Chris Rose
GPUs can be used for much more than graphics processing. As opposed to a CPU, which can only run four or five threads at once, a GPU is made up of hundreds or even thousands of individual, low-powered cores, allowing it to perform thousands of concurrent operations. Because of this, GPUs can tackle large, complex problems on a much shorter time scale than CPUs. Dive into parallel programming on NVIDIA hardware with CUDA Succinctly by Chris Rose, and learn the basics of unlocking your graphics card.
Introduction
Creating a CUDA Project
Architecture
First Kernels
Porting from C++
Shared Memory
Blocking with Shared Memory
NVIDIA Visual Profiler (NVVP)
Nsight
CUDA Libraries
Conclusion
More Information
978-1-64200-027-6
December 31, 2014
119
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