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Programming in Parallel with CUDA Richard Ansorge (University of Cambridge)

Programming in Parallel with CUDA By Richard Ansorge (University of Cambridge)

Programming in Parallel with CUDA by Richard Ansorge (University of Cambridge)

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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.

Programming in Parallel with CUDA Summary

Programming in Parallel with CUDA: A Practical Guide by Richard Ansorge (University of Cambridge)

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.

About Richard Ansorge (University of Cambridge)

Richard Ansorge is Emeritus University Senior Lecturer at the Cavendish Laboratory, University of Cambridge and Emeritus Tutor and Fellow at Fitzwilliam College, Cambridge. He is the author of over 170 peer-reviewed publications and co-author of the book The Physics and Mathematics of MRI (2016).

Table of Contents

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; A. A brief history of CUDA; B. Atomic operations; C. The NVCC complier; D. AVX and the Intel complier; E. Number formats; F. CUDA documentation and libraries; G. The CX header files; H. AI and Python; I. Topics in C++; Index.

Additional information

Programming in Parallel with CUDA: A Practical Guide by Richard Ansorge (University of Cambridge)
Cambridge University Press
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