A multidisciplinary team of researchers from 91°µÍø (ORNL) and other institutions created a Machine Learning (ML) library for the training of classifiers on spectrographic chemical data.
Researchers from 91°µÍø and the University of Central Florida have extended an evolutionary approach for training spiking neural networks.
The researchers from ORNL have developed a new and faster algorithm for the graph all-pair shortest-path (APSP) problem.
A team of researchers from 91°µÍø applied advanced statistical methods from biomedical research to study an unexpected failure mode of general-purpose computing on graphics processing units (GPGPUs).
Metal halide perovskites are promising materials for optoelectronic and sensing applications.
Researchers developed a novel algorithm for resilient and communication-efficient parallel matrix multiplication in HPC systems.
Researchers built a deep neural network to estimate the compressibility of scientific data.
To help expedite the use of quantum processing units, ORNL researchers developed an advanced software framework.
A team of ORNL researchers has used the DCA++ application, a popular code for predicting the performance of quantum materials, to verify two performance-enhancing strategies.
Kokkos is a programming model and library for writing performance-portable code in C++.