HPC
Projects with this topic
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Projet réalisé dans le cadre de l'UE - Calculs parallèles et Programmation GPU, visant le retraitement des données de DNAse-seq produites par Gomez-Cabrero et al. en 2019 (https://doi.org/10.1038/s41597-019-0202-7) avec une approche de calcul parallèle sous serveur HPC.
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Projet réalisé dans le cadre de l'UE - Calculs parallèles et Programmation GPU, visant le retraitement des données de DNAse-seq produites par Gomez-Cabrero et al. en 2019 (https://doi.org/10.1038/s41597-019-0202-7) avec une approche snakemake sous serveur HPC.
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A flexible package manager that supports multiple versions, configurations, platforms, and compilers.
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Tamaas is a C++ library with a Python interface to efficiently solve contact mechanics problems with periodic rough surfaces, plasticity, adhesion and friction.
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Developers, Build your backends in record time! Deploy at scale with serverless technology. Self-host, install on any cloud or use our hosted platform. Open source, low code.
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Flux Reconstruction Educational Implementation
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sstack is a tool to install multiple software stacks, such as Spack, EasyBuild, and Conda. These stacks are then linked together, using lmod module files, to easily integrate with most HPC environments.
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Apache-Spark with Master-Slave setup to work out of the box using OpenHPC and Slurm
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SC-MD: A Shift/Collapse (SC) Hybrid MPI/OpenMP/GPU/MIC Molecular Dynamics Code for Many-Body Potentials
SC-MD is a fast and highly scalable molecular dynamics (MD) simulator specialized for many-body potential on large-scale computing platforms.
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This is the Companion material for our research paper for the ISC High Performance 2022.
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j-hpn-ssh: A development fork of the Pittsburgh Super-computing Center's High Performance SSH implementation.
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Configuration files for MAX IV HPC scientific software installation
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Documentation for high performance computing (HPC) clusters at UT. https://majid682008.gitlab.io/hpc-doc/
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How to scale up Analog Ensemble on HPC. https://weiming-hu.github.io/AnalogsEnsemble/
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This repo presents performance comparisons between a serial implementation, a MPI based and a Spark based implementation of a document clustering algorithm
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