A curated list of awesome open source workflow engines

Graphormer is a general-purpose deep learning backbone for molecular modeling.

Concurrent Adaptive Sampling (CAS) Algorithm Code

Benchmark for sparse linear algebra libraries for CPU and GPU platforms.

Sparse matrix computation library for GPU

A GPU algorithm for sparse matrix-matrix multiplication

A repository of update in molecular dynamics field by recent progress in machine learning and deep learning.

[DEPRECATED] Moved to ROCm/rocm-systems repo

Large Model Support in Tensorflow

(discontinued) GPFS Current Activity Monitor using Python with ncurses

Lustre Monitoring Tools

Magpie contains a number of scripts for running Big Data software in HPC environments, including Hadoop and Spark. There is support for Lustre, Slurm, Moab, Torque. LSF, Flux, and more.

A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch

Make huge neural nets fit in memory

Ongoing research training transformer models at scale

BlazingSQL is a lightweight, GPU accelerated, SQL engine for Python. Built on RAPIDS cuDF.

Python 3 Bindings for NVML library. Get NVIDIA GPU status inside your program.

Quickly setup RabbitMQ, an ELK stack, and Grafana to start collecting and visualizing data from Pegasus workflow runs.

All things RCT-related

Benchmarking Deep Learning operations on different hardware

Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology

SPECFEM3D Cartesian simulates acoustic (fluid), elastic (solid), coupled acoustic/elastic, or poroelastic seismic wave propagation in any type of conforming mesh of hexahedra (structured & unstruct…

A plugin that allows the use of Tensorflow in Hoomd-Blue for GPU-accelerated ML+MD

NVIDIA cuML: GPU-Accelerated Machine Learning

Discussion of general issues related to the project.

Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.

user-friendly and scalably execute adaptive sampling molecular dynamics on HPCs

Novel Coronavirus (COVID-19) Cases, provided by JHU CSSE

Deep-Learning Driven Adaptive Molecular Simulations for Protein Folding

user-friendly and scalably execute adaptive sampling molecular dynamics on HPCs