Anyone who is unfamiliar with CUDA and wants to learn it, at a beginner's level, should read this tutorial, provided they complete the pre-requisites. It can also be used by those who already know CUDA and …

Here is the architecture of a CUDA capable GPU − There are 16 streaming multiprocessors (SMs) in the above diagram. Each SM has 8 streaming processors (SPs). That is, we get a total of 128 SPs. Now, …

CUDA ? Compute Unified Device Architecture. It is an extension of C programming, an API model for parallel computing created by Nvidia. Programs written using CUDA harness the power of GPU. …

Hands-On GPU Programming with Python and CUDA is for developers and data scientists who want to learn the basics of effective GPU programming to improve performance using Python code.

The CUDA toolkit will also install the required GPU drivers, along with the required libraries and header files to develop CUDA applications. It will also install some sample code to help starters.

We have learnt how threads are organized in CUDA and how they are mapped to multi-dimensional data. Let us go ahead and use our knowledge to do matrix-multiplication using CUDA.

The CUDA toolkit will also install the required GPU drivers, along with the required libraries and header files to develop CUDA applications. It will also install some sample code to help starters.

In this chapter, we will learn about a few key concepts related to CUDA. We will understand data parallelism, the program structure of CUDA and how a CUDA C Program is executed.

Apart from the device DRAM, CUDA supports several additional types of memory that can be used to increase the CGMA ratio for a kernel. We know that accessing the DRAM is slow and expensive.