Awesome
Fast-GPU-PCC
This repository contains the implementation of Fast-GPU-PCC algorithm. This algorithm is used for computing pairwise Pearson’s correlation coefficient. Based on the symmetric property of Pearson’s correlation, this approach returns N(N-1)/2 correlation coefficients located at strictly upper triangle part of the correlation matrix.
Please use the following instructions for compiling and running the code.
Compilation
Use the following command to compile the code.
nvcc -lcublas -O2 -arch=compute_35 -code=sm_35 -std=c++11 CPU_side.cpp GPU_side.cu -o exec
Running
Place the fMRI time series data in a text file called data.txt in the current directory. This file will be the input of the Fast-GPU-PCC method.
To run the code use the following command:
./exec N M W
| Argument | Description |
|---|---|
| N | Number of voxels |
| M | Length of time series |
| W | Option for writing the results into the binary or text file, possible values: {b: binary, t: text} |
The correlations will be stored in a binary file called corrs.bin or a text file called corrs.txt based on the value of parameter W.
Note
The data should be stored in the row major format (first N elements corresponds to the time series of first element, second N elements corresponds to the time series of second element and …)
Example of an input file containing 5 voxels with time series of length 3 is shown in the file data.txt.
Usage example
Use the following commands for computing pariwise corrlations of the sample file (data.txt) and storing the result into a binary file:
nvcc -lcublas -O2 -arch=compute_35 -code=sm_35 -std=c++11 CPU_side.cpp GPU_side.cu -o exec
./exec 5 3 b
As the result of running this example, 10 pairwise correlation (upper triangle part of the correlation matrix) will be stored in the binary file called corrs.bin.