Featured Publication Summaries
All Featured Publication Summaries
The Exascale Computing Project’s (ECP’s) Co-design Center for Particle Applications (CoPA) aims to prepare particle applications for exascale computing.
A team of researchers has developed the Feature Tracking Kit (FTK), which uses simplicial spacetime meshing to simplify, scale, and deliver novel feature-tracking algorithms for in situ data analysis and scientific visualization. FTK delivers feature-tracking tools, scales feature-tracking algorithms in distributed and parallel environments, and simplifies development of new feature-tracking algorithms, enabling new analyses in greater detail than previously possible.
A team collaborating across national laboratories, universities, and industry has developed a new approach to runtime programming that enables scalable execution of implicitly parallel programs on large-scale machines using distributed dynamic dependence analysis for efficient, on-the-fly computation of dependences.
Researchers at the National Energy Technology Laboratory (NETL) and Lawrence Berkeley National Laboratory (LBNL) have developed MFIX-Exa, a massively parallel CFD-DEM (computational fluid dynamics-discrete element method) code, a high-fidelity computational tool that allows for simulating dense particle-laden flows in systems with complex geometries so high-performance computing (HPC) can be used in place of physical testing.
For the ExaWind challenge problem, decomposing the linear systems for each overset mesh offers several advantages by (1) allowing the use of rigorously coupled, separate CFD codes wherein optimal solvers can be chosen for their respective domains and (2) speeding up the linear solvers.
The analytical and computational methods developed in this work pave the way for future calculations in more complicated systems involving multiparticle and nuclear systems.
ArborX will speed up exascale applications for computational cosmology, multiphysics data transfer, computational mechanics, wind farm simulations, and other research areas.
Novel method combining machine learning and data partitioning benefits cancer records data extraction
A team of cancer researchers and computer scientists have applied machine learning (ML) ensemble techniques to reduce training time, mitigate task complexity, and improve accuracy and classification performance for information extraction with cancer pathology reports.
Scientists have demonstrated the value in two particle simulators of so-called rendezvous methods, which invoke a communication pattern useful when the processors sending and receiving information are unknown to each other.
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.
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.
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.
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.
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.