CUDA serves as a platform for parallel computing, as well as a programming model.
CUDA was developed by NVIDIA for general-purpose computing on NVIDIA’s graphics processing unit (GPU) hardware.
With CUDA programming, developers can use the power of GPUs to parallelize calculations and speed up processing-intensive applications.
For GPU-accelerated applications, the sequential parts of the workload run single-threaded on the machine’s CPU, and the compute-intensive parts run in parallel on thousands of GPU cores.
Developers can use CUDA to write programs in popular languages (C, C++, Fortran, Python, MATLAB, etc.) and add parallelism to their code with a few basic keywords.
Related content: read our guide to NVIDIA CUDA
Open Computing Language (OpenCL) serves as an independent, open standard for cross-platform parallel programming.
OpenCL is used to accelerate supercomputers, cloud servers, PCs, mobile devices, and embedded platforms.
OpenCL has dramatically improved the speed and flexibility of applications in various market categories, including professional development tools, scientific and medical software, imaging, education, and deep learning.
OpenCL uses a programming language similar to C. It provides an API that enables programs running on a host to load the OpenCL kernel on computing devices. You can also use the API to manage device memory separately from host memory.
OpenCL programs are designed to be compiled at run time, so applications that use OpenCL can be ported between different host devices.
OpenCL is not just for GPUs (like CUDA) but also for CPUs, FPGAs… In addition, OpenCL was developed by multiple companies, as opposed to NVIDIA’s CUDA.
In this article, you will learn:
There are three major manufacturers of graphic accelerators: NVIDIA, AMD and Intel.
NVIDIA currently dominates the market, holding the largest share. NVIDIA provides comprehensive computing and processing solutions for mobile graphics processors (Tegra), laptop GPUs (GeForce GT), desktops GPUs (GeForce GTX), and GPU servers (Quadro and Tesla).
This wide range of NVIDIA hardware can be used both with CUDA and OpenCL, but the performance of CUDA on NVIDIA is higher, because it was designed with NVIDIA hardware in mind.
Related content: read our in-depth guide about CUDA on NVIDIA
AMD creates Radeon GPUs for embedded solutions and mobile systems, laptops and desktops, and Radeon Instinct GPUs for servers. OpenCL is the primary language used to run graphics processing on AMD GPUs.
Intel offers GPUs integrated into its CPUs. OpenCL can run on these GPUs, but while sufficient for laptops, it does not perform competitive performance for general-purpose computations.
Besides GPU, you can run OpenCL code on CPU and FPGA / ASIC. This is a major trend when using OpenCL in integrated solutions.
CUDA can run on Windows, Linux, and MacOS, but only on NVIDIA hardware.
OpenCL applications can run on almost any operating system, and on most types of hardware, including FPGAs and ASICs.
NVIDIA is committed to the commercialization and development of the CUDA platform. NVIDIA developed tools including the CUDA Toolkit, NVIDIA Performance Primitives (NPP), and Video SDK, and Visual Profiler, and built integrations with Microsoft Visual Studio and other popular platforms. CUDA has a broad ecosystem of third-party tools and libraries. The latest NVIDIA hardware features are quickly supported in the CUDA Toolkit.
AMD’s community activity is more limited. AMD built the CodeXL Toolkit, which provides a full range of OpenCL programming tools.
CUDA is not a language or an API. It is a platform and programming model for parallel computing, and it accelerates general-purpose computing using GPUs. Developers can still write software in C or C++ and include parallelization by using CUDA keywords.
OpenCL does not enable writing code in C++, but you can work in an environment resembling the C programming language, and work directly with GPU resources.
There are several advantages that give CUDA an edge over traditional general purpose graphics processor (GPGPU) computers with graphics APIs:
Run:AI automates resource management and orchestration for machine learning infrastructure. With Run:AI, you can automatically run as many compute intensive experiments as needed, incorporating CUDA and/or OpenCL.
Here are some of the capabilities you gain when using Run:AI:
Run:AI simplifies machine learning infrastructure pipelines, helping data scientists accelerate their productivity and the quality of their models.
Learn more about the Run:AI GPU virtualization platform.