Featured Publication Summaries

Ginkgo math library increases software portability across architectures

Scientists working with the Exascale Computing Project (ECP) have developed Ginkgo, a sparse linear algebra library designed to increase portability of software among heterogeneous supercomputing architectures.

Novel software design brings extensibility, sustainability to quantum chemistry supercomputing

Researchers supporting the development of NWChemEx, an open-source, exascale software platform for high-performance quantum chemistry simulations, have demonstrated a novel modular software design solution that can be extended to future architectures with minimal software engineering effort and provides a sustainable pathway to software development with kernels that can be plugged into many high-level algorithms.

WarpX gets ready for plasma physics simulations in the exascale era

A team funded by the Exascale Computing Project (ECP) have ported and scaled WarpX, a particle-in-cell (PIC) code for solving the motion of relativistic, charged particles in the presence of electromagnetic fields, to GPU-based supercomputers such as Summit and the upcoming Aurora and Frontier machines.

Technical Highlights

ExaIO: Access and Manage Storage of Data Efficiently and at Scale on Exascale Systems

As the word exascale implies, the forthcoming generation exascale supercomputer systems will deliver 1018 flop/s of scalable computing capability.

ECP Brings a Host of Hardware-Accelerated and GPU-Friendly Optimizations to the MPICH Library

Message Passing Interface (MPI) has been the communications backbone for distributed high-performance computing (HPC) scientific applications since its introduction in the 1990s.

ECP Brings Much Needed Visualization Software to Exascale and GPU-Accelerated Systems

The development of the VTK-m toolkit, a scientific visualization toolkit for emerging architectures, is a critical advancement in support of scientific visualization on exascale and GPU-accelerated systems for high-performance computing (HPC) users. VTK-m is needed because—counterintuitively—GPUs currently have software challenges when supporting large-scale scientific visualization tasks.

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