Accelerate Drug and Material Discovery with New Math Library NVIDIA cuEquivariance
AI models for science are often trained to make predictions about the workings of nature, such as predicting the structure of a biomolecule or the properties of...
Memory Efficiency, Faster Initialization, and Cost Estimation with NVIDIA Collective Communications Library 2.22
For the past few months, the NVIDIA Collective Communications Library (NCCL) developers have been working hard on a set of new library features and bug fixes....
The release supports GB100 capabilities and new library enhancements to cuBLAS, cuFFT, cuSOLVER, cuSPARSE, as well as the release of Nsight Compute 2024.3.
NVIDIA is excited to collaborate with Colfax, Together.ai, Meta, and Princeton University on their recent achievement to exploit the Hopper GPU architecture and...
Measuring the GPU Occupancy of Multi-stream Workloads
NVIDIA GPUs are becoming increasingly powerful with each new generation. This increase generally comes in two forms. Each streaming multi-processor (SM), the...
Universal Scene Description, also called OpenUSD or USD, is an open and extensible framework for creating, editing, querying, rendering, collaborating, and...
Efficient CUDA Debugging: Using NVIDIA Compute Sanitizer with NVIDIA Tools Extension and Creating Custom Tools
NVIDIA Compute Sanitizer is a powerful tool that can save you time and effort while improving the reliability and performance of your CUDA applications....
Building High-Performance Applications in the Era of Accelerated Computing
AI is augmenting high-performance computing (HPC) with novel approaches to data processing, simulation, and modeling. Because of the computational requirements...
While part 1 focused on the usage of the new NVIDIA cuTENSOR 2.0 CUDA math library, this post introduces a variety of usage modes beyond that, specifically...
cuTENSOR 2.0: A Comprehensive Guide for Accelerating Tensor Computations
NVIDIA cuTENSOR is a CUDA math library that provides optimized implementations of tensor operations where tensors are dense, multi-dimensional arrays or array...
Differentiable Slang easily integrates with existing codebases—from Python, PyTorch, and CUDA to HLSL—to aid multiple computer graphics tasks and enable...
Differentiable Slang: A Shading Language for Renderers That Learn
NVIDIA just released a SIGGRAPH Asia 2023 research paper, SLANG.D: Fast, Modular and Differentiable Shader Programming. The paper shows how a single language...
When working with embedded systems such as the Jetson modules, you must optimize your application based on your power budget and compute resources. To avoid...
