User monitoring

Long term aggregations for end users

The long term job-specific aggregations for end users consist of eight metrics from the domains of energy consumption, performance characterization and I/O.

1. the total energy consumption per job [Wh]
2. the achieved memory bandwidth, averaged, per node.
3. the achieved floating point performance, averaged, per node.
4. the total amount of data written or read to the lustre file systems, per job.
5. the achieved peak write bandwidth to the lustre file systems, averaged, per node
6. the achieved peak read bandwidth to the lustre file systems, averaged, per node
7. the total number of metadata operations on the lustre file systems, per job
8. the achieved peak rate of metadata operations on the lustre file systems, averaged, per node

How the data is aggregated:

For the total energy consumption per job:

1. we calculate the median of the power consumption per node over the timeline of the job and sum up the respective contributions from all compute nodes of the job.
2. we factor in static contributions from the admin and storage infrastructure, averaged over all compute nodes, assigned per compute node of the job
3. we add a static contribution from the cooling distribution units, averaged over all compute nodes, assigned per compute node of the job
4. we include the overhead corresponding to the efficiency rating of the power supply units

For the achieved memory bandwidth:

1. this is the rate at which data can be read from or stored to the main memory.
2. to calculate the achieved memory bandwidth, we calculate the median of the memory bandwidth per node as reported by Lkiwd[1] over the timeline of the job. We then average over the respective contributions from all compute nodes of the job.

For the achieved floating point performance:

1. this is the amount of floating point operations per second. While this metric does not discriminate between single or double precision floating point operations, it will take into account the SIMD width [2] of the floating point instructions.
2. to calculate the amount of floating point operations per second, we calculate the median of the amount of floating point operations per second as reported by Lkiwd[1] over the timeline of the job. We then average over the respective contributions from all compute nodes of the job

[1] Likwid: https://github.com/RRZE-HPC/likwid

[2] SIMD: https://en.wikipedia.org/wiki/Single_instruction,_multiple_data