Parallelization

Running scripts in parallel on a Linux command line interface (CLI) enhances the efficiency of processes by utilizing multiple cores or threads of a CPU simultaneously. This approach is particularly beneficial for tasks that can be executed independently, without requiring interaction or data exchange between them. Several tools and utilities facilitate parallel execution in Linux, including xargs, GNU Parallel, and background processes using &.

GNU Parallel

GNU Parallel is a powerful shell tool for executing jobs in parallel. It can be used to run commands from a file or input, spreading them across multiple CPU cores.

Example: Converting MRI images from DICOM to NIfTI format using dcm2niix:

ls /path/to/dicom_folders/* | parallel dcm2niix -o /path/to/output_folder {}

This command lists all DICOM folders and pipes them to parallel, which executes dcm2niix for each folder, directing the output to a specified directory. Each conversion runs in parallel, significantly reducing the total processing time.

xargs

xargs can run multiple instances of a command in parallel with the -P flag.

Example: Similarly, using xargs to convert DICOM images:

ls /path/to/dicom_folders/* | xargs -n 1 -P 4 dcm2niix -o /path/to/output_folder

This command processes up to 4 folders in parallel (-P 4), with each dcm2niix command working on a single folder at a time (-n 1).

Background Processes

Commands can be executed in the background using &, allowing the next command to start executing without waiting for the current one to finish.

Example: Running two dcm2niix commands in parallel:

dcm2niix -o /path/to/output_folder /path/to/dicom_folder1 & dcm2niix -o /path/to/output_folder /path/to/dicom_folder2 &
wait

This method is simpler but less scalable for a large number of jobs compared to GNU Parallel or xargs.

Operations Benefiting from Parallel Execution

Operations like copying, renaming, or moving files generally see limited benefits from parallel execution because they are often limited by disk I/O rather than CPU processing power. However, for CPU-intensive tasks such as image processing, conversion, or data analysis (e.g., processing MRI imaging data), parallel execution can significantly reduce processing time.

Converting MRI images from DICOM to NIfTI format using dcm2niix is a CPU-intensive task that benefits from parallel execution. As each image or folder of images can be processed independently, distributing these tasks across multiple cores or processors speeds up the overall process, making it an ideal candidate for parallelization.