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


=== How is data aggregated (default): ===
# the total energy consumption, per job
# Collection of data over a timebucket and depending on each metric  perform a calculation to get new metric. The formula for each metric follow the formulas from:  https://github.com/RRZE-HPC/likwid/tree/master/groups/zen2
# the achieved memory bandwidth, averaged, per node.
# Calculate median, min, max of each node or CPU if relevant.
# the achieved floating point performance, averaged, per node.
# Calculate average and standard deviation of median, 10th percentile of min and 90th percentile of max.
# the total amount of data written to the lustre file systems, per job.
# the total amount of data read from the lustre file systems, per job.
# the achieved peak write bandwidth to the lustre file systems, per job
# the achieved peak read bandwidth to the lustre file systems, per job
# the total number of metadata operations on the lustre file systems, per job 
# the achieved peak rate of metadata operations on the lustre file systems, per job


=== What the metrics represent and how the data is aggregated: ===


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


=== Aggregated metrics: ===
2. The achieved memory bandwidth, averaged, per node [in GByte/s]:
* Bandwidth: Total memory bandwidth on socket basis. The two memory controller of a socket are added together. The data is saved in Bytes/s in the TimescaleDB  and in GBytes/s in the JSON for Users.
  1. this is the rate at which data can be read from or stored to the main memory.
* Flops:  Amount of floating point operations per second. The group does not differentiate between singe and double point precision rate.  The data is saved in MFlops/s in
  2. to calculate the achieved memory bandwidth, we calculate the mean of the memory bandwidth per node as reported by Likwid [https://github.com/RRZE-HPC/likwid] over the timeline of the job. We then average over all compute nodes of the job.
* Energy sum: This is the total amount of energy consumed by each node. It’s measured in W. It is calculated by first adding the rapl counters of both sockets together and then adding a constant of 220 W for the energy consumption of other parts of the node.
 
3. The achieved floating point performance, averaged, per node [in GFlop/s]:
  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 [https://en.wikipedia.org/wiki/Single_instruction,_multiple_data] of the floating point instructions.
  2. to calculate the amount of floating point operations per second, we calculate the mean of the amount of floating point operations per second as reported by Likwid over the timeline of the job. We then average over all compute nodes of the job.
 
4. The total amount of data written to the lustre file systems, per job [in GByte]:
   1. the amount of data written to the workspaces of the lustre storage within the job.
 
5. The total amount of data read from the lustre file systems, per job [in GByte]:
  1. the amount of data read from the workspaces of the lustre storage within the job.
 
6. the achieved peak write bandwidth to the lustre file systems, per job [in Gbyte/s]
  1. this is the peak rate at which the job writes to the workspaces of the lustre storage, over the lifetime of the job.
 
7. the achieved peak read bandwidth to the lustre file systems, per job [in Gbyte/s]
  1. this is the peak rate at which the job reads from the workspaces of the lustre storage, over the lifetime of the job.
 
8. the total number of metadata operations on the lustre file systems, per job [in metadata ops]
  1. this is the number of metadata operations [status, open, close, rename, unlink, etc.] on the lustre storage which can be assigned to the job.
 
9. the achieved peak rate of metadata operations on the lustre storage, per job [in metadata ops/s]
  1. this is the peak rate of metadata operations on the lustre storage which can be assigned to the job, over the lifetime of the job.

Latest revision as of 10:21, 29 August 2024

Long term aggregations for end users

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

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

What the metrics represent and how the data is aggregated:

1. The total energy consumption, per job [in Wh] 

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

2. The achieved memory bandwidth, averaged, per node [in GByte/s]: 

 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 mean of the memory bandwidth per node as reported by Likwid [1] over the timeline of the job. We then average over all compute nodes of the job.

3. The achieved floating point performance, averaged, per node [in GFlop/s]: 

 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 mean of the amount of floating point operations per second as reported by Likwid over the timeline of the job. We then average over all compute nodes of the job.

4. The total amount of data written to the lustre file systems, per job [in GByte]: 

 1. the amount of data written to the workspaces of the lustre storage within the job.

5. The total amount of data read from the lustre file systems, per job [in GByte]: 

 1. the amount of data read from the workspaces of the lustre storage within the job.

6. the achieved peak write bandwidth to the lustre file systems, per job [in Gbyte/s] 

 1. this is the peak rate at which the job writes to the workspaces of the lustre storage, over the lifetime of the job.

7. the achieved peak read bandwidth to the lustre file systems, per job [in Gbyte/s] 

 1. this is the peak rate at which the job reads from the workspaces of the lustre storage, over the lifetime of the job.

8. the total number of metadata operations on the lustre file systems, per job [in metadata ops] 

 1. this is the number of metadata operations [status, open, close, rename, unlink, etc.] on the lustre storage which can be assigned to the job.

9. the achieved peak rate of metadata operations on the lustre storage, per job [in metadata ops/s] 

 1. this is the peak rate of metadata operations on the lustre storage which can be assigned to the job, over the lifetime of the job.
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