Optimization and GPU porting of information flow implementation

Project info

Title: Optimization and GPU porting of information flow implementation

Project lead and collaborators: Etienne Combrisson & Ruggero Basanisi

Image of the project:

Registered Brainhack Global 2020 Event: BrainHack Marseille

Project Description: Frites is a recent pure Python package (https://github.com/brainets/frites) to analyse neurophysiological data within the Information Theoretical framework and to perform group-level statistics on information-based measures. The aim of the software is to extract brain networks that are modulated according to the task (model-based and model-free analysis). To this end, Frites contains several CPU-based functions to estimate the directed information flow between brain areas. While those methods estimate the directed connectivity in a reasonable amount of time on small networks, the complexity of larger networks are too demanding for the current CPU implementation. Therefore, the ultimate goal of this project would be to have both a CPU and a GPU implementations of our recent multivariate information flow measure.

Data to use: No data are needed, Frites includes functions to generate simulated data.

Link to project repository/sources: GitHub: https://github.com/brainets/frites

Goals for Brainhack Global 2020: The following points are going to be addressed in this project:

• Code a GPU implementation of the conditional mutual-information
• Include a switch for the user to specify if a CPU or a GPU implementations are going to be used when computing the univariate information flow
• GPU porting of the multivariate information flow measure (which is also going to use the GPU version of scikit-learn)

Good first issues: Translate the core functions to be executed on GPU devices Benchmark those functions against CPU implementations and decide whether it’s better in the future to have two implementations (CPU / GPU) or to have system-agnostic functions Skills: Good knowledge of python is needed. We will mostly use basic libraries used in computer science, especially numpy. No needing about specific knowledge of GPU programming.

Tools/Software/Methods to Use: Python, Jupyter, GitHub, and Nextjournal or Colab.

Project labels

• Type of project: coding_methods, data_management, documentation, #method_development, pipeline_development, tutorial_recording, visualization

• Project development status: 0_concept_no_content, 1_basic structure, #2_releases_existing

• Topic of the projet: Bayesian_approaches, #causality, connectome, data_visualisation, deep_learning, diffusion, diversity_inclusivity_equality, EEG_EventRelatedResponseModelling, EEG_source_modelling, Granger_causality, hypothesis_testing, ICA, #information_theory, machine_learning, MR_methodologies, neural_decoding, neural_encoding, neural_networks, PCA, #physiology, reinforcement_learning, reproducible_scientific_methods, single_neuron_models, statistical_modelling, systems_neuroscience, tractography, #connectivity

• Tools used in the project: AFNI, ANTs, BIDS, Brainstorm, CPAC, Datalad, DIPY, FieldTrip, fMRIPrep, Freesurfer, FSL, #Jupyter, #MNE, MRtrix, Nipype, NWB, SPM

• Tools skill level required to enter the project (more than one possible): comfortable, #expert, #familiar, no_skills_required

• Programming language used in the project: no_programming_involved, C++, containerization, documentation, Java, Julia, Matlab, #Python, R, shell_scripting, Unix_command_line, Web, workflows

• Modalities involved in the project (if any): behavioral, DWI, ECG, ECOG, EEG, eye_tracking, fMRI, fNIRS, MEG, MRI, PET, TDCS, TMS

• Git skills reuired to enter the project (more than one possible): 0_no_git_skills, #1_commit_push, #2_branches_PRs, 3_continuous_integration

• I added all of the labels I want an associate to my project

Project Submission

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• Link to your project: could be a code repository, a shared document, etc.
• Goals for Brainhack Global 2020: describe what you want to achieve during this brainhack.
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• Number of participants required.
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