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

The sreport command is an integral utility within the SLURM (Simple Linux Utility for Resource Management) workload manager, which is widely used for scheduling and managing jobs on computing clusters. It facilitates generating comprehensive reports about cluster usage, providing insights into job execution, user activity, and overall system performance from the accounting data. The command supports various options that allow users to get specific details which can help in resource management, planning, and optimization of the computational workload.

Use case 1: Show pipe delimited cluster utilization data

Code:

sreport --parsable cluster utilization

Motivation:

When managing a cluster, it’s crucial to regularly assess its utilization to ensure it’s operating efficiently and resources are neither underutilized nor overburdened. Monitoring utilization helps system administrators to make informed decisions regarding resource allocation, scaling, and load balancing. By using the --parsable flag, you obtain data in a format that’s easy to export or manipulate further with scripting tools, aiding in automated reporting processes or integration with monitoring systems.

Explanation:

• sreport: Invokes the SLURM command to generate reports.
• --parsable: Ensures that the report output is easily machine-readable, typically using a pipe-delimited format. This is useful for integration with data processing pipelines.
• cluster: Specifies that the report should be focused on the cluster level, rather than specific jobs or users.
• utilization: Requests data related to the usage patterns and efficiency of resource consumption within the cluster.

Example Output:

StartTime|EndTime|AllocCPUs|AllocNodes|...
2023-01-01T00:00:00|2023-01-01T23:59:59|1024|64|...
...

This example output is a time series showcasing the start and end times for data sampling, alongside resources allocated such as CPUs and nodes.

Use case 2: Show number of jobs run

Code:

sreport job sizes printjobcount

Motivation:

Understanding the volume of jobs submitted and executed over a period is essential for workload management and capacity planning on a cluster. It provides insights into user activity, queue lengths, and the system’s ability to handle submitted jobs. It can also help in identifying trends or shifts in workload that might require adjustments in infrastructure.

Explanation:

• sreport: Initiates the report-generating tool.
• job: Indicating that the report will involve job-related data.
• sizes: Specifies the focus on the size of the jobs, likely encompassing metrics such as the number executed.
• printjobcount: Directs the command to output the count of jobs, delivering a straightforward metric to gauge system activity.

Example Output:

ClusterName|Accounts|JobCount|TotalCPU
mycluster|all|2500|...

The output reveals the total number of jobs managed by the specified cluster and potentially relates these to CPU times, providing a clear measure of system throughput.

Use case 3: Show users with the highest CPU time use

Code:

sreport user topuser

Motivation:

Identifying users who consume the most CPU time is vital for fair resource allocation, diagnosis of potential bottlenecks, and planning future resource needs. It highlights users or projects with the highest demand, potentially guiding policy adjustments or user outreach to ensure optimal resource utilization.

Explanation:

• sreport: Engages the report-generating functionality within SLURM.
• user: Targets the report at user-specific data points rather than clusters or job metrics.
• topuser: Specifically flags the command to extract and display users who have the highest total CPU usage over a defined period.

Example Output:

UserName|CPUTime|...
jdoe|102400|...
asmith|98500|...

This output lists users by their total CPU time usage, potentially sorted in descending order, providing administrators with a clear perspective on who occupies the most resources.

Conclusion:

The sreport command is an essential tool for cluster administrators using SLURM, providing crucial insights into cluster utilization, workload, and user activity. These reports assist in maintaining efficiency, planning for future growth, and ensuring fair usage of shared computational resources, ultimately improving the functioning and management of high-performance computing environments. Each discussed use case demonstrates how sreport can be tailored to extract specific datasets, empowering users with targeted information for decision-making processes.