Exascale Computing Project (ECP)–funded researchers have demonstrated a massively parallel, scalable system for simulating physical behaviors of materials undergoing complex topological changes, collisions, and large deformations using multi-GPU supercomputers such as Summit at Oak Ridge National Laboratory.
A team of researchers funded by the Exascale Computing Project demonstrated the efficacy of combining DRAM and high-density, byte-addressable nonvolatile memory (NVM) for accelerating high-performance computing (HPC) and enabling large problems at reasonable performance beyond the capabilities of DRAM and NVM alone.
Scientists funded by the Exascale Computing Project (ECP) have developed WarpX, a modern, performance-portable Particle-in-Cell code that describes the relativistic physics of charged plasma particles for accelerator and laser–plasma physics.
Researchers funded by the Exascale Computing Project have delivered a novel method that addresses overloaded communication processes that use MPI-IO by adding a second I/O request aggregation layer.
Scientists working on the VeloC-SZ project have optimized SZ, an error-bounded prediction-based lossy compression model.
Researchers supported by the Exascale Computing Project have developed a Multiscale Modelling Framework (MMF) configuration of E3SM, which involves embedding a limited-area cloud resolving model into each column of the global E3SM model.
Researchers working with the Exascale Computing Project (ECP) have demonstrated a novel moment invariant pattern detection algorithm to drive in situ pattern recognition during simulations running on high-performance computing architectures.
Researchers working under the Exascale Computing Project have shown that JuliaChem, a quantum chemistry software written with the Julia programming language, can deliver effective performance, superior ease of use, and improved scalability of calculations compared to the most commonly used applications.
Researchers working with the Exascale Computing Project’s Center for Efficient Exascale Discretization recently published findings of investigations they conducted into performance trade-offs for compute-intensive kernels in scientific computing applications.
Researchers funded by the Exascale Computing Project have developed a new algorithm that improves kernel ridge regression methods used for supervised learning problems.