as well as OpenCL. This book is recommended for advanced students, software engineers, programmers, and hardware engineers. Teaches computational thinking and problem-solving techniques that facilitate high-performance parallel computing. Utilizes CUDA (Compute Unified Device Architecture), NVIDIA's software development tool created specifically for massively parallel environments. Shows you how to achieve both high-performance and high-reliability using the CUDA programming model as well as OpenCL.

GPU Computing Gems Jade Edition

GPU Computing Gems Jade Edition Book
Author : Wen-mei Hwu
Publisher : Elsevier
Release : 2011-09-28
ISBN : 0123859638
Language : En, Es, Fr & De

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Book Description :

"Since the introduction of CUDA in 2007, more than 100 million computers with CUDA capable GPUs have been shipped to end users. GPU computing application developers can now expect their application to have a mass market. With the introduction of OpenCL in 2010, researchers can now expect to develop GPU applications that can run on hardware from multiple vendors"--