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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
कंपनियाँ
हमारे पास 3,657 कंपनियों के डेटा हैं जो CUDA का उपयोग करती हैं। यह क्यूरेटेड सूची डाउनलोड के लिए उपलब्ध है और इसमें उद्योग वर्गीकरण, संगठन का आकार, भौगोलिक स्थान, फंडिंग राउंड्स, और राजस्व आंकड़ों सहित महत्वपूर्ण कंपनी विवरण शामिल हैं।
कंपनी | देश | उद्योग | कर्मचारी | राजस्व |
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NVIDIA | Computer Hardware Manufacturing | 32K | $27B | |
Oski Technology | Computer Hardware Manufacturing | 27K | $27B | |
Northrop Grumman | Defense And Space Manufacturing | 88K | $35B | |
Swinburne University of Technology | Higher Education | 6.2K | ||
DeepSight Technology | Medical Equipment Manufacturing | 33 | ||
Lockheed Martin | Defense And Space Manufacturing | 111K | $66B | |
Valeo | Motor Vehicle Parts Manufacturing | 115K | $22B | |
Livario GmbH | Retail Apparel And Fashion | 22 | ||
Sinopec Tech Houston | Oil And Gas | 41 | ||
Evertz Microsystems Limited | Manufacturing | 1.2K | $332M | |
Zendar | Software Development | 40 | $2.7M | |
CentML | Software Development | 5K | $11M |
क्या आप पूरी सूची डाउनलोड करना चाहते हैं?
साइन अप करें और 3,657 कंपनियों की पूरी सूची डाउनलोड करें
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टेक्नोलॉजी उपयोग सांख्यिकी और बाजार हिस्सेदारी
आप इस डेटा को अपनी आवश्यकताओं के अनुसार फिल्टर करके अनुकूलित कर सकते हैं, जैसे भूगोल, उद्योग, कंपनी का आकार, राजस्व, तकनीक का उपयोग, नौकरी पद और अधिक। आप डेटा को Excel या CSV प्रारूप में डाउनलोड कर सकते हैं।
आप इस डेटा के लिए अलर्ट प्राप्त कर सकते हैं। आप उस तकनीक का चयन करके शुरुआत कर सकते हैं जिसमें आप रुचि रखते हैं और फिर आपको अपने इनबॉक्स में अलर्ट प्राप्त होंगे जब नई कंपनियां उस तकनीक का उपयोग कर रही होंगी।
आप Theirstack के डेटा को एक Excel फ़ाइल में निर्यात कर सकते हैं, जिसे आपके CRM में आयात किया जा सकता है। आप डेटा को एक API में भी निर्यात कर सकते हैं।
CUDA का उपयोग 51 देशों में किया जाता है
76 विकल्प CUDA के हैं
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अक्सर पूछे जाने वाले सवाल
हमारा डेटा लाखों कंपनियों से एकत्रित नौकरी पोस्टिंग से प्राप्त होता है। हम इन पोस्टिंग को कंपनी वेबसाइटों, नौकरी बोर्डों, और अन्य भर्ती प्लेटफार्मों पर निगरानी करते हैं। नौकरी पोस्टिंग का विश्लेषण करने से कंपनियों द्वारा उपयोग की जा रही तकनीकों को समझने के लिए एक विश्वसनीय विधि मिलती है, जिसमें उनके आंतरिक उपकरणों का उपयोग भी शामिल है।
हम अपने डेटा को दैनिक रूप से ताज़ा करते हैं ताकि यह सुनिश्चित किया जा सके कि आप सबसे अद्यतित जानकारी तक पहुँच रहे हैं। यह बार-बार अद्यतन प्रक्रिया यह गारंटी देती है कि हमारे अंतर्दृष्टि और बुद्धिमत्ता उद्योग के नवीनतम विकास और रुझानों को दर्शाती हैं।
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.
आप TheirStack.com पर जाकर CUDA का उपयोग करने वाली कंपनियों की अद्यतन सूची प्राप्त कर सकते हैं। हमारा प्लेटफ़ॉर्म विभिन्न तकनीकों और आंतरिक उपकरणों का उपयोग करने वाली कंपनियों का एक व्यापक डेटाबेस प्रदान करता है।
अभी तक, हमारे पास 3,657 कंपनियों के बारे में डेटा है जो CUDA का उपयोग करती हैं।
CUDA का उपयोग विभिन्न उद्योगों सहित "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" में कई संगठनों द्वारा किया जाता है। CUDA का उपयोग करने वाले सभी उद्योगों की विस्तृत सूची के लिए, कृपया TheirStack.com पर जाएं।
कुछ कंपनियाँ जो CUDA का उपयोग करती हैं, उनमें NVIDIA, Oski Technology, Northrop Grumman, Swinburne University of Technology, DeepSight Technology, Lockheed Martin, Valeo, Livario GmbH, Sinopec Tech Houston, Evertz Microsystems Limited और कई अन्य शामिल हैं। आप TheirStack.com पर CUDA का उपयोग करने वाली 3,657 कंपनियों की पूरी सूची पा सकते हैं।
हमारे डेटा के आधार पर, CUDA (1,477 companies), (230 companies), (166 companies), (120 companies), (117 companies), (68 companies), (65 companies), (43 companies), (30 companies), (29 companies) में सबसे लोकप्रिय है। हालांकि, इसे दुनिया भर की कंपनियों द्वारा उपयोग किया जाता है।
आप TheirStack.com पर CUDA का उपयोग करने वाली कंपनियों को खोजकर पा सकते हैं, हम लाखों कंपनियों की नौकरी पोस्टिंग को ट्रैक करते हैं और इसका उपयोग करके पता लगाते हैं कि वे कौन सी तकनीक और आंतरिक टूल का उपयोग कर रहे हैं।