What is CUDA and How Does it Work?


CUDA is a parallel computing platform and programming model invented by NVIDIA. It enables dramatic increases in computing performance by harnessing the power of the graphics processing unit (GPU).

“CUDA is a computing architecture designed to facilitate the development of parallel programs. In particular, CUDA provides parallel programmers with a small set of extensions to standard programming languages (e.g., C, C++) that resemble familiar programming concepts like functions, loops, and recursion. Key to this approach is a software layer called the CUDA Runtime API (or Driver API), which allows programmers to launch kernels written in these extensions directly on an attached graphics processing unit (GPU) without having to write explicit communications or manage complex execution configurations.”

The mission of the CUDA architecture is to enable dramatic increases in computing performance by harnessing the power of the GPU. This technology enables machines to speed up applications through parallelism, which will ultimately improve our lives in many ways.

It’s free! Go get it!

CUDA is a parallel computing platform and programming model invented by NVIDIA. It enables dramatic increases in computing performance by harnessing the power of the graphics processing unit (GPU).

With millions of CUDA-enabled GPUs sold to date, software developers, scientists and researchers are finding broad-ranging uses for GPU computing with CUDA. Some examples include:

Accelerating applications in scientific computing, healthcare and life sciences, financial services, and digital media.

Simulating real-world events — from hurricane tracking to global climate change modeling.

Creating new effects in the latest blockbuster movies.

Developing new products in manufacturing industries like automotive design.

CUDA was the first unified computing architecture to allow general purpose programming with a C-like language on the GPU. In order to use CUDA, you must have a GPU card installed. The GPU is typically a huge amount of smaller processors that can perform calculations in parallel. This allows CUDA to run up to thousands of threads concurrently.

This is perfect for coding situations where there are many iterations that must be made with only slight changes between each iteration. A good example of this would be in image processing, when we need to apply some filter to each pixel in the image.

If we have an image that is 800 x 600 pixels, that means we have 480,000 pixels in total. If we simply iterated over each pixel and ran our algorithm on each pixel, it would take quite a long time to complete! However, if we make these into 480,000 threads and run them in parallel, it will take much less time to finish all the iterations.

CUDA is a parallel computing platform and application programming interface (API) model created by Nvidia. It allows software developers and software engineers to use a CUDA-enabled graphics processing unit (GPU) for general purpose processing – an approach termed GPGPU (General-Purpose computing on Graphics Processing Units). The CUDA platform is a software layer that gives direct access to the GPU’s virtual instruction set and parallel computational elements, for the execution of compute kernels.

The CUDA platform is designed to work with programming languages such as C, C++, and Fortran. The CUDA platform is portable; it works not only across various brands of GPUs but also across various systems that have an x86 architecture with an OS running a Linux, Windows, or Mac OSX operating system.

CUDA is the parallel computing platform and programming model developed by Nvidia for general computing on its own GPUs (graphics processing units). CUDA enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.

The no. of CUDA cores in a GPU directly determines its processing power, but with an increasing number of cores, it becomes harder to fit all of them onto a single chip. This is where Pascal architecture comes in handy with its revolutionary 3D FinFET transistor technology that allows packing more transistors into less space while reducing power consumption.

CUDA provides a relatively low-level interface to the GPU hardware that gives you complete control over what’s happening on the hardware, but it can also be used to accelerate large classes of applications without having to worry about low-level details.

CUDA has two important characteristics:

1) It is a C/C++ based programming language that allows general purpose programming on GPUs;

2) It consists of a series of extensions to industry-standard programming languages that enable a straightforward implementation of parallel algorithms.

CUDA, the Compute Unified Device Architecture, is a parallel computing platform and application programming interface (API) model created by Nvidia. It allows software developers and software engineers to use a CUDA-enabled graphics processing unit (GPU) for general purpose processing — an approach termed GPGPU (General-Purpose computing on Graphics Processing Units). The CUDA platform is a software layer that gives direct access to the GPU’s virtual instruction set and parallel computational elements, for the execution of compute kernels. This access allows programmers to leverage the large number of cores available on high end GPUs.

The CUDA platform is designed to work with programming languages such as C, C++, and Fortran. Third party wrappers are also available for Python and other high-level languages.The platform has become popular in the HPC community[citation needed] and many application areas have emerged since its introduction in 2006. The United States Department of Energy selected CUDA as a platform for pre-exascale supercomputing.[1]

CUDA was developed with several design goals in mind:

* Provide a small set of extensions to standard programming languages, like C, that enable a straightforward implementation of parallel algorithms;

* Support both data-parallel and task-par

CUDA is a framework that allows us to execute arbitrary code on the GPU. While CUDA has the potential to be a much more general purpose platform, most of the time we will use it in the context of running existing libraries on the GPU. In this section, we are going to back up and discuss what it means for code to run on a GPU, what types of APIs exist, and why CUDA is designed the way that it is.

What does it mean for code to run on a GPU?

First let’s talk about what it means for code to run on a GPU and contrast that with traditional CPUs.

A CPU consists of several components. It has registers where data can be stored temporarily while the CPU executes instructions. Registers are extremely fast and can be accessed in a few nanoseconds (billionths of a second). It has an arithmetic logic unit (ALU) where computation can happen with numbers in registers or memory. It has caches, which are typically hierarchical in nature with faster and smaller caches closer to the processor. Finally, there is memory (RAM) which is much slower than registers or caches but can store much more information.

Usually when we think about data structure organization, we think about how values map into memory (


Leave a Reply

Your email address will not be published.