This guide iincludes updates that cover both the Kepler and Maxwell GPUs from NVIDIA, as well as the latest heterogeneous systems from AMD. Suitable for someone without a parallel programming background or previous CUDA experience, as well as those who already have dabbled in GPU programming, the contents range from installation and getting started, to building your own GPU workstation.
This revision includes a new chapter on visualizing data, and new content on the latest CUDA features including data caching, shared memory, and dynamic parallelism. Author Shane Cook also covers the latest host systems and changes to the installation process, NVIDIA’s Parallel NSight IDE, and hardware systems that run CUDA applications. The final new chapter looks ahead to future GPU platforms and releases including on-core ARM CPU and NVlink technologies.
Shane Cook is Technical Director at CUDA Developer, a consultancy company that helps companies exploit the power of GPUs by re-engineering code to make the optimal use of the hardware available. He formed CUDA Developer upon realizing the potential of heterogeneous systems and CUDA to disrupt existing serial and parallel programming technologies. He has a degree in Applied Software Engineering, specializing in the embedded software field. He has worked in senior roles with blue chip companies over the past twenty years, always seeking to help to develop the engineers in his team. He has worked on C programming standards including the MISRA Safer C used by widely in the automotive software community, and previously developed code for companies in the Germany automotive and defense contracting industries as well as Nortel and Ford Motor Company.