Peter S. Pacheco

PARALLEL NEUROSYS: A system for the simulation of very large networks of biologically accurate neurons on parallel computers

Abstract We present a software package for the simulation of very large neuronal networks on parallel computers. The package can be run on any system with an implementation of the Message Passing Interface standard. We also present some example results for a simple neuronal model in networks of up to a quarter of a million neurons. The full software package as well as usage and installation guidelines can be found in [http://cs.usfca.edu/neurosys].

The Performance of Parallel Disk Write Methods for Linux Multiprocessor Nodes

We experimentally evaluate several methods for implementing parallel computations that interleave a significant number of contiguous or strided writes to a local disk on Linux-based multiprocessor nodes. Using synthetic benchmark programs written with MPI and Pthreads, we have acquired detailed performance data for different application characteristics of programs running on dual processor nodes. In general, our results show that programs that perform a significant amount of I/O relative to pure computation benefit greatly from the use of threads, while programs that perform relatively little I/O obtain excellent results using only MPI. For a pure MPI approach, we have found that it is usually best to use two writing processes with mmap(). For Pthreads it is usually best to use two writing processes, write() for contiguous data, and writev() for strided data. Codes that use mmap() tend to benefit from periodic syncs of the data of the data to the disk, while codes that use write() or writev() tend to have better performance with few syncs. A straightforward use of ROMIO usually does not perform as well as these direct approaches for writing to the local disk.

Object-oriented neurosys: Parallel programs for simulating large networks of biologically accurate neurons

Object-oriented NeuroSys is a collection of programs for simulating very large networks of biologically accurate neurons on distributed memory parallel computers. It includes two principle programs: ooNeuroSys, a parallel program for solving the systems of ordinary differential equations arising from the modelling of large networks of interconnected neurons, and Neurondiz, a parallel program for visualizing the results of ooNeuroSys. Both programs are designed to be run on clusters and use the MPI library to obtain parallelism. ooNeuroSys also includes an easy-to-use Python interface. This interface allows neuroscientists to quickly develop and test complex neuron models. Both ooNeuroSys and Neurondiz have a design that allows for both high performance and relative ease of maintenance.