Evaluation of model scalability in parallel neural simulators
2005 (English)Conference paper (Refereed)
A long standing belief in neuroscience has been that the brain and specifically the neocortex obtains its computational power by massive parallelism. Albeit conceptually appealing, this notion that effective processing requires large networks has not been possible to test in detailed simulations. In one project, we intend to study the generation of theta activity in the entorhinal-hippocampal system. Several simulation studies indicate that frequency and synchronization of the oscillation generated may depend on density of connectivity and/or geometry of connections. In a second project, we are studying how a model of early visual processing scales towards realistic sizes. To effectively evaluate the model, it must be scaled up to sizes where processing demands from the input given are sufficiently high, and where network size is made sufficiently large to process this information.
We have in preliminary studies tested two parallel simulators. One is a version of pGENESIS supporting MPI from University of Sunderland, UK. The other is Split, a software produced in our own laboratory. Both have been tested on an Itanium2 cluster. Tests include variable number of processors and scaling number of neurons/compartments or number of synapses. In these simulations, average spike frequency in the network is also varied. The aim is to identify main bottle-necks. For instance, we foresee the need to parallelize the construction/layout of synapses.
Place, publisher, year, edition, pages
Computer and Information Science
IdentifiersURN: urn:nbn:se:kth:diva-59235OAI: oai:DiVA.org:kth-59235DiVA: diva2:475488
QC 201201132012-01-102012-01-102012-01-13Bibliographically approved