How to use the command 'sh5util' (with examples)

The sh5util command is a powerful tool used for managing and analyzing HDF5 files that arise from job monitoring and profiling when using the SLURM workload manager. These files are typically generated by the sacct_gather_profile plugin, which collects valuable performance metrics and resource usage data from different workloads or computational jobs running in a distributed computing environment. The primary purpose of sh5util is to merge these data-intensive files for easier analysis and to extract relevant insights from them. Leveraging its robust capabilities helps in simplifying data management tasks, facilitating better resource utilization reporting, and improving overall workflow efficiency.

Use case 1: Merge HDF5 files produced on each allocated node for the specified job or step

Code:

sh5util --jobs=job_id|job_id.step_id

Motivation:

In distributed computing environments, jobs often run on multiple nodes to harness parallel computational power. Each node generates its own set of HDF5 files containing performance metrics. Analyzing disparate files from each node can be cumbersome and time-consuming. By merging these files, you can centralize data representation, making it easier to perform comprehensive analyses and visualize the complete picture of resource utilization across the job’s entirety. This can be crucial for debugging, performance tuning, and efficient resource allocation for future tasks.

Explanation:

• --jobs=job_id|job_id.step_id: This argument specifies the particular job or job step whose HDF5 files are to be merged. The job_id represents the unique identifier for the job, while job_id.step_id specifies a particular step within a larger job. By targeting specific jobs and steps, users ensure that only relevant data are merged, preventing unnecessary data bloat and focusing the analysis on specific tasks.

Example output:

After running this command, a merged HDF5 file is produced that represents all the data from the specified job or step. This new file can then be used for further analysis.

Use case 2: Extract one or more data series from a merged job file

Code:

sh5util --jobs=job_id|job_id.step_id --extract -i path/to/file.h5 --series=Energy|Filesystem|Network|Task

Motivation:

Once HDF5 files are merged, specific data insights may be required, such as energy consumption, filesystem usage, network performance metrics, or task-specific information. Extracting these series allows users to fine-tune their analysis on areas of interest. For instance, a performance analyst might want to focus on energy usage patterns for efficiency reporting or to identify potential bottlenecks affecting network throughput.

Explanation:

• --jobs=job_id|job_id.step_id: As in the first use case, this identifies the job or job step relevant to the dataset being probed.
• --extract: This flag indicates that the operation to be performed is data extraction from the merged file.
• -i path/to/file.h5: Here, -i specifies the input file path, guiding sh5util to the merged HDF5 file from which data series should be extracted.
• --series=Energy|Filesystem|Network|Task: This argument determines which data series or types are to be extracted. Users can specify one or multiple series depending on the requirements.

Example output:

Running this command results in the extraction of specified data series into a new file or console output, simplifying access to targeted datasets for review or reporting.

Use case 3: Extract one data item from all nodes in a merged job file

Code:

sh5util --jobs=job_id|job_id.step_id --item-extract --series=Energy|Filesystem|Network|Task --data=data_item

Motivation:

When dealing with a comprehensive merged HDF5 file, pinpointing a single data item across all nodes can provide granular insight into specific metrics like “peak memory usage” or “CPU load.” This kind of detailed visibility at the data item level is invaluable for in-depth analysis, benchmarking, troubleshooting, or performance comparisons across nodes.

Explanation:

• --jobs=job_id|job_id.step_id: Identical to previous cases, it designates the job or step for inquiry.
• --item-extract: Signals that a specific data item should be extracted from the series.
• --series=Energy|Filesystem|Network|Task: Defines which data series encompasses the item of interest.
• --data=data_item: This parameter pinpoints the exact data item to be extracted from the HDF5 dataset, ensuring precise data retrieval.

Example output:

The output provides the extracted data item values, typically organized by node, giving immediate access to the individual metrics for further examination or reporting purposes.

Conclusion:

The sh5util command is an efficient way to handle complex datasets generated by distributed computing jobs. Merging HDF5 files and extracting specific data insights effectively streamline performance evaluations and resource management. By mastering these use cases, users can ensure they harness the full potential of their computational infrastructure while optimizing performance and monitoring tasks.