CUDA vs OpenCL

Which One is Right for Your Project?


What is CUDA?

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.

What is OpenCL?

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:

CUDA vs OpenCL: What’s the Difference?


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.

Operating Systems

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. 

Software and Community 

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.

Programming Model

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.

CUDA Advantages and Limitations


There are several advantages that give CUDA an edge over traditional general purpose graphics processor (GPGPU) computers with graphics APIs:

  • Unified memory (in CUDA 6.0 or later) and unified virtual memory (in CUDA 4.0 or later)
  • Shared memory—provides a faster area of shared memory for CUDA threads. It can be used as a caching mechanism, and provides more bandwidth than texture lookups.
  • Scattered reads: code can be read from any address in memory.
  • Improved performance on downloads and reads, which works well from the GPU and to the GPU
  • There is full support for bitwise and integer operations


  • CUDA source code is provided on host machines or GPU, as defined by C++ syntax rules. Older versions of CUDA used C syntax rules, meaning that updated CUDA source code may or may not work as expected. 
  • CUDA has one-way interoperability with rendering languages like OpenGL. OpenGL can access the CUDA registered memory, but CUDA cannot access OpenGL memory.
  • Later versions of CUDA do not provide emulators or fallback support for older versions.
  • CUDA only supports NVIDIA hardware.

OpenCL Advantages and Limitations


  • OpenCL provides abstract memory and portability, due to its run-time execution model. 
  • The OpenCL kernel can run on any supported software implementation.
  • OpenCL supports a heterogeneous system architecture that enables efficient communication between the GPU and the processor using C++ 17 atomics.


  • Developers cannot directly implement proprietary hardware technologies like inline Parallel Thread Execution (PTX) on NVIDIA GPUs without sacrificing portability. 
  • A study that directly compared CUDA programs with OpenCL on NVIDIA GPUs showed that CUDA was 30% faster than OpenCL.
  • OpenCL is rarely used for machine learning. As a result, the community is small, with few libraries and tutorials available.

Running CUDA and OpenCL at Scale with RunAI

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: 

  • Advanced visibility—create an efficient pipeline of resource sharing by pooling GPU compute resources.
  • No more bottlenecks—you can set up guaranteed quotas of GPU resources, to avoid bottlenecks and optimize billing.
  • A higher level of control—Run:AI enables you to dynamically change resource allocation, ensuring each job gets the resources it needs at any given time.

Run:AI simplifies machine learning infrastructure pipelines, helping data scientists accelerate their productivity and the quality of their models. 

Learn more about the GPU virtualization platform.