README.md

15-418 Final Project

Manish Nagireddy (mnagired) and Ziad Khattab (zkhattab)

Please take a look at our Final Report and our Video Summary!

Title

An Exploration of Parallelism in Neural Networks

Summary

For our 15-418 final project, we are looking into potential axes of parallelism that exist within neural networks. We will be implementing neural networks in Python (via PyTorch and mpi4py, an MPI package for Python) as well as potentially also via MPI in C++ and measuring their performance on CPUs as well as GPUs.

Installation / Usage

You will need the following installed (preferably in a virtual environment using Python 3.6):

numpy==1.19.0
torch==1.10.2
torchvision==0.11.3
mpi4py

Additionally, you will need OpenMP and OpenMPI installed.

To run the Python code, simply navigate to the code directory and do:

python baseline.py --lr learning_rate --nepochs num_epochs --batch_size batch_size
python cnn.py --lr learning_rate --nepochs num_epochs --batch_size batch_size
mpirun -np num_procs python cnn_mpi.py --lr learning_rate --nepochs num_epochs --batch_size batch_size
mpirun -np num_procs python cnn_resnet_mpi.py --lr learning_rate --nepochs num_epochs --batch_size batch_size

For the C++ code:

make train && out/train
make test && out/test