Your NVIDIA Systems Just Got Faster
NVIDIA introduces over 60 updates to its CUDA-X libraries, tools and technologies.
By Ankit Patel, Sr. Director of Developer Product Marketing at NVIDIA
NVIDIA AI is not something you can download. It is not one package. Yet almost everything our company builds touches and contributes to AI.
As NVIDIA founder and CEO Jensen Huang said during his opening GTC keynote, developers use AI to achieve groundbreaking science, solve the world's most complex problems, and revolutionize industries.
That work is packaged into software development kits, libraries, frameworks and tools. SDKs help developers reach new markets and achieve their life’s work.
NVIDIA CUDA libraries are at the heart of accelerated computing.
That’s why NVIDIA unveiled over 60 updates to its CUDA-X portfolio. NVIDIA CUDA-X, built on top of CUDA, is a collection of libraries, tools, and technologies that deliver dramatically higher performance compared to CPU-only alternatives across multiple application domains, from AI to HPC.
Developers can tap into the power of accelerated computing for new science, new applications, and new industries.
The span of these updates touches areas including quantum computing and 6G research,
logistics optimization, robotics, cybersecurity, genomics, drug discovery, data analytics, and more.
And it’s no coincidence that we introduce most of our CUDA-X updates at GTC. At its core, GTC is a conference for developers. It brings them together to learn about the latest technology developments, so they have what they need to solve their most pressing computing challenges.
Get Access Now
NVIDIA’s Developer Program provides developers in all industries access to SDKs and developer tools, year-round training, early access programs, developer forums, and unlimited access to NVIDIA On-Demand, a library of resources from past GTCs and other industry events from around the world.
For those new to CUDA, NVIDIA’s parallel computing platform and programming model, “How CUDA Programming Works” provides a look at how hardware design motivates the CUDA language and how the CUDA language motivates hardware design. Students will get the fundamental background they need. For experienced developers, this session will prepare you to face your next optimization problem with a new perspective on what might work and why.
To learn more about how to deploy CUDA and CUDA-X libraries and tools, or to check out other GTC sessions, such as “CUDA: New Features and Beyond,” register for free for GTC.
