Whether they're looking at nanoscale electron behaviors or starry galaxies colliding millions of light years away, many scientists share a common challenge - they must comb through petabytes of data to extract insights that can advance their fields.
With the NVIDIA cuPyNumeric accelerated computing library, researchers can now take their data-crunching Python code and effortlessly run it on CPU-based laptops and GPU-accelerated workstations, cloud servers or massive supercomputers. The faster they can work through their data, the quicker they can make decisions about promising data points, trends worth investigating and adjustments to their experiments.
To make the leap to accelerated computing, researchers don't need expertise in computer science. They can simply write code using the familiar NumPy interface or apply cuPyNumeric to existing code, following best practices for performance and scalability.
Once cuPyNumeric is applied, they can run their code on one or thousands of GPUs with zero code changes.
The latest version of cuPyNumeric, now available on Conda and GitHub, offers support for the NVIDIA GH200 Grace Hopper Superchip, automatic resource configuration at run time and improved memory scaling. It also supports HDF5, a popular file format in the scientific community that helps efficiently manage large, complex data.
Researchers at the SLAC National Accelerator Laboratory, Los Alamos National Laboratory, Australia National University, UMass Boston, the Center for Turbulence Research at Stanford University and the National Payments Corporation of India are among those who have integrated cuPyNumeric to achieve significant improvements in their data analysis workflows.
Less Is More: Limitless GPU Scalability Without Code Changes Python is the most common programming language for data science, machine learning and numerical computing, used by millions of researchers in scientific fields including astronomy, drug discovery, materials science and nuclear physics. Tens of thousands of packages on GitHub depend on the NumPy math and matrix library, which had over 300 million downloads last month. All of these applications could benefit from accelerated computing with cuPyNumeric.
Many of these scientists build programs that use NumPy and run on a single CPU-only node - limiting the throughput of their algorithms to crunch through increasingly large datasets collected by instruments like electron microscopes, particle colliders and radio telescopes.
cuPyNumeric helps researchers keep pace with the growing size and complexity of their datasets by providing a drop-in replacement for NumPy that can scale to thousands of GPUs. cuPyNumeric doesn't require code changes when scaling from a single GPU to a whole supercomputer. This makes it easy for researchers to run their analyses on accelerated computing systems of any size.
Solving the Big Data Problem, Accelerating Scientific Discovery Researchers at SLAC National Accelerator Laboratory, a U.S. Department of Energy lab operated by Stanford University, have found that cuPyNumeric helps them speed up X-ray experiments conducted at the Linac Coherent Light Source.
A SLAC team focused on materials science discovery for semiconductors found that cuPyNumeric accelerated its data analysis application by 6x, decreasing run time from minutes to seconds. This speedup allows the team to run important analyses in parallel when conducting experiments at this highly specialized facility.
By using experiment hours more efficiently, the team anticipates it will be able to discover new material properties, share results and publish work more quickly.
Other institutions using cuPyNumeric include:
Australia National University, where researchers used cuPyNumeric to scale the Levenberg-Marquardt optimization algorithm to run on multi-GPU systems at the country's National Computational Infrastructure. While the algorithm can be used for many applications, the researchers' initial target is large-scale climate and weather models.
Los Alamos National Laboratory, where researchers are applying cuPyNumeric to accelerate data science, computational science and machine learning algorithms. cuPyNumeric will provide them with additional tools to effectively use the recently launched Venado supercomputer, which features over 2,500 NVIDIA GH200 Grace Hopper Superchips.
Stanford University's Center for Turbulence Research, where researchers are developing Python-based computational fluid dynamics solvers that can run at scale on large accelerated computing clusters using cuPyNumeric. These solvers can seamlessly integrate large collections of fluid simulations with popular machine learning libraries like PyTorch, enabling complex applications including online training and reinforcement learning.
UMass Boston, where a research team is accelerating linear algebra calculations to analyze microscopy videos and determine the energy dissipated by active materials. The team used cuPyNumeric to decompose a matrix of 16 million rows and 4,000 columns.
National Payments Corporation of India, the organization behind a real-time digital payment system used by around 250 million Indians daily and expanding globally. NPCI uses complex matrix calculations to track transaction paths between payers and payees. With current methods, it takes about 5 hours to process data for a one-week transaction window on CPU systems. A trial showed that applying cuPyNumeric to accelerate the calculations on multi-node NVIDIA DGX systems could speed up matrix multiplication by 50x, enabling NPCI to process larger transaction windows in less than an hour and detect suspected money laundering in near real time.
To learn more about cuPyNumeric, see a live demo in the NVIDIA booth at the Supercomputing 2024 conference in Atlanta, join the theater talk in the expo
More from Nvidia
16/09/2025
The U.K. is driving investments in sovereign AI, using the technology to advance...
13/09/2025
Celtic languages - including Cornish, Irish, Scottish Gaelic and Welsh - are the U.K.'s oldest living languages. To empower their speakers, the UK-LLM sover...
10/09/2025
GeForce NOW Blackwell RTX 5080-class SuperPODs are now rolling out, unlocking a new level of ultra high-performance, cinematic cloud gaming.
GeForce NOW Ultima...
09/09/2025
Inference has emerged as the new frontier of complexity in AI. Modern models are...
09/09/2025
As large language models (LLMs) grow larger, they get smarter, with open models from leading developers now featuring hundreds of billions of parameters. At the...
