Featured Publication Summaries

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.

Technical Highlights

HeFFTe – a widely applicable, CPU/GPU, scalable multidimensional FFT that can even support exascale supercomputers

HeFFTe (highly efficient FFTs for Exascale, pronounced “hefty”) enables multinode and GPU-based multidimensional fast Fourier transform (FFT) capabilities in single- and double-precision.

SOLLVE: OpenMP for HPC and Exascale

SOLLVE is an Exascale Computing Project (ECP) focused on defining and developing OpenMP capabilities for the high-performance computing (HPC) community.

ECP releases of the tested and verified MAGMA Numerical Linear Algebra Library provide a wealth of cross-platform capabilities for exascale supercomputing

Matrix Algebra on GPU and Multicore Architectures (MAGMA) is a well-known and performant choice for those who desire Basic Linear Algebra Subroutines (BLAS) and Linear Algebra Package (LAPACK) functionality that runs on CPUs, GPUs from various vendors, and across multiple GPUs in a single node.

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