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A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.
3,716
companies
Technology
is any of
CUDA
Company | Country | Industry | Employees | Revenue | Technologies |
---|---|---|---|---|---|
United States | Computer Hardware Manufacturing | 32K | $27B | ![]() CUDA | |
United States | Defense And Space Manufacturing | 89K | $35B | ![]() CUDA | |
Australia | Higher Education | 6.2K |
| ![]() CUDA | |
United States | Medical Equipment Manufacturing | 33 |
| ![]() CUDA | |
United States | Defense And Space Manufacturing | 111K | $66B | ![]() CUDA | |
| ![]() CUDA | ||||
France | Transportation Equipment Manufacturing | 115K | $22B | ![]() CUDA | |
Germany | Retail Apparel And Fashion | 22 |
| ![]() CUDA | |
United States | Oil And Gas | 41 |
| ![]() CUDA | |
Canada | Manufacturing | 1.2K | $332M | ![]() CUDA | |
United States | Software Development | 51 | $2.7M | ![]() CUDA | |
Canada | Software Development | 5K | $11M | ![]() CUDA |
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We have data on 3,716 companies that use CUDA. Our CUDA customers list is available for download and comes enriched with vital company specifics, including industry classification, organizational size, geographical location, funding rounds, and revenue figures, among others.
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Technoloy Usage Stadistics and Market Share
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CUDA is used in 51 countries
Frequently asked questions
Our data is sourced from job postings collected from millions of companies. We monitor these postings on company websites, job boards, and other recruitment platforms. Analyzing job postings provides a reliable method to understand the technologies companies are employing, including their use of internal tools.
We refresh our data daily to ensure you are accessing the most current information available. This frequent updating process guarantees that our insights and intelligence reflect the latest developments and trends within the industry.
CUDA, which stands for Compute Unified Device Architecture, is a parallel computing platform and programming model developed by NVIDIA. It allows software developers to harness the power of NVIDIA GPUs for general-purpose processing, particularly in tasks requiring high computational power. By offloading intensive parallel computation tasks from the CPU to the GPU, CUDA enables significant acceleration of applications across various domains, including scientific simulations, deep learning, image processing, and more.
CUDA falls under the category of Machine Learning Tools due to its crucial role in accelerating machine learning algorithms using GPU parallelism. Machine learning models often involve complex computations that can benefit greatly from the parallel processing capabilities of GPUs. CUDA provides the infrastructure and tools necessary to exploit GPU acceleration for training and inference tasks in machine learning, making it a popular choice among data scientists and machine learning engineers.
CUDA was founded by NVIDIA Corporation in 2006 with the primary goal of enabling developers to leverage the power of GPU computing for general-purpose applications. The initial motivation behind CUDA was to address the increasing demand for high-performance computing solutions that could handle complex parallel workloads efficiently. As a pioneer in GPU technology, NVIDIA saw an opportunity to revolutionize parallel computing by providing a platform that could unlock the massive parallel processing capabilities of GPUs for a broader range of applications.
Currently, CUDA holds a dominant position in the market for GPU computing platforms, with a significant market share within the Machine Learning Tools category. As the demand for accelerated computing continues to rise, fueled by the growth of artificial intelligence and big data analytics, CUDA is poised to maintain its market dominance and even expand its reach further. The forecast suggests that CUDA is likely to see continuous growth in the future, driven by the increasing adoption of GPU acceleration in diverse computing applications.
You can access an updated list of companies using CUDA by visiting TheirStack.com. Our platform provides a comprehensive database of companies utilizing various technologies and internal tools.
As of now, we have data on 3,716 companies that use CUDA.
CUDA is used by a diverse range of organizations across various industries, including "Computer Hardware Manufacturing", "Defense And Space Manufacturing", "Higher Education", "Medical Equipment Manufacturing", "Defense And Space Manufacturing", "Transportation Equipment Manufacturing", "Retail Apparel And Fashion", "Oil And Gas", "Manufacturing". For a comprehensive list of all industries utilizing CUDA, please visit TheirStack.com.
Some of the companies that use CUDA include NVIDIA, Northrop Grumman, Swinburne University of Technology, DeepSight Technology, Lockheed Martin, Tenica, VALEO, Livario GmbH, Sinopec Tech Houston, Evertz Microsystems Limited and many more. You can find a complete list of 3,716 companies that use CUDA on TheirStack.com.
Based on our data, CUDA is most popular in United States (1,280 companies), United Kingdom (215 companies), Germany (132 companies), France (101 companies), Canada (97 companies), India (56 companies), Spain (55 companies), Netherlands (42 companies), Australia (27 companies), Singapore (24 companies). However, it is used by companies all over the world.
You can find companies using CUDA by searching for it on TheirStack.com, We track job postings from millions of companies and use them to discover what technologies and internal tools they are using.
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