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Found 7 papers, 0 papers with code
Connecting Large Language Model Agent to High Performance Computing Resource
The Large Language Model agent workflow enables the LLM to invoke tool functions to increase the performance on specific scientific domain questions.
BioNeMo Framework: a modular, high-performance library for AI model development in drug discovery
We introduce the BioNeMo Framework to facilitate the training of computational biology and chemistry AI models across hundreds of GPUs.
LSHBloom: Memory-efficient, Extreme-scale Document Deduplication
Deduplication is a major focus for assembling and curating training datasets for large language models (LLM) -- detecting and eliminating additional instances of the same content -- in large collections of technical documents.
Employing Artificial Intelligence to Steer Exascale Workflows with Colmena
Computational workflows are a common class of application on supercomputers, yet the loosely coupled and heterogeneous nature of workflows often fails to take full advantage of their capabilities.
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences.
Linking the Dynamic PicoProbe Analytical Electron-Optical Beam Line / Microscope to Supercomputers
The Dynamic PicoProbe at Argonne National Laboratory is undergoing upgrades that will enable it to produce up to 100s of GB of data per day.
Coupling streaming AI and HPC ensembles to achieve 100-1000x faster biomolecular simulations
The results establish DeepDriveMD as a high-performance framework for ML-driven HPC simulation scenarios, that supports diverse MD simulation and ML back-ends, and which enables new scientific insights by improving the length and time scales accessible with current computing capacity.
