How NVIDIA Uses Julia

Physical AI systems must understand the real world before they can act within it. Robots, autonomous vehicles, and smart spaces need to understand what’s…

NVIDIA CUDA Tile (cuTile) is a tile-based programming model that enables developers to write GPU kernels in terms of tile-level operations—loads, stores…

NVIDIA CUDA Tile is one of the most significant additions to NVIDIA CUDA programming and unlocks automatic access to tensor cores and other specialized hardware.

The article discusses NVIDIA cuTENSOR 2. 0, highlighting its applications, performance improvements, and usage from Python and Julia.

cuTENSOR 2. 0 is an advanced CUDA math library designed to accelerate tensor computations, offering optimized implementations for dense, multi-dimensional arrays.

The article discusses the security of Jupyter environments and introduces jupysec, a JupyterLab extension developed by the NVIDIA AI Red Team to assess vulnerabilities in Jupyter setups.

The article discusses FLAME GPU, an open-source software designed for fast, large-scale agent-based simulations on NVIDIA GPUs.

The article discusses the introduction of new and updated High-Performance Computing (HPC) containers in the NVIDIA NGC catalog.

The article discusses the advancements in NVIDIA's HPC Container Maker (HPCCM) and how it simplifies the process of building High Performance Computing (HPC) containers.

Julia is a high-level programming language designed for mathematical computing, combining ease of use with performance comparable to C.

This article provides a comprehensive overview of deep learning, focusing on its historical development and training methodologies.

This article provides an intuitive introduction to deep learning, focusing on core concepts such as machine learning, feature engineering, and deep learning architectures.

Mocha. jl is a deep learning library for Julia, designed for scientific and numerical computing.