Title: Optimization and GPU porting of information flow implementation
Project lead and collaborators: Etienne Combrisson & Ruggero Basanisi
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 (https://github.com/brainets/xfrites/blob/main/xfrites/conn/conn_pca_covgc_cpu.py#L115).
Goals for Brainhack Marseille
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)
Skills:
python 100%
NumPy 70%
GPU programming 20%
Striking Image
Project submission
Submission checklist
Once the issue is submitted, please check items in this list as you add under ‘Additional project info’
[x] Link to your project: could be a code repository, a shared document, etc.
[x] Goals for the Brainhack: describe what you want to achieve during this brainhack.
[x] Skills: list skills that would be particularly suitable for your project (coding and non-coding).
Project info
Title: Optimization and GPU porting of information flow implementation
Project lead and collaborators: Etienne Combrisson & Ruggero Basanisi
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 (https://github.com/brainets/xfrites/blob/main/xfrites/conn/conn_pca_covgc_cpu.py#L115).
Goals for Brainhack Marseille The following points are going to be addressed in this project:
Skills:
Striking Image
Project submission
Submission checklist
Once the issue is submitted, please check items in this list as you add under ‘Additional project info’
[x] Link to your project: could be a code repository, a shared document, etc.
[x] Goals for the Brainhack: describe what you want to achieve during this brainhack.
[x] Skills: list skills that would be particularly suitable for your project (coding and non-coding).