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.

Last updated: May 2026
Contributions welcome! See Contributing.

• 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.

• 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

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).

• 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.

Star the repo if this helps you write faster kernels! 🚀
Fully expanded after a full web search — this is now the most complete public CUDA books list.