A team of researchers from Oak Ridge National Laboratory 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++.
A new method was developed for the discovery of fundamental descriptors for gas adsorption through deep learning neural network (DNN) approach. This approach has great potential to identify structural parameters for gas adsorption.
A team from Oak Ridge and Los Alamos National Laboratories led a demonstration of quantum key distribution systems that harness the power of quantum mechanics to authenticate data and encrypt messages with a secret ”key” to securely transmit “locked” in
Researchers at ORNL have developed new solvers for implicit time discretization of a simplified Boltzmann-Poisson system.