How NVIDIA Uses NumPy

NVIDIA nvmath-python is a library designed to bridge the gap between the Python scientific community and NVIDIA CUDA-X math libraries. It gives Python users…

Developers building 3D, design, simulation, robotics, and industrial digital twin applications need ways to bring physical AI capabilities into the tools and…

A massive-scale X-ray free-electron laser (XFEL) enables tracking structural and electron dynamics in novel systems, including fusion materials, semiconductors…

In a previous post, we introduced the Universal Sparse Tensor (UST), enabling developers to decouple a tensor’s sparsity from its memory layout for greater…

Physical AI—AI systems that perceive, reason, and act in physically grounded simulated environments—is changing how teams design and validate robots and…

Computer-aided engineering (CAE) is shifting from human-driven workflows toward AI-driven ones, including physics foundation models that generalize across…

The article discusses how accelerated computing, particularly through NVIDIA's technologies, is transforming scientific experiments at large research facilities like the NSF-DOE Vera C.

The article discusses how to simulate an accurate radio environment for 5G and 6G systems using the NVIDIA Aerial Omniverse Digital Twin (AODT).

The article discusses the launch of NVIDIA CUDA Tile with CUDA 13. 1, which introduces a virtual instruction set for tile-based parallel programming.

The article discusses enhancing robot perception efficiency on the NVIDIA Jetson Thor platform by utilizing specialized hardware accelerators alongside powerful GPUs.

This article discusses the integration of the Newton physics engine with NVIDIA Isaac Lab for training quadruped locomotion policies and simulating cloth manipulation.

The article discusses Autodesk Research's development of the Accelerated Lattice Boltzmann (XLB) library, which enhances computational fluid dynamics (CFD) performance using NVIDIA's Warp and GH200...

The article discusses the integration of AI-powered simulations in computer-aided engineering (CAE) to accelerate design processes.

The article discusses the significant updates in CUDA Toolkit 13.

The article discusses the advancements in single-cell analysis facilitated by RAPIDS-singlecell, an open-source tool that leverages GPU acceleration to handle large datasets efficiently.

The article discusses the enhancements in the Forest Inference Library (FIL) within NVIDIA cuML 25. 04, focusing on its capabilities for fast inference of tree-based models.

The article discusses how to accelerate Deep Learning (DL) and Large Language Model (LLM) inference using Apache Spark in cloud environments.

NVIDIA cuPyNumeric 25. 03 is a fully open-source library designed as a drop-in replacement for NumPy, leveraging the Legate framework for accelerated computing.

The article discusses advancements in Federated Learning (FL) specifically in the context of large language models (LLMs), focusing on the challenges of communication overhead and memory constraint...

NVIDIA cuML has introduced a zero code change capability that allows data scientists and machine learning engineers to accelerate scikit-learn applications on NVIDIA GPUs without modifying existing...

The article discusses how to leverage GPU acceleration for algorithmic trading simulations using Numba, highlighting the significant performance improvements achievable—over 100x faster simulations.

The article discusses how NAVER Place optimizes its small language model (SLM)-based vertical services using NVIDIA TensorRT-LLM, enhancing usability and performance.

The article discusses the importance of GPU acceleration in data science, highlighting how NVIDIA's RAPIDS suite can significantly enhance performance for data processing tasks.

The article introduces tile-based programming in Warp 1. 5. 0, highlighting new Python primitives that enhance GPU programming efficiency.

The article introduces NVIDIA cuPyNumeric, an accelerated and distributed implementation of the NumPy API that allows users to scale their NumPy programs seamlessly from laptops to supercomputers w...

The NVIDIA RAPIDS v24.

Federated learning is transforming the development of autonomous vehicles (AVs) by allowing decentralized training using locally collected data.

The article discusses the latest features and improvements in NVIDIA CUDA-Q v0. 8, an open-source programming model for hybrid quantum-classical applications.

The article discusses the importance of securing AI model files against unauthorized access and introduces the concept of canaries as a detection mechanism.

The article discusses how to build a zero-copy AI sensor processing pipeline using OpenCV within the NVIDIA Holoscan SDK.

The article discusses WholeGraph, a feature in the RAPIDS cuGraph library designed to optimize memory and retrieval for Graph Neural Networks (GNNs).

The article discusses the challenges of drug discovery and introduces NVIDIA BioNeMo's new models, MolMIM and DiffDock, which enhance molecule generation and molecular docking.

The article discusses how NVIDIA Holoscan is being utilized to accelerate ptychography workflows at the Diamond Light Source, a leading synchrotron facility.

NVIDIA's Differentiable Slang is a new shading language designed to unify real-time, inverse, and differentiable rendering, enabling seamless integration of machine learning with graphics programmi...

NVIDIA hosted a two-day training session at Black Hat USA 2023, focusing on the unique security risks associated with machine learning (ML).

The article discusses the security vulnerabilities found in machine learning research code, particularly focusing on the analysis of the Meta Kaggle for Code dataset.

The article discusses the advancements in single-cell RNA sequencing analysis using the RAPIDS-singlecell library, which leverages GPU acceleration to significantly enhance performance.

The article discusses the integration of distributed deep learning with Apache Spark 3. 4, highlighting new built-in APIs for both distributed model training and inference.

The article discusses RAPIDS RAFT, a library designed to optimize machine learning and data analytics on GPUs by providing reusable computational patterns.

The article discusses the advancements in NVIDIA cuQuantum, specifically focusing on the cuTensorNet library for approximate tensor network simulations in quantum circuit modeling.

The article discusses the latest features of NVIDIA AI Enterprise 3. 0, focusing on optimizing production AI performance and efficiency.

The article discusses the increasing computational demands for AI processing at the edge and introduces the NVIDIA Holoscan SDK v0.

The article discusses NVIDIA's leadership in MLPerf Training 2.

The article discusses NVIDIA FLARE 2. 2, an open-source platform for federated learning that introduces new features aimed at reducing development time and enhancing deployment efficiency.

NVIDIA has announced significant updates to its AI software suite, including JAX, NVIDIA CV-CUDA, and NVIDIA RAPIDS, aimed at accelerating AI research, computer vision, and data science.

The article discusses how to accelerate ETL processes on KubeFlow using RAPIDS, a data science framework that leverages GPUs for improved performance.

The article discusses the importance of multidimensional image processing for enhanced image analysis, particularly in fields like medical imaging and remote sensing.

The article discusses the advantages of using Naive Bayes (NB) classifiers for text classification tasks, particularly when leveraging GPU acceleration through RAPIDS cuML.

The article discusses the improved interoperability between NVIDIA Vision Programming Interface (VPI) and PyTorch, focusing on how VPI can enhance object detection and tracking in computer vision a...

This article discusses the implementation of Hierarchical Risk Parity (HRP) using RAPIDS to optimize portfolio allocation through machine learning techniques.