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--- hpc:accounting [2025/01/10 12:01] – [resource accounting uniformization] Yann Sagon
+++ hpc:accounting [2025/03/13 09:57] (current) – [Report and statistics with sreport] Yann Sagon
@@ Line 2: / Line 2: @@
 ====== Utilization and accounting ======
 When you submit jobs, they are using physical resources such as CPUs, Memory, Network, GPUs, Energy etc. We keep track of the usage of some of those resource. On this page we'll let you know how to consult your usage of the resource. We have several tools that you can use to consult your utilization: sacct, sreport, openxdmod
+===== Comparison of sreport, sacct, and sshare =====
+We use **sreport** as our primary accounting reference. However, you may find other tools useful for specific purposes. Here's a comparison:
+  * **sacct**: Displays only account jobs, excluding time allocated via reservations. If duplicate jobs exist, only one is shown.
+  * **sreport**: By default, jobs with wall times overlapping the report's time range are truncated. For reservation-based jobs, the requested idle time is distributed among all users with access to the reservation.
+  * **sshare**: Not recommended for accounting purposes; displayed values are adjusted based on fairshare calculations.
+===== Resource accounting uniformization =====
+We charge usage uniformly by converting GPU hours and memory usage into CPU hour equivalents, leveraging the [[https://slurm.schedmd.com/tres.html|TRESBillingWeights]] functionality provided by SLURM.
+A CPU hour represents one hour of processing time by a single CPU core.
+For GPUs, SLURM assigns a conversion factor to each GPU model through TRESBillingWeights (see below the conversion table), reflecting its computational performance relative to a CPU. Similarly, memory usage is also converted into CPU hour equivalents based on predefined weights, ensuring that jobs consuming significant memory resources are accounted for fairly.
+For example, a job using a GPU with a weight of 10 for 2 hours and memory equivalent to 5 CPU hours would be billed as 25 CPU hours. This approach ensures consistent, transparent, and fair resource accounting across all heterogeneous components of the cluster.
+You can see the detail of the conversion by looking at the parameter of a random partition on any of the clusters. We are using the same conversion table everywhere.
+<code>
+(bamboo)-[root@slurm1 ~]$ scontrol show partition debug-cpu | grep TRESBillingWeights | tr "," "\n"
+   TRESBillingWeights=CPU=1.0
+Mem=0.25G
+GRES/gpu=1
+GRES/gpu:nvidia_a100-pcie-40gb=5
+GRES/gpu:nvidia_a100_80gb_pcie=8
+GRES/gpu:nvidia_geforce_rtx_2080_ti=2
+GRES/gpu:nvidia_geforce_rtx_3080=3
+GRES/gpu:nvidia_geforce_rtx_3090=5
+GRES/gpu:nvidia_geforce_rtx_4090=8
+GRES/gpu:nvidia_rtx_a5000=5
+GRES/gpu:nvidia_rtx_a5500=5
+GRES/gpu:nvidia_rtx_a6000=8
+GRES/gpu:nvidia_titan_x=1
+GRES/gpu:tesla_p100-pcie-12gb=1
+</code>
+Here you can see for example that using a gpu nvidia_a100-pcie-40gb for 1 hour is equivalent in term of cost to use 5 CPUhour.
+===== Resources available for research group =====
+Research groups that have invested in the HPC cluster by purchasing private CPU or GPU nodes benefit from high priority access to these resources.
+While these nodes remain available to all users, owners receive priority scheduling and a designated number of included compute hours per year.
+To check the details of their owned resources, users can run the script ''ug_getNodeCharacteristicsSummary.sh'', which provides a summary of the node characteristics within the cluster.
+Example:
+<code>
+ug_getNodeCharacteristicsSummary.sh --partitions private-<group>-gpu private-<group>-cpu --cluster <cluster> --summary
+host    sn             cpu    mem    gpunumber    gpudeleted  gpumodel                      gpumemory  purchasedate      months remaining in prod. (Jan 2025)    billing
+------  -----------  -----  -----  -----------  ------------  --------------------------  -----------  --------------  --------------------------------------  ---------
+cpu084  N-20.02.151     36    187            0             0                                        0  2020-02-01                                           1         79
+cpu085  N-20.02.152     36    187            0             0                                        0  2020-02-01                                           1         79
+cpu086  N-20.02.153     36    187            0             0                                        0  2020-02-01                                           1         79
+cpu087  N-20.02.154     36    187            0             0                                        0  2020-02-01                                           1         79
+cpu088  N-20.02.155     36    187            0             0                                        0  2020-02-01                                           1         79
+cpu089  N-20.02.156     36    187            0             0                                        0  2020-02-01                                           1         79
+cpu090  N-20.02.157     36    187            0             0                                        0  2020-02-01                                           1         79
+cpu209  N-17.12.104     20     94            0             0                                        0  2017-12-01                                           0         41
+cpu210  N-17.12.105     20     94            0             0                                        0  2017-12-01                                           0         41
+cpu211  N-17.12.106     20     94            0             0                                        0  2017-12-01                                           0         41
+cpu212  N-17.12.107     20     94            0             0                                        0  2017-12-01                                           0         41
+cpu213  N-17.12.108     20     94            0             0                                        0  2017-12-01                                           0         41
+cpu226  N-19.01.161     20     94            0             0                                        0  2019-01-01                                           0         41
+cpu227  N-19.01.162     20     94            0             0                                        0  2019-01-01                                           0         41
+cpu228  N-19.01.163     20     94            0             0                                        0  2019-01-01                                           0         41
+cpu229  N-19.01.164     20     94            0             0                                        0  2019-01-01                                           0         41
+cpu277  N-20.11.131    128    503            0             0                                        0  2020-11-01                                          10        251
+gpu002  S-16.12.215     12    251            5             0  NVIDIA TITAN X (Pascal)           12288  2016-12-01                                           0         84
+gpu012  S-16.12.216     24    251            8             0  NVIDIA GeForce RTX 2080 Ti        11264  2016-12-01                                           0        108
+gpu017  S-20.11.146    128    503            8             0  NVIDIA GeForce RTX 3090           24576  2020-11-01                                          10        299
+gpu023  S-21.09.121    128    503            8             0  NVIDIA GeForce RTX 3080           10240  2021-09-01                                          20        283
+gpu024  S-21.09.122    128    503            8             0  NVIDIA GeForce RTX 3080           10240  2021-09-01                                          20        283
+gpu044  S-23.01.148    128    503            8             0  NVIDIA RTX A5000                  24564  2023-01-01                                          36        299
+gpu047  S-23.12.113    128    503            8             0  NVIDIA RTX A5000                  24564  2023-12-01                                          47        299
+gpu049  S-24.10.140    128    384            8             0  NVIDIA GeForce RTX 4090           24564  2024-10-01                                          57        291
+============================================================ Summary ============================================================
+Total CPUs: 1364 Total CPUs memory[GB]: 6059 Total GPUs: 61 Total GPUs memory[MB]: 142300 Billing: 1959 CPUhours per year: 10.30M
+</code>
+How to read the output:
+  * **host**: the hostname of the compute node
+  * **sn**: the serial number of the node
+  * **cpu**: the number of CPUs available in the node
+  * **mem**: the quantity of memory on the node in GB
+  * **gpunumber**: the number of GPU cards on the node
+  * **gpudeleted**: the number of GPU cards out of order
+  * **gpumodel**: the GPU model
+  * **gpumemory**: the GPU memory in MB per GPU card
+  * **purchasedