A curated list of every major book on CUDA programming — beginner to advanced, C++/Python, architecture, optimization, and the latest 2024–2026 releases.
Focused on practical, high-quality resources for NVIDIA GPU parallel computing.

Contributions welcome! See Contributing.

• Beginner / Getting Started
• Core Architecture & Parallel Programming
• Practical & Hands-on Guides
• Advanced / Optimization / Reference
• Python & High-Level CUDA
• Modern & Recent Releases (2022–2026)
• Contributing
• Related Awesome Lists

• CUDA by Example: An Introduction to General-Purpose GPU Programming
  Jason Sanders & Edward Kandrot (2010, Addison-Wesley)
  The timeless classic. Short, example-driven, perfect first book.

• Learn CUDA Programming
  Jaegeun Han & Bharatkumar Sharma (2019, Packt)
  Modern beginner-to-intermediate with CUDA 10+ examples and GitHub repo.

• CUDA for Engineers: An Introduction to High-Performance Parallel Computing
  Mete Yurtoglu & Duane Storti (2016, Addison-Wesley)
  Engineer-focused, hands-on projects for scientists and non-CS folks.

• Programming Massively Parallel Processors: A Hands-on Approach (3rd Edition)
  David B. Kirk & Wen-mei W. Hwu (2022)
  The definitive GPU architecture bible. Used in universities worldwide.

• Programming in Parallel with CUDA: A Practical Guide
  Richard Ansorge (2022, Cambridge University Press)
  Real-world scientific examples (stencils, Monte Carlo, imaging). Excellent modern C++ coverage.

• Professional CUDA C Programming
  John Cheng, Max Grossman & Ty McKercher (2014, Wrox)
  Production-level: multi-GPU, streams, libraries, and performance pitfalls.

• GPU Parallel Program Development Using CUDA
  Tolga Soyata (2018, Chapman & Hall/CRC)
  Strong on libraries (cuBLAS, cuFFT, Thrust, NPP) and OpenCL comparison.

• CUDA for Deep Learning Elliot Arledge (2025, Manning) From first kernels to Flash Attention — teaches hands-on CUDA optimization for deep learning with Nsight Compute profiling.

• The CUDA Handbook: A Comprehensive Guide to GPU Programming
  Nicholas Wilt (2013)
  The deep-dive reference. Every API detail and low-level trick.

• CUDA Programming: A Developer's Guide to Parallel Computing with GPUs
  Shane Cook (2013, Morgan Kaufmann)
  Parallel algorithms, optimization patterns, and best practices.

• CUDA Application Design and Development
  Rob Farber (2011, Morgan Kaufmann)
  Real research applications and scalable design.

• Hands-On GPU Programming with Python and CUDA
  Brian Tuomanen (2018, Packt)
  Best for Python users — Numba, CuPy, and raw bindings.

• GPU Programming with C++ and CUDA (or 9781805128823 variant)
  Paulo Motta (2024, Packt)
  Modern C++20 + Python interop (pybind11).

• Programming in Parallel with CUDA (Ansorge, 2022) — see above

• Programming Massively Parallel Processors (3rd Ed.) (Kirk & Hwu, 2022) — see above

• GPU Programming with C++ and CUDA (Motta, 2024) — see above

• CUDA for Deep Learning (Arledge, 2025, Manning) — see above

Notable 2024–2026 titles (mostly specialized or self-published but frequently appearing in searches):

• CUDA C++ Optimization – David Spuler (2024) — kernel performance & memory tuning
• CUDA C++ Debugging – Dr. David Spuler (2024) — error checking & Nsight
• CUDA Programming from Basics to Advanced – Finbarrs Oketunji (2024, covers CUDA 12.6)
• CUDA Mastery – Elbert Gale (2024) — scientific simulations & CUDA-X
• CUDA in Action – Leon Chapman (2024) — Tensor Cores & multi-GPU
• Mastering CUDA C++ Programming – Brett Neutreon (2024) / Toby Webber (2025) — comprehensive C++ guides
• High-Performance Computing with C++26 and CUDA 13 – William M. Crutcher (2026)

Pro tip: CUDA changes fast. Always pair books with the free official CUDA C++ Programming Guide (v13.x, 2026).

Contributions are welcome! See contributing.md for the full guide.

Quick version:

• Add a new high-quality book? Open a PR with title, authors, year, short description, and link.
• Preference for books post-2018 or still relevant classics.
• Only include books with substantial code/examples and good reviews.

• Awesome-CUDA — tools & libraries
• Awesome-GPU
• awesome-parallel-computing

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Star the repo if this helps you write faster kernels! 🚀

Inspired by GoBooks.

MIT © Dariush Abbasi & Altern