An Argonne National Laboratory computer scientist finds efficiencies for extracting knowledge from a data explosion.
Los Alamos researchers develop code to distribute computation more efficiently and across increasing numbers of supercomputer processors.
A Sandia researcher applies his DOE early-career award to improving predictive reliability in high-performance computing.
A Purdue University professor is using a DOE early-career award to bridge gaps in high-performance computing languages.
The cosmological search in the dark is no walk in the park. With help from Berkeley Lab’s NERSC, Fermilab aims open-source software at data from high-energy physics.
Los Alamos experiments with a quantum-physics device that could boost computation to the next level.
Oak Ridge National Laboratory is testing a ‘deep-learning supercomputer in a box’ as it looks ahead to machines that automatically find insights in data.
Biologically inspired architectures create opportunities and obstacles.
A University of Washington DOE early-career-awardee, with techniques called verified lifting and stencil programming, aims to boost speeds in climate models and other HPC simulations.
Lawrence Livermore National Laboratory’s time-saving HPC tool eases the way for next era of scientific simulations.