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.
Table of Contents
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
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