26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

Slipstream Multiprocessors

The main goal of parallelization is speed up. As the number of processors increases, there is a little or no speedup, since a performance threshold is reached for a fixed problem size. This is because scalability for a parallel program is limited by the communication and synchronization overhead. Slipstream multiprocessor runs two redundant copies of the same program in a chip multiprocessor, where one program runs ahead of the other. The leading program is called the A-Stream and the trailing one is the R-Stream. The A-Stream, which runs ahead, is used to reduce overhead and improve the efficiency of execution rather than to increase concurrency. Prefetching of shared data by the A-Stream for the R-Stream helps the whole unit to speedup. AR-Synchronization limits the movement of the leading program (A-Stream). The user can choose the type of synchronization desired at the beginning of program execution. A program might benefit if we vary the synchronization mode with different regions of code. This thesis proposes a technique to make the synchronization mode dynamic. This thesis implements a method to check whether it is possible to change synchronization method dynamically. This method incurs some overhead which we avoid when we implement the sliding-window method to eliminate aggressive switching. Using dynamic AR-Synchronization with the sliding- window approach results in a 7.9 % improvement in execution time for the OCEAN benchmark, a 10 % improvement for SOR, and a 0.2 % improvement for MG. Implementation of dynamic synchronization fo

High Performance Computing (HPC) Resources for Parallel Simulations and Data Analysis: NSG and HPAC

Need free, easy access to HPC resources to run widely used neural simulators or connectome analysis tools? Interested in new, free HPC tools developed by the HBP, including hardware, simulation and analytics software?The Neuroscience Gateway (NSG) project and the High Performance Analytics and Computing (HPAC) of the Human Brain Project will host a joint satellite workshop at the Society for Neuroscience (SfN) 2018 annual meeting in San Diego California. Workshop presenters are neuroscientists who are involved in computational neuroscience research and education as well as developers of tools (such as NEST, NEURON) used in computational neuroscience

The Open EEGLAB portal

International audienceThe EEGLAB signal processing environment is a widely used open source software environment for processing electroencephalographic (EEG) data. The Neuroscience Gateway (nsgportal.org) is a software portal allowing users to readily run a variety of neuroimaging software on high performance computing (HPC) resources. We have expanded the current Neuroscience Gateway (NSG) services to enable researchers to freely run EEGLAB processing scripts and pipelines on their EEG or related data via the Neuroscience Gateway. This Open EEGLAB Portal is open to all for use in nonprofit projects and allows researchers to submit unimodal or multimodal EEG data for parallel processing using standard or custom EEGLAB processing pipelines. A detailed user tutorial is available (sccn.ucsd.edu/wiki/EEGLAB_on_NSG). As a proof of concept, we apply an EEGLAB pipeline to freely available 128-channel EEG data from 1,097 participants in the Child Mind Institute Healthy Brain Network project (childmind.org/center/healthy-brain-network)

NEMAR: an open access data, tools and compute resource operating on neuroelectromagnetic data

To preserve scientific data created by publicly and/or philanthropically funded research projects and to make it ready for exploitation using recent and ongoing advances in advanced and large-scale computational modeling methods, publicly available data must use in common, now-evolving standards for formatting, identifying and annotating should share data. The OpenNeuro.org archive, built first as a repository for magnetic resonance imaging data based on the Brain Imaging Data Structure formatting standards, aims to house and share all types of human neuroimaging data. Here, we present NEMAR.org, a web gateway to OpenNeuro data for human neuroelectromagnetic data. NEMAR allows users to search through, visually explore and assess the quality of shared electroencephalography (EEG), magnetoencephalography and intracranial EEG data and then to directly process selected data using high-performance computing resources of the San Diego Supercomputer Center via the Neuroscience Gateway (nsgportal.org, NSG), a freely available web portal to high-performance computing serving a variety of neuroscientific analysis environments and tools. Combined, OpenNeuro, NEMAR and NSG form an efficient, integrated data, tools and compute resource for human neuroimaging data analysis and meta-analysis. Database URL: https://nemar.org

