In this post, we have prepared a curated top list of reading recommendations for beginners and experienced. This hand-picked list of the best Cuda books and tutorials can help fill your brain this October and ensure you’re getting smarter. We have also mentioned the brief introduction of each book based on the relevant Amazon or Reddit descriptions.
- Professional CUDA C Programming (2014)
- CUDA Programming (2012)
- CUDA by Example: An Introduction to General-Purpose GPU Programming (2010)
- Programming Massively Parallel Processors, Third Edition: A Hands-on Approach (2016)
- CUDA Handbook: A Comprehensive Guide to GPU Programming, The (2013)
- CUDA for Engineers: An Introduction to High-Performance Parallel Computing (2015)
- The CUDA Handbook (2018)
- CUDA Fortran for Scientists (2013)
- Cuda Programming (2014)
- GPU Programming in MATLAB (2016)
- Parallel Programming with OpenACC (2016)
- GPU parallel computing for machine (2017)
Break into the powerful world of parallel GPU programming with this down-to-earth, practical guide.
Author(s): John Cheng, Max Grossman
CUDA Programming (2012)
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.
Author(s): Shane Cook
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.
Author(s): Jason Sanders, Edward Kandrot
Programming Massively Parallel Processors: A Hands-on Approach, Third Edition shows both student and professional alike the basic concepts of parallel programming and GPU architecture, exploring, in detail, various techniques for constructing parallel programs. 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
Author(s): David B. Kirk, Wen-mei W. Hwu
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
Author(s): Nicholas Wilt
CUDA for Engineers allows researchers in engineering and mathematics to perform calculations hundreds of times faster than was previously possible on microcomputers. This permits new kinds of calculations to be performed and reveals this book to be a game changer.
Author(s): Duane Storti, Mete Yurtoglu
The CUDA Handbook (2018)
The CUDA Handbook is the only comprehensive reference to CUDA that exists. 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
Author(s): Nicholas Wilt
CUDA Fortran for Scientists (2013)
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
Author(s): Gregory Ruetsch, Massimiliano Fatica
Cuda Programming (2014)
This book is a practical guide to using CUDA in real applications, by real practitioners. At the same time, however, we cover the necessary theory and background so everyone, no matter what their background, can follow along and learn how to program in CUDA, making this book ideal for both professionals and those studying GPUs or parallel programming.
Author(s): Shane Cook
GPU Programming in MATLAB (2016)
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
Author(s): Nikolaos Ploskas, Nikolaos Samaras
Parallel Programming with OpenACC (2016)
Parallel Programming with OpenACC is a modern, practical guide to implementing dependable computing systems. The book explains how anyone can use OpenACC to quickly ramp-up application performance using high-level code directives called pragmas. The OpenACC directive-based programming model is designed to provide a simple, yet powerful, approach to accelerators without significant programming effort. Author Rob Farber, working with a team of expert contributors
Author(s): Rob Farber
This book illustrates how to build a GPU parallel computer. If you don’t want to waste your time for building, you can buy a built-in-GPU desktop/laptop machine. All you need to do is to install GPU-enabled software for parallel computing. Imagine that we are in the midst of a parallel computing era. The GPU parallel computer is suitable for machine learning, deep (neural network) learning. For example, GeForce GTX1080 Ti is a GPU board with 3584 CUDA cores.
Author(s): Yoshiyasu Takefuji
Best CUDA Books You Should Read
We highly recommend you to buy all paper or e-books in a legal way, for example, on Amazon. But sometimes it might be a need to dig deeper beyond the shiny book cover. Before making a purchase, you can visit resources like Library Genesis and download some Cuda books mentioned below at your own risk. Once again, we do not host any illegal or copyrighted files, but simply give our visitors a choice and hope they will make a wise decision.
Programming in Parallel with CUDA: A Practical Guide
Author(s): Richard Ansorge
ID: 3271216, Publisher: Cambridge University Press, Year: 2022, Size: 13 Mb, Format: pdf
Modern Data Mining Algorithms in C++ and CUDA C: Recent Developments in Feature Extraction and Selection Algorithms for Data Science
Author(s): Timothy Masters
ID: 2533710, Publisher: Apress, Year: 2020, Size: 2 Mb, Format: pdf
Learn CUDA Programming: A beginner's guide to GPU programming and parallel computing with CUDA 10.x and C/C++
Author(s): Jaegeun Han, Bharatkumar Sharma
ID: 2526345, Publisher: Packt Publishing, Year: 2019, Size: 33 Mb, Format: epub
Please note that this booklist is not absolute. Some books are absolutely hot items according to Chicago Tribune, others are composed by unknown writers. On top of that, you can always find additional tutorials and courses on Coursera, Udemy or edX, for example. Are there any other relevant resources you could recommend? Drop a comment if you have any feedback on the list.