Superneurons is a brand new deep learning framework built for HPC. It is written in C++ and the codes are easy to modify to work for major HPC libraries. The first release is a mere demonstration of framework architecture.

As a graduate student, I'm no longer able to maintain the code, and I decided to invest much of my time on Neural Architecture Search in hoping to build an AI that builds AI. However, DeepSpeed should provide a great alternative, and being compatiable to PyTorch and other frameworks.

please configure config.osx or config.linux. Make a 'build' dir

mkdir build
cmake ..
make -j8

please download cifar10 and mnist dataset, use the util convert_mnist and convert_cifar to prepare the dataset.

specify the path in texting\cifar10.cpp and change the path.

make again, and run the binaries at build/testing/cifar10

The testing folder has a variety of networks.

Chief Architect: Linnan Wang (Brown University)

Developers: Jinmian Ye (UESTC) and Yiyang Zhao (WPI)

For for information, please contact [email protected]. We're also looking for people to collaborate on this project, please feel free to email me if you're interested.

Please cite Superneurons in your publications if it helps your research:

Wang, Linnan, et al. "Superneurons: dynamic GPU memory management for training deep neural networks." Proceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. ACM, 2018.

Wang, Linnan, Wei Wu, Yiyang Zhao, Junyu Zhang, Hang Liu, George Bosilca, Jack Dongarra, Maurice Herlihy, and Rodrigo Fonseca. "SuperNeurons: FFT-based Gradient Sparsification in the Distributed Training of Deep Neural Networks." arXiv preprint arXiv:1811.08596 (2018).