Workshop on High Performance Computing Resources for Neuroscience
High Performance Computing Resources for Parallel Simulations and Data Analysis: NSG and HPAC Saturday, Nov. 3 from 9 AM to noon at downtown San Diego location. SUMMARY This workshop is intended for neuroscientists who need to use high performance computing (HPC) resources for computationally intensive tasks such as large modeling projects or analysis of neuroimaging data. It will examine resources offered by the Neuroscience Gateway Portal (NSG https://www.nsgportal.org/) and The Human Brain Project's High Performance Analytics and Computing Platform (HPAC https://hbp-hpc-platform.fz-juelich.de/). The workshop will combine hands-on instruction on how to use NSG with didactic presentations by NSG and HPAC developers and discussions with experienced users of these resources. The registration deadline for this workshop is Friday Oct. 12, but you should sign up early because space is limited. See https://neuron.yale.edu/neuron/static/courses/nsg2018/nsg2018.html for more information and a link to the registration form. OVERVIEW OF NSG AND HPAC NSG eliminates most administrative and technical barriers, providing free CPU time to users, and easy access to widely used software that currently includes BluePyOpt, Brian, CARLsim, DynaSim, EEGLAB, Freesurfer, GENESIS, Human Neocortical Neuroserver (HNN), Large Scale Neural Modeling Simulator (LSNM), MATLAB, MOOSE, NEST, NetPyNE, NEURON, Octave, PyNN, Python, R, TensorFlow, and the Virtual Personalized Multimodal Connectome Pipeline. NSG's web-based interface simplifies the tasks of uploading models or data, specifying job parameters, monitoring job status, and storing and retrieving output data. HPAC provides extensive HPC support for the HBP community. This includes developing and providing the hardware and software infrastructure required for large scale simulations, data management and analysis, visualization, and managing the complex workflows involved in performing these tasks.
participants (1)
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Ted Carnevale