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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.
Introduction to GPU Kernels and Hardware
Thinking and Coding in Parallel
Warps and Cooperative Groups
Parallel Stencils
Textures
Monte Carlo Applications
Concurrency Using CUDA Streams and Events
Application to PET Scanners
Scaling Up
Tools for Profiling and Debugging
Tensor Cores