Our research projects vary in size, scope, and duration, but they share a focus on developing tools and methods that help LLNL deliver on its missions to the nation and, more broadly, advance the state of the art in scientific HPC. Projects are organized here in three ways: Active projects are those currently funded and regularly updated. Legacy projects are no longer actively developed. The A-Z option sorts all projects alphabetically, both active and legacy.
PERM
PERM is a 'C' library for persistent heap management and is intended for use with a dynamic-memory allocator (e.g. malloc, free).
Phase-Field Modeling
Based on a discretization and time-stepping algorithm, these equations include a local order parameter, a quaternion representation of local orientation, and species composition.
Preparing Codes for Exascale
LLNL's Advanced Simulation Computing program formed the Advanced Architecture and Portability Specialists team to help LLNL code teams identify and implement optimal porting strategies.
P^nMPI
PnMPI is a thin, low-overhead wrapper library that is automatically generated from mpi.h file and that can be linked by default.
Qbox
LLNL’s version of Qbox, a first-principles molecular dynamics code, will let researchers accurately calculate bigger systems on supercomputers.
Quantum Molecular Dynamics
A new algorithm for use with first-principles molecular dynamics codes enables the number of atoms simulated to be proportional to the number of processors available.
Sapphire
Drawing from data mining, image and video processing, statistics, and pattern recognition, these computational tools improve the way scientists extract useful information from data.
SOAR
SOAR (Stateless, One-pass Adaptive Refinement) is a view-dependent mesh refinement and rendering algorithm.
Sphinx
Sphinx, an integrated parallel microbenchmark suite, consists of a harness for running performance tests and extensive tests of MPI, Pthreads and OpenMP.
Veritas
Veritas provides a method for validating proxy applications to ensure that they capture the intended characteristics of their parents.
VPC
Researchers develop innovative data representations and algorithms to provide faster, more efficient ways to preserve information encoded in data.