Nicolas Renaud
Kernel Tuner
Kernel Tuner greatly simplifies the development of highly-optimized and auto-tuned CUDA, OpenCL, and C code, supporting many advanced use-cases and optimization strategies that speed up the auto-tuning process.
- Big data
- GPU
- High performance computing
- + 3
- Python
DeepRank GNN
DeepRank-GNN is the graph neural network of our DeepRank package. DeepRank GNN allows to train graph neural networks to classify protein-protein interface
- Machine learning
- Protein-Protein Interaction
- Python
DeepRank
Deep learning framework for data mining protein-protein interactions using CNN
- Big data
- Machine learning
- Optimized data handling
- Python
- R
- C
- + 2
iScore
A framework and predictor based on support vector machine and random walk graph kernel for scoring protein-protein interfaces.
- Big data
- Machine learning
- Protein-Protein Interaction
- Python
- Cuda
- Makefile
- + 1
pdb2sql
Fast and versatile Python package that leverages SQL queries to parse, manipulate and process biomolecular structure files. The structure files should be in the PDB format and are available on www.rcsb.org.
- Big data
- Optimized data handling
- Python
- TeX
- Makefile
PSSMGen
Generates consistent PSSM and PDB files for protein-protein complexes
- Big data
- High performance computing
- Optimized data handling
- Python
- Makefile
nanomesh
Python workflow tool for generating meshes from 2D and 3D microscopy image data
- Image processing
- Visualization
- Workflow technologies
- Jupyter Notebook
- Python
- Shell
- + 1
MPET
This software is designed to run simulations of batteries with porous electrodes using porous electrode theory, which is a volume-averaged, multiscale approach to capture the coupled behavior of electrolyte and active material within electrodes
- Python
- Dockerfile
CHAMP-EU
The Cornell-Holland Ab-initio Materials Package (CHAMP) is a quantum Monte Carlo suite of programs for electronic structure calculations. The code is developed by Claudia Filippi and Saverio Moroni, with significant contributions by Ravindra Shinde, N. Renaud, V. Azizi, E. Landinez, and S. Shepard.
- Chemistry
- High performance computing
- Physics
- + 1
- C
- Fortran
- Nemerle
- + 16
Swan
Swan is a Python package to create statistical models to predict molecular properties
- Machine learning
- Python
- Jupyter Notebook
HPGEM
hpGEM is a C++ partial differential equation solver. It is intended for Discontinuous Galerkin Finite Element Methods, but can also do normal (conforming) Finite Element Methods and Finite Volume.
- Finite Element
- High performance computing
- C++
- CMake
- TeX
- + 6
QMCBlip
QMCBlip allows to couple Quantum Monte Carlo Simulations with Machine Learning Force Fields to accelerate Molecular Dynamics simulations
- Python