Monsteer is a library for Interactive Supercomputing in the neuroscience domain. Monsteer facilitates the coupling of running simulations (currently NEST) with interactive visualization and analysis applications. Monsteer supports streaming of simulation data to clients (currenty only spikes) as well as control of the simulator from the clients (also kown as computational steering). Monsteer's main components are a C++ library, a MUSIC-based application and Python helpers.

Monsteer provides the following functionality:

• A brion::SpikeReportPlugin for streaming spike data using ZeroEQ. The plugin accepts URIs with the format "monsteer://[host[:port]]".
• A MUSIC application called music_proxy to be used as the runtime gateway to simulators that support MUSIC, e.g. NEST.
• A small Python library to interface the Simulator in the client side and MUSIC proxy on the simulator side. This library also activates the Brion plugin when imported.

The directory examples/nest contains two simple examples using NEST. For each example there is Python script (the simulator code) and a MUSIC configuration file. The music configuration files have instructions on how to run them. The first example, (files minimal.py and nest2music_proxy.music), is a minimal simulation with streaming enabled. The second example (minimal_steered.py and nest2music_proxy_with_steering.music) is an extended version of the first one that adds basic steering support.

This package does not provide any client code example at the moment. As a streaming client you can use the spikeConverter tool from Brion as a reference.

git clone https://github.com/BlueBrain/Monsteer.git
mkdir Monsteer/build
cd Monsteer/build
cmake ..
make

The following bugs were known at release time. Please file a Bug Report if you find any other issue with this release

The API documentation can be found on bluebrain.github.io.

The following platforms and build environments are tested:

• Linux: Ubuntu 14.04, RHEL 6.5 (Makefile, x64)

Contact: ahmet.bilgili@epfl.ch jafet.villafrancadiaz@epfl.ch jhernando@fi.upm.es