Efficient spike communication in the MUSIC multi-simulation framework
© Brocke and Djurfeldt; licensee BioMed Central Ltd. 2011
Published: 18 July 2011
MUSIC is a standard API and software library allowing large-scale neuronal network simulators, or other applications, to exchange data within a parallel computer during runtime . It promotes interoperability between models written for different simulators and allows models to be re-used to build a larger model system, a multi-simulation. In addition, it allows for independent development of pre- and post-processing tools, for example for scientific visualization. A prototype implementation of the API in the form of a C++ library was released under the GPL license in early 2009. The simulators NEST, Neuron and MOOSE are adapted for the MUSIC library. Work in progress includes extension of MUSIC to support multi-scale multi-simulations  and connecting MUSIC-enabled applications to robotic hardware .
In the MUSIC prototype implementation, spikes are communicated using pair-wise MPI_Send/MPI_Recv. While this scales well for one-to-one-like connectivity, there are cases where such a communication scheme might not use machine resources optimally, e.g. for all-to-all-like connectivity in a simulation running on a large number of MPI processes. Here we report on recent developments of the library providing an alternative communication algorithm based on collective MPI communication. We present benchmark results comparing the scaling of the two communication algorithms for different topologies of connectivity.
We describe two communication algorithms used in MUSIC and give a quantitative evaluation of their scaling performance. Preliminary results indicate that the new allgather-based algorithm scales better than the pair-wise algorithm for all-to-all connectivity. It also performs better than the pair-wise algorithm on the Cray XE6 with more than 100 processors.
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