GPUs are capable of much more than just rendering graphics. A GPU is made up of hundreds or even thousands of discrete, low-powered cores, allowing it to do thousands of concurrent operations, in contrast to a CPU, which can only run four or five threads at simultaneously.
As a result, GPUs are able to solve complicated issues quickly compared to CPUs. Utilize CUDA to get started with parallel programming on NVIDIA hardware. Discover the fundamentals of unlocking your graphics card as explained briefly by Chris Rose.
This book covers both CUDA 5.0 and Kepler and goes into further detail on CUDA hardware and software. Every CUDA developer, from the novice to the most experienced, will find something of interest and practical use in this article. More seasoned CUDA developers will value the in-depth discussion of issues like the driver API and context migration, as well as the advice on how to organize CPU/GPU data exchange and synchronization. Newer CUDA developers will learn how the hardware processes command and how the driver checks progress.