09/09/2025
At this week's AI Infrastructure Summit in Silicon Valley, NVIDIA's VP o...
09/09/2025
Inference performance is critical, as it directly influences the economics of an AI factory. The higher the throughput of AI factory infrastructure, the more to...
09/09/2025
At this week's IAA Mobility conference in Munich, NVIDIA Vice President of A...
09/09/2025
ComfyUI - an open-source, node-based graphical interface for running and buildin...
04/09/2025
NVIDIA today announced new AI education support for K-12 programs at a White House event to celebrate public-private partnerships that advance artificial intell...
04/09/2025
Editor's note: This post is part of the AI On blog series, which explores the latest techniques and real-world applications of agentic AI, chatbots and copi...
04/09/2025
NVIDIA Blackwell RTX is coming to the cloud on Wednesday, Sept. 10 - an upgrade ...
03/09/2025
3D artists are constantly prototyping.
In traditional workflows, modelers must build placeholder, low-fidelity assets to populate 3D scenes, tinkering and adju...
02/09/2025
For more than a century, meteorologists have chased storms with chalkboards, equ...
28/08/2025
Brace yourself, COGs - the Locusts aren't the only thing rising up. The Coal...
28/08/2025
Last week at Gamescom, NVIDIA announced the winners of the NVIDIA and ModDB RTX ...
27/08/2025
AI models are advancing at a rapid rate and scale.
But what might they lack that (most) humans don't? Common sense: an understanding, developed through rea...
25/08/2025
Robots around the world are about to get a lot smarter as physical AI developers...
25/08/2025
As autonomous vehicle systems rapidly grow in complexity, equipped with reasonin...
22/08/2025
As the latest member of the NVIDIA Blackwell architecture family, the NVIDIA Blackwell Ultra GPU builds on core innovations to accelerate training and AI reason...
22/08/2025
AI reasoning, inference and networking will be top of mind for attendees of next...
21/08/2025
Japan is once again building a landmark high-performance computing system - not ...
21/08/2025
From AI assistants doing deep research to autonomous vehicles making split-second navigation decisions, AI adoption is exploding across industries.
Behind ever...
21/08/2025
Across the globe, AI factories are rising - massive new data centers built not to serve up web pages or email, but to train and deploy intelligence itself. Inte...
21/08/2025
Get a glimpse into the future of gaming.
The NVIDIA Blackwell RTX architecture is coming to GeForce NOW in September, marking the service's biggest upgrade...
20/08/2025
Editor's note: This blog is a part of Into the Omniverse, a series focused o...
18/08/2025
With over 175 games now supporting NVIDIA DLSS 4 - a suite of advanced, AI-power...
18/08/2025
At Gamescom, NVIDIA is releasing its first major update to Project G Assist - an...
15/08/2025
Of around 7,000 languages in the world, a tiny fraction are supported by AI lang...
14/08/2025
NVIDIA is partnering with the U.S. National Science Foundation (NSF) to create a...
14/08/2025
Warhammer 40,000: Dawn of War - Definitive Edition is marching onto GeForce NOW,...
13/08/2025
Black Forest Labs' FLUX.1 Kontext [dev] image editing model is now available as an NVIDIA NIM microservice.
FLUX.1 models allow users to edit existing imag...
11/08/2025
Using NVIDIA digital twin technologies, Amazon Devices & Services is powering bi...
11/08/2025
Packing the power of the NVIDIA Blackwell architecture in compact, energy-effici...
11/08/2025
Physical AI is becoming the foundation of smart cities, facilities and industria...
07/08/2025
This GFN Thursday brings an offer members can't refuse - 2K's highly ant...
05/08/2025
Two new open-weight AI reasoning models from OpenAI released today bring cutting...
05/08/2025
In collaboration with OpenAI, NVIDIA has optimized the company's new open-so...
05/08/2025
NVIDIA and OpenAI began pushing the boundaries of AI with the launch of NVIDIA D...
05/08/2025
NVIDIA GPUs are at the heart of modern computing. They're used across industries - from healthcare and finance to scientific research, autonomous systems an...
31/07/2025
August brings new levels of gaming excitement on GeForce NOW, with 2,300 titles now available to stream in the cloud.
Grab a controller and get ready for epic ...
31/07/2025
Interest in generative AI is continuing to grow, as new models include more capabilities. With the latest advancements, even enthusiasts without a developer bac...
29/07/2025
FourCastNet3 (FCN3) is the latest AI global weather forecasting system from NVID...
28/07/2025
The electrical grid is designed to support loads that are relatively steady, such as lighting, household appliances, and industrial machines that operate at con...
24/07/2025
For media company Black Mixture, AI isn't just a tool - it's an entire p...
24/07/2025
Sharpen the blade and brace for a journey steeped in myth and mystery. WUCHANG: Fallen Feathers has launched in the cloud.
Ride in style with skateboarding leg...
23/07/2025
In today's fast-evolving digital landscape, marketing teams face increasing ...
22/07/2025
Editor's note: This post is part of the AI On blog series, which explores th...
17/07/2025
Listen up citizens, the law is back and patrolling the cloud. Nacon's RoboCop Rogue City - Unfinished Business launches today in the cloud, bringing justice...
Whether they're looking at nanoscale electron behaviors or starry galaxies colliding millions of light years away, many scientists share a common challenge - they must comb through petabytes of data to extract insights that can advance their fields.
