PROGRAMMING IN PARALLEL WITH CUDA PROGRAMMING IN PARALLEL WITH CUDA : a practical guide

"CUDA is now the dominant language used for programming GPUs, one of the most exciting hardware developments of recent decades. With CUDA, you can use a desktop PC for work that would have previously required a large cluster of PCs or access to a HPC facility. As a result, CUDA is increasingly important in scientific and technical computing across the whole STEM community, from medical physics and financial modelling to big data applications and beyond. This unique book on CUDA draws on the author's passion for and long experience of developing and using computers to acquire and analyse scientific data. The result is an innovative text featuring a much richer set of examples than found in any other comparable book on GPU computing. Much attention has been paid to the C++ coding style, which is compact, elegant and efficient. A code base of examples and supporting material is available online, which readers can build on for their own projects"-- Provided by publisher Contents Preface 1. Introduction to GPU Kernels and Hardware 2. Thinking and Coding in Parallel 3. Warps and Cooperative Groups 4. Parallel Stencils 5. Textures 6. Monte Carlo Applications 7. Concurrency Using CUDA Streams and Events 8 Application to PET Scanners 9. Scaling Up 10. Tools for Profiling and Debugging 11. Tensor Cores Appendix A. A Brief History of CUDA Appendix B. Atomic Operations Appendix C. The NVCC Compiler Appendix D. AVX and the Intel Compiler Appendix E. Number Formats Appendix F. CUDA Documentation and Libraries Appendix G. The CX Header Files Appendix H. AI and Python Appendix I. Topics in C++ Index This practical guide shows how to analyse, manipulate or simulate scientific or other numerical data using the power of modern GPUs to greatly increase the speed of calculations. Aimed at researchers and graduate students, it contains numerous real-world examples in clear uncluttered C++ code. All example code is available online. A handy guide to speeding up scientific calculations with real-world examples including simulation, image processing and image registration.