Neuroscience Gateway – An Overview

The Neuroscience Gateway (NSG http://www.nsgportal.org), a NSF funded project, catalyzes computational neuroscience research by lowering or eliminating the <br>administrative and technical barriers that can make it difficult for <br>neuroscience researchers to access supercomputer resources for large <br>scale simulations and brain image data processing. It provides free and <br>open access to supercomputers using time acquired via the peer reviewed allocation process managed by the Extreme Science and Engineering Discovery Environment (XSEDE). <br><br>It has about 400 registered users. Total core hours used, per-user rate of usage, and the number of users have all been growing at a rapid rate. Given current annual usage and the rate at which it has risen over the past 4 years, we expect NSG users to need about 10,000,000 core hours in 2017. <br><br>NSG is enabling participation by the wider neuroscience community in <br>research that would otherwise involve too great a computational burden, <br>such as large scale and detailed models of cells and networks, parameter <br>optimization, brain image processing, connectome pipelines etc., <br>resulting in over 50 publications and posters to date. <br><br>Many neuroscientists who are developing new network modeling tools, data <br>driven parameter optimization pipelines (such as the BluePyOpt from the <br>Human Brain Project) etc. are using the NSG to disseminate their results <br>to the neuroscience community. <br><br>NSG's scope has been expanded to offer programmatic access to <br>supercomputing resources in addition to access via the web portal. <br>Developing and operating the NSG has given us a unique opportunity to <br>understand and analyze how a very diverse range of neuroscientists are <br>using an environment like the NSG, and examine their growing need for <br>supercomputer power, as well as associated issues and needs for <br>collaboration, data sharing/management and various forms of computing

The open EEGLAB portal Interface: High-Performance computing with EEGLAB

EEGLAB signal processing environment is currently the leading open-source software for processing electroencephalographic (EEG) data. The Neuroscience Gateway (NSG, nsgportal.org) is a web and API-based portal allowing users to easily run a variety of neuroscience-related software on high-performance computing (HPC) resources in the U.S. XSEDE network. We have reported recently (Delorme et al., 2019) on the Open EEGLAB Portal expansion of the free NSG services to allow the neuroscience community to build and run MATLAB pipelines using the EEGLAB tool environment. We are now releasing an EEGLAB plug-in, nsgportal, that interfaces EEGLAB with NSG directly from within EEGLAB running on MATLAB on any personal lab computer. The plug-in features a flexible MATLAB graphical user interface (GUI) that allows users to easily submit, interact with, and manage NSG jobs, and to retrieve and examine their results. Command line nsgportal tools supporting these GUI functionalities allow EEGLAB users and plug-in tool developers to build largely automated functions and workflows that include optional NSG job submission and processing. Here we present details on nsgportal implementation and documentation, provide user tutorials on example applications, and show sample test results comparing computation times using HPC versus laptop processing

Open Source Brain: A Collaborative Resource for Visualizing, Analyzing, Simulating, and Developing Standardized Models of Neurons and Circuits

International audienceComputational models are powerful tools for exploring the properties of complex biological systems. In neuroscience, data-driven models of neural circuits that span multiple scales are increasingly being used to understand brain function in health and disease. But their adoption and reuse has been limited by the specialist knowledge required to evaluate and use them. To address this, we have developed Open Source Brain, a platform for sharing, viewing, analyzing, and simulating standardized models from different brain regions and species. Model structure and parameters can be automatically visualized and their dynamical properties explored through browser-based simulations. Infrastructure and tools for collaborative interaction, development, and testing are also provided. We demonstrate how existing components can be reused by constructing new models of inhibition-stabilized cortical networks that match recent experimental results. These features of Open Source Brain improve the accessibility, transparency, and reproducibility of models and facilitate their reuse by the wider community