Understanding Submitting Python Scripts with Slurm and Srun on HPC скачать в хорошем качестве

Understanding Submitting Python Scripts with Slurm and Srun on HPC

Discover how to effectively submit Python scripts using `slurm`, ensuring optimal performance in high-performance computing environments. --- This video is based on the question https://stackoverflow.com/q/77749209/ asked by the user 'Okano' ( https://stackoverflow.com/u/5623007/ ) and on the answer https://stackoverflow.com/a/77819984/ provided by the user 'tomgalpin' ( https://stackoverflow.com/u/12113829/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions. Visit these links for original content and any more details, such as alternate solutions, comments, revision history etc. For example, the original title of the Question was: Submitting to slurm a python script which calls srun Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l... The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license. If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com. --- Understanding Submitting Python Scripts with Slurm and Srun on HPC High-Performance Computing (HPC) environments can be quite complex, especially when it comes to submitting jobs that involve multiple scripts and commands. One common scenario is submitting a Python script that ultimately calls srun for execution. If you've ever found yourself in this situation and wondered about potential performance impacts, you're not alone. The Problem at Hand You have a Python script that you want to execute in a Slurm-managed environment. Your concern is about the performance implications of submitting your job through a bash script that triggers a Python execution, which then calls srun to run a parallel workload. Specifically, you’re worried that Python may take up a single process, affecting your job's overall performance. The Workflow Breakdown To clarify your situation, let’s look at the workflow you described: Submit Job: You start by using sbatch myscript.sh. Bash Script Execution: This script calls the Python script, e.g., python running.py. Python Script: Inside this Python script, you utilize check_call from a subroutine module to issue the srun command. HPC Workload: The srun command then launches your massively-parallelized application. Now, let’s discuss whether this setup might introduce any performance issues. Performance Considerations Minimal Overhead Based on the described workflow, it appears that you would only introduce a very small (negligible) overhead by submitting your job this way. This is primarily because: The overhead happens at the startup phase when you submit the bash script that runs the Python script. As long as the Python script and srun are designed efficiently, the overall execution won't face significant delays or bottlenecks. Runtime Performance During the runtime of your application, the performance of the workload itself should not be negatively affected. Here’s why: Job Submission: The time taken for the initial job submission is relatively insignificant in the grand scheme of your application's total execution time. Process Management: Once you invoke srun, it effectively manages the distribution of processes across the nodes in the cluster, which is where the parallel performance gains come into play. Key Takeaways Negligible Overhead: Executing a bash script to launch a Python program in turn calling srun will have minimal impact on performance. Efficient Use of Resources: By utilizing srun, you're leveraging the HPC infrastructure for optimal workload distribution. Focus on Code: Concentrate on writing efficient Python and bash scripts; the performance concerns regarding job submission are minimal. In conclusion, as long as you structure your submissions correctly, you can confidently utilize Slurm to handle your Python scripts without worrying excessively about performance loss. Shifting focus to the efficiency of your code and the specific configurations of your HPC environment will yield the best outcomes. If you have more questions or need a deeper dive into specific concerns, feel free to ask!