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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.
3,657
aziende
Abbiamo dati su 3,657 aziende che usano CUDA. La nostra lista di clienti CUDA è disponibile per il download ed è arricchita con specifiche vitali dell'azienda, incluse classificazione industriale, dimensioni organizzative, posizione geografica, round di finanziamenti e cifre di ricavi, tra gli altri.
Azienda | Paese | Settore | Dipendenti | Entrate |
---|---|---|---|---|
NVIDIA | Stati Uniti | Computer Hardware Manufacturing | 32K | $27B |
Oski Technology | Stati Uniti | Computer Hardware Manufacturing | 27K | $27B |
Northrop Grumman | Stati Uniti | Defense And Space Manufacturing | 88K | $35B |
Swinburne University of Technology | Australia | Higher Education | 6.2K | |
DeepSight Technology | Stati Uniti | Medical Equipment Manufacturing | 33 | |
Lockheed Martin | Stati Uniti | Defense And Space Manufacturing | 111K | $66B |
Valeo | Francia | Motor Vehicle Parts Manufacturing | 115K | $22B |
Livario GmbH | Germania | Retail Apparel And Fashion | 22 | |
Sinopec Tech Houston | Stati Uniti | Oil And Gas | 41 | |
Evertz Microsystems Limited | Canada | Manufacturing | 1.2K | $332M |
Zendar | Stati Uniti | Software Development | 40 | $2.7M |
![]() CentML | Canada | Software Development | 5K | $11M |
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CUDA è utilizzata in 51 paesi
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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.
CUDA, a parallel computing platform and application programming interface created by NVIDIA, is widely utilized by companies in the machine learning field for its exceptional performance and scalability. By harnessing the power of GPU-accelerated computing, CUDA allows organizations to expedite complex data processing tasks and train machine learning models with unparalleled efficiency.
CUDA significantly accelerates computing tasks by offloading intensive calculations to the GPU. This faster processing speed enables companies to reduce their model training times drastically, leading to quicker insights and improved productivity compared to traditional CPU-based systems.
One of CUDA's key advantages is its ability to handle massive parallel processing, allowing for simultaneous execution of multiple operations. This enhanced parallelism not only boosts overall system performance but also enables companies to tackle intricate machine learning algorithms with ease, surpassing the capabilities of other parallel computing technologies.
CUDA optimizes resource utilization by efficiently distributing workloads between the CPU and GPU. This balanced approach results in better overall system performance and cost-effectiveness, making it a preferred choice for companies looking to maximize their computing resources effectively.
Companies Using CUDA:
CUDA, developed by NVIDIA, is a parallel computing platform and application programming interface model that enables developers to use NVIDIA GPUs for general-purpose processing, including accelerating tasks related to machine learning and artificial intelligence. Several prominent companies leverage CUDA to enhance the performance of their applications and deliver cutting-edge solutions in various domains. Here are some real-world examples of companies that have successfully integrated CUDA into their operations:
NVIDIA: NVIDIA, the pioneer of CUDA technology, extensively uses it for various applications, including deep learning, image processing, and scientific simulations. NVIDIA started utilizing CUDA in 2007 and has since optimized its GPUs to support a wide range of machine learning tasks.
Adobe Systems: Adobe Systems, a leading software company, incorporates CUDA in its Adobe Creative Cloud suite for accelerating graphics processing tasks. By leveraging CUDA's parallel computing capabilities, Adobe has significantly improved the performance of applications like Adobe Photoshop for tasks such as image editing and rendering. Adobe adopted CUDA technology in 2013.
Netflix: Netflix, the renowned streaming service provider, utilizes CUDA to enhance its recommendation algorithms and video encoding processes. By harnessing CUDA's computing power, Netflix can analyze massive amounts of viewer data efficiently to personalize recommendations and optimize video streaming quality. Netflix began integrating CUDA technology into its operations in 2015.
These case studies highlight how companies like NVIDIA, Adobe Systems, and Netflix have harnessed the power of CUDA to drive innovation, improve performance, and deliver a superior user experience in the realm of machine learning tools. By leveraging CUDA's parallel processing capabilities, these companies have successfully enhanced their applications and services, showcasing the versatility and effectiveness of this technology in transforming business operations.
Puoi accedere a un elenco aggiornato di aziende che utilizzano CUDA visitando TheirStack.com. La nostra piattaforma fornisce un database completo di aziende che utilizzano varie tecnologie e strumenti interni.
Fino ad ora, abbiamo dati su 3,657 aziende che utilizzano CUDA.
CUDA è utilizzato da una vasta gamma di organizzazioni in vari settori, inclusi "Computer Hardware Manufacturing", "Computer Hardware Manufacturing", "Defense And Space Manufacturing", "Higher Education", "Medical Equipment Manufacturing", "Defense And Space Manufacturing", "Motor Vehicle Parts Manufacturing", "Retail Apparel And Fashion", "Oil And Gas", "Manufacturing". Per un elenco completo di tutti i settori che utilizzano CUDA, si prega di visitare TheirStack.com.
Alcune delle aziende che utilizzano CUDA includono NVIDIA, Oski Technology, Northrop Grumman, Swinburne University of Technology, DeepSight Technology, Lockheed Martin, Valeo, Livario GmbH, Sinopec Tech Houston, Evertz Microsystems Limited e molte altre. Puoi trovare un elenco completo di 3,657 aziende che utilizzano CUDA su TheirStack.com.
Secondo i nostri dati, CUDA è più popolare in Stati Uniti (1,477 companies), Regno Unito (230 companies), Germania (166 companies), Francia (120 companies), Canada (117 companies), Spagna (68 companies), India (65 companies), Paesi Bassi (43 companies), Singapore (30 companies), Australia (29 companies). Tuttavia, è utilizzato da aziende in tutto il mondo.
Puoi trovare aziende che utilizzano CUDA cercandolo su TheirStack.com. Tracciamo le offerte di lavoro di milioni di aziende e le utilizziamo per scoprire quali tecnologie e strumenti interni stanno utilizzando.