How to GPU-Accelerate Model Training with CUDA-X Data Science
In previous posts on AI in manufacturing and operations, we covered the unique data challenges in the supply chain and how smart feature engineering can...
Autodesk Research Brings Warp Speed to Computational Fluid Dynamics on NVIDIA GH200
Computer-aided engineering (CAE) forms the backbone for modern product development across industries, from designing safer aircraft to optimizing renewable...
Improving GEMM Kernel Auto-Tuning Efficiency on NVIDIA GPUs with Heuristics and CUTLASS 4.2
Selecting the best possible General Matrix Multiplication (GEMM) kernel for a specific problem and hardware is a significant challenge. The performance of a...
The newest update to the CUDA Toolkit, version 13.0, features advancements to accelerate computing on the latest NVIDIA CPUs and GPUs. As a major release, it...
Advanced NVIDIA CUDA Kernel Optimization Techniques: Handwritten PTX
As accelerated computing continues to drive application performance in all areas of AI and scientific computing, there's a renewed interest in GPU optimization...
AI in Manufacturing and Operations at NVIDIA: Accelerating ML Models with NVIDIA CUDA-X Data Science
NVIDIA leverages data science and machine learning to optimize chip manufacturing and operations workflows—from wafer fabrication and circuit probing to...
Simplify Setup and Boost Data Science in the Cloud using NVIDIA CUDA-X and Coiled
Imagine analyzing millions of NYC ride-share journeys—tracking patterns across boroughs, comparing service pricing, or identifying profitable pickup...
Each year, the world recycles only around 13% of its two billion-plus tons of municipal waste. By 2050, the world's annual municipal waste will reach 3.88B...
