Page Rank on SpiNNaker

Master's Thesis project at Imperial College London investigating alternative applications for the SpiNNaker neuromorphic hardware. The aim is to demonstrate that the machine offers better scaling opportunities for stateful and massively- parallel algorithms, such as Page Rank.

This repository constitutes the main contribution of the project, with a simulation framework to seamlessly run Page Rank computations on SpiNNaker.

Presentation slides available here.

Project structure

.
├── docker             // Tools as Docker images
├── python             // Source root
│   └── page_rank      // Python namespacing
│       ├── examples   // User code
│       ├── model      // Framework implementation
│       │   ├── Makefile          // Builds project 
│       │   ├── c_models          // C - Execution on SpiNNaker
│       │   ├── python_models     // Python - Neuron model specification
│       │   ├── requirements.txt
│       │   └── tools             // Python - High-level Page Rank specification 
│       └── tests      // Testing framework code
└── scripts            // Docker / runners utilities 

Tooling w/ Docker images

Environment management is complex for this project so the following Docker images were built to ease development w/ SpiNNaker:

• louisleblin/pynn8: user install of sPyNNaker8 - to run spiking neural network simulations.
• louisleblin/toolchain:v4.0.0: developer install of the version 4.0.0 of the development toolchain - to develop the Page Rank framework.
• louisleblin/toolchain:v2016.001: same w/ version 2016.001 - to run older tutorials based on this version.

These three images are available under the two releases, accessible via a specific tag:

• prod version, via <img> for the slim release of the image
• dev version, via <img>:[<tag>-]dev which adds some development tools such as vim and loads a custom zsh / oh-my-zsh shell - see docker/common/Dockerfile-dev for further details.

Usage

The toolchain v4.0.0 w/ the directory of examples mounted can be started as follows:

./scripts/start_container louisleblin/toolchain:v4.0.0-dev --volume $PWD/python:/app/w

Consider modifying ./scripts/start_container on:

• network config to interface with SpiNNaker
• X11 to display graphics from the container over SSH

Developing

Consider sourcing ./scripts/set_env in your .<shell>rc to add the scripts to your PATH.

Page Rank on SpiNNaker

Page Rank Model (python/page_rank/model)

We adapt the existing SpiNNakerManchester/sPyNNaker spiking neural network framework to support Page Rank simulations. Neurons become vertices, synapses are now message dispatchers that propagate rank updates along edges. The model is constituted of:

• c_models: the C implementation of the model, which runs on SpiNNaker.
• python_models: the Python specification of a Page Rank simulation.
• tools/simulation.py: a unique interface that exposes a user-friendly PageRankSimulation class that does all the heavy lifting.

Examples (python/page_rank/examples)

To run an python/page_rank/examples/<example_name>.py, use:

# Starts the toolchain container in interactive mode
./scripts/start_container \
    louisleblin/toolchain:v4.0.0-dev \
    --volume $PWD/python:/app/w

...

# In the container
cd page_rank/examples
make <example_name>

or as a one-liner:

./scripts/start_container \
    louisleblin/toolchain:v4.0.0-dev \
    --volume $PWD/python:/app/w --rm \
    --exec "make -C page_rank/examples <example_name>"

Minimal example w/ simple_4_vertices

Command:

./scripts/start_container \
    louisleblin/toolchain:v4.0.0-dev \
    --volume $PWD/python:/app/w --rm \
    --exec "make -C page_rank/examples simple_4_vertices --show-in --show-out"

The following caption shows how the toolchain:v4.0.0-dev can be used to compute Page Rank on a simple graph as described in this video. First, the input graph is displayed to visually confirm its structure. Then, Page Rank is computed on SpiNNaker and an output graph is displayed, showing the evolution over time of the rank of each node.

Additionally, a Python implementation of Page Rank runs on the same graph and is used to ensure the results obtained are correct.