A research team from ORNL and Pacific Northwest National Laboratory has developed a deep variational framework to learn an approximate posterior for uncertainty quantification.
A team of researchers from Oak Ridge National Laboratory (ORNL) designed, implemented, and evaluated a high-performance computing (HPC) runtime system.
Researchers from Oak Ridge National Laboratory and the University of Central Florida have extended an evolutionary approach for training spiking neural networks.
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).
Researchers developed a novel algorithm for resilient and communication-efficient parallel matrix multiplication in HPC systems.
Achievement: Developed analysis tools for analyzing system logs from Titan, Jaguar and Eos systems from OLCF to extract characteristics of interest and created fault catalogue.
Significance and Impact: Understanding the characteristics of system failures