相关论文: HPC Extensions to the OpenKIM Processing Pipeline

Recent advancements in nanopore sequencing technology, particularly the R10 nanopore from Oxford Nanopore Technology, have necessitated the development of improved data processing methods to utilize their potential for more than 9-mer…

Quantum resources are increasingly integrated into high-performance computing (HPC) and cloud environments, but quantum high-performance computing (QHPC) software stacks remain isolated, often proprietary, full-stack solutions lacking…

Scientific computing is at the core of many High-Performance Computing applications, including computational flow dynamics. Because of the uttermost importance to simulate increasingly larger computational models, hardware acceleration is…

Computer simulations have long been key to understanding and designing phase-change materials (PCMs) for memory technologies. Machine learning is now increasingly being used to accelerate the modelling of PCMs, and yet it remains…

Hybrid quantum-high performance computing (Q-HPC) workflows are emerging as a key strategy for running quantum applications at scale in current noisy intermediate-scale quantum (NISQ) devices. These workflows must operate seamlessly across…

An application for high-performance computing (HPC) is shown that is relevant in the field of battery development. Simulations of electrolyte wetting and flow are conducted using pore network models (PNM) and the lattice Boltzmann method…

To address the growing needs for scalable High Performance Computing (HPC) and Quantum Computing (QC) integration, we present our HPC-QC full stack framework and its hybrid workload development capability with modular…

Machine learning interatomic potentials (MLIPs) enable atomistic simulations with near ab initio accuracy at significantly reduced computational cost, but their broader adoption is often limited by fragmented tooling, limited scalability,…

Due to its flexible architecture, FPGAs support unique, deep hardware pipeline implementations for accelerating HPC applications. However, these devices are quite new in the HPC space, and thus, have been scarcely explored outside some…

In this paper, we describe the optical imaging data processing pipeline developed for the Subaru Telescope's Hyper Suprime-Cam (HSC) instrument. The HSC Pipeline builds on the prototype pipeline being developed by the Large Synoptic Survey…

High-performance computing (HPC) has evolved over decades through multiple architectural transitions, from vector supercomputers to massively parallel CPU clusters and GPU-accelerated systems, continuously expanding the frontier of…

Learning exchange correlation functionals, used in quantum chemistry calculations, from data has become increasingly important in recent years, but training such a functional requires sophisticated software infrastructure. For this reason,…

Generative models for molecules have shown considerable promise for use in computational chemistry, but remain difficult to use for non-experts. For this reason, we introduce open-source infrastructure for easily building generative…

ArQTiC is an open-source, full-stack software package built for the simulations of materials on quantum computers. It currently can simulate materials that can be modeled by any Hamiltonian derived from a generic, one-dimensional,…

Data-driven (DD) interatomic potentials (IPs) trained on large collections of first principles calculations are rapidly becoming essential tools in the fields of computational materials science and chemistry for performing atomic-scale…

We present a systematic methodology, built within the Open Knowledgebase of Interatomic Models (OpenKIM) framework (https://openkim.org), for quantifying properties of grain boundaries (GBs) for arbitrary interatomic potentials (IPs), GB…

We present the VASPKIT, a command-line program that aims at providing a powerful and user-friendly interface to perform high-throughput analysis of a variety of material properties from the raw data produced by the VASP code. It consists of…

Electromagnetic simulations form an indispensable part of the design and optimization process for superconducting quantum devices. Although several commercial platforms exist, open-source alternatives optimized for high-performance…

Processing-in-memory (PIM) has shown extraordinary potential in accelerating neural networks. To evaluate the performance of PIM accelerators, we present an ISA-based simulation framework including a dedicated ISA targeting neural networks…

We develop a workflow within the ONIOM framework and demonstrate it on the hybrid computing system consisting of the supercomputer Fugaku and the Quantinuum Reimei trapped-ion quantum computer. This hybrid platform extends the layered…