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

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Presentation transcript:

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 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) cannot possible 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

Neuron simulation p1, p2, p3,... Feature extraction Fit to data / select best candidates generation iterate until best candidates converge Optimization Workflow 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. Werner Van Geit

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

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

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) Status & Roadmap