Nvidia is introducing new software tools at the ISC conference in Hamburg to speed up AI for scientific research. The tools include the NVIDIA DAQIRI library, NVIDIA ALCHEMI NIM microservices, and the NVIDIA cuPhoton reference code. These tools are part of the NVIDIA CUDA-X suite, designed to deliver significantly higher performance across various application domains. The software aims to transform tasks that once took hours or days on CPUs into real-time, GPU-accelerated pipelines. The performance improvements have tangible impacts, enabling scientists to generate data and insights more quickly than ever before. For instance, cuPhoton, running on NVIDIA GB200 NVL72 systems, accelerates FITS data processing from observatories and telescopes.
In early access, cuPhoton achieved a 14,900x speedup in loading and reading FITS images from the Rubin Observatory’s Legacy Survey of Space and Time (LSST). It also enabled up to 8,400x faster signal processing and analysis using 32 NVIDIA Grace Blackwell superchips. This means faster insights from the LSST camera, the largest digital camera ever built, which captures images of billions of far-away galaxies and faint objects. NVIDIA cuPhoton is a reference code for scientists to extract insights from multidimensional data collected from telescopes, X-rays, and laser experiments. It is built to load, process, analyze, and visualize petabytes of data and can be used alongside other NVIDIA CUDA-X technologies to build end-to-end accelerated pipelines for work in astrophysics and astronomy. Researchers at Princeton University collaborated with NVIDIA to develop cuPhoton and will use it, along with Harvard University, for processing and analyzing massive data collected from observatories and dark energy surveys.
NVIDIA DAQIRI is a high-performance networking library that streams data from fast detectors and sensors into NVIDIA software. Older systems are tied to fixed hardware and can drop data when instruments produce it faster than they can save it. DAQIRI keeps up by handling the stream as it arrives. A research project called A-GHOST was developed by scientists from CERN, the University of Chicago, and University College London, in the framework of CERN openlab. It uses DAQIRI to run AI in real time on collision data recorded by the ATLAS Experiment at CERN. A-GHOST analyses data that would normally be rejected by ATLAS — over 99% of it, due to storage constraints — allowing it to catch potentially interesting signals that would otherwise be lost. NVIDIA ALCHEMI comprises a collection of domain-specific microservices and a toolkit for accelerating chemical and materials discovery, with applications across battery materials, catalysts, OLED displays, beauty products, and more.
NVIDIA released in March two ALCHEMI NIM microservices for batched geometry relaxation (BGR) and batched molecular dynamics (BMD). These AI-accelerated tools let researchers simulate millions of molecules and materials at once: BGR to find their most stable structures, BMD to simulate how they move over time. In addition, ALCHEMI is expected to soon include a microservice for the widely used Vienna Ab initio Simulation Package (VASP), enabling researchers to run materials simulations with higher GPU throughput. By running multiple VASP calculations on a single GPU with the NVIDIA Multi-Process Service, the microservice achieves a 3x speedup for geometry optimization — the process of finding the most stable arrangement of atoms in a material. Plus, developers and researchers can use the ALCHEMI Toolkit to accelerate training of AI surrogate models called machine learning interatomic potentials and easily build custom, high-performance atomistic simulation workflows.
Source: nvidia
