Featured Publication Summaries

All Featured Publication Summaries

Team delivers review of mixed-precision numerical linear algebra algorithms for exascale computing

Researchers supported by the Exascale Computing Project (ECP) conducted the first comprehensive review of research examining the usefulness of mixed-precision algorithms to power exascale computations.

First effort to couple nonlinear gyrokinetic codes advances fusion whole-device simulations

This work extends the capabilities of exascale computing to fusion research and establishes the validity and scalability of the code-coupling approach for whole-device simulations.

Urban planning simulation tools examine impact of new development

A research team funded in part by the Exascale Computing Project has produced tools for assimilating high-resolution urban terrain into regional weather models, determining building-by-building meteorology, and for extremely fast generation of building simulations for energy use analysis.

ADEPT introduced to improve large-scale bioinformatics data analysis

Researchers have introduced ADEPT, a novel domain-independent parallelization strategy that optimizes the Smith-Waterman algorithm for DNA and protein sequencing on the heterogeneous architectures and GPUs of petascale. supercomputers.

ECP-funded research develops solutions for additive manufacturing simulation needs

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.

ECP-funded team investigates NVM techniques to improve data storage and performance speed

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.

ECP-funded research advances plasma accelerator modeling

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.

ECP-funded researchers enable faster time-to-science with novel I/O processing method

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.

ECP project optimizes lossy compression methods to manage big science data volumes

Scientists working on the VeloC-SZ project have optimized SZ, an error-bounded prediction-based lossy compression model.

New model seeks to make cloud–atmospheric process simulations faster, more cost-efficient

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.

ALPINE project tests novel algorithm for in situ exascale data analysis

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.

ECP-funded scientists examine Julia’s usefulness for quantum chemistry software development

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.

CEED project examines scalability and performance of key algorithms for HPC 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.

Novel algorithm improves supervised learning training methods

Researchers funded by the Exascale Computing Project have developed a new algorithm that improves kernel ridge regression methods used for supervised learning problems.

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