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
:
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:
-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
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.