1 / 7

Blue Brain Project

Blue Brain Project. Carlos Osuna, Carlos Aguado, Fabien Delalondre. Outline. Blue Brain Project (BBP) Optimizer Framework: Single neuron simulation Implementation Status & models (MPI & BOINC) Future directions: Simplifying development workflow (CERN). Blue Brain Project - Modeling.

tannar
Download Presentation

Blue Brain Project

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Blue Brain Project • Carlos Osuna, Carlos Aguado, Fabien Delalondre

  2. Outline • Blue Brain Project (BBP) Optimizer Framework: Single neuron simulation • Implementation Status & models (MPI & BOINC) • Future directions: Simplifying development workflow (CERN)

  3. Blue Brain Project - Modeling • Biology & Motivation • Morphology: a exemplar morphology is used as a template. • Ion channels are added to the compartments of the morphology. • Parameters of the ions channels • (such as density per channel type) cannotpossible be measured experimentally. • Modeling & Algorithms • Single neuron simulation models neuron electrical response • Optimizer Framework: Genetic algorithm scans parameter to select best fitting candidates to data Werner Van Geit

  4. Optimization Workflow Fit to data / select best candidates Neuron simulation Feature extraction Neuron simulation executed using different input protocols (p1, p2, …) to obtain electrical activity of a single neuron Goodness of model can be evaluated by comparing certain features of electrical response with data. generation p1, p2, p3,... Werner Van Geit iterate until best candidates converge

  5. Optimizater Task Distribution p3 p2 Each set of parameters in the phase space, and each protocol is an independent neuron simulation No communication involve among slaves p1 master task submit slaves

  6. Genetic Algorithm Flow master task submit master return outcome to master slaves slaves master Evaluate features of current generation it best fit can be improved slaves

  7. Status & Roadmap • MPI/BOINC implementation • Implementation 1: Pure MPI (fast interconnect) • Implementation 2: Adding BOINC support to explore new computing models (S. Wenzel) • Cons: BOINC approach requires porting code on all volunteer platforms (windows, linux, …) Roadmap Extending Volunteer support using CERN software stack (Virtualization) Making master/slave framework generic by abstracting implementation details (BOINC/CERN/MPI)

More Related