There are several advantages that give CUDA an edge over traditional general purpose graphics processor (GPGPU) computers with graphics APIs: CUDA Advantages and Limitations Advantages
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.
Developers can still write software in C or C++ and include parallelization by using CUDA keywords. It is a platform and programming model for parallel computing, and it accelerates general-purpose computing using GPUs. Programming ModelĬUDA is not a language or an API.
AMD built the CodeXL Toolkit, which provides a full range of OpenCL programming tools. The latest NVIDIA hardware features are quickly supported in the CUDA Toolkit.ĪMD’s community activity is more limited. CUDA has a broad ecosystem of third-party tools and libraries. 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. NVIDIA is committed to the commercialization and development of the CUDA platform. OpenCL applications can run on almost any operating system, and on most types of hardware, including FPGAs and ASICs. Operating SystemsĬUDA can run on Windows, Linux, and MacOS, but only on NVIDIA hardware. This is a major trend when using OpenCL in integrated solutions. OpenCL can run on these GPUs, but while sufficient for laptops, it does not perform competitive performance for general-purpose computations.īesides GPU, you can run OpenCL code on CPU and FPGA / ASIC. Intel offers GPUs integrated into its CPUs. OpenCL is the primary language used to run graphics processing on AMD GPUs. Related content: read our in-depth guide about CUDA on NVIDIAĪMD creates Radeon GPUs for embedded solutions and mobile systems, laptops and desktops, and Radeon Instinct GPUs for servers. 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. 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).
NVIDIA currently dominates the market, holding the largest share. There are three major manufacturers of graphic accelerators: NVIDIA, AMD and Intel.
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. OpenCL programs are designed to be compiled at run time, so applications that use OpenCL can be ported between different host devices. You can also use the API to manage device memory separately from host memory. It provides an API that enables programs running on a host to load the OpenCL kernel on computing devices. OpenCL uses a programming language similar to C.
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 is used to accelerate supercomputers, cloud servers, PCs, mobile devices, and embedded platforms. Open Computing Language (OpenCL) serves as an independent, open standard for cross-platform parallel programming. With CUDA programming, developers can use the power of GPUs to parallelize calculations and speed up processing-intensive applications.įor 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.ĭevelopers 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. CUDA serves as a platform for parallel computing, as well as a programming model.ĬUDA was developed by NVIDIA for general-purpose computing on NVIDIA’s graphics processing unit (GPU) hardware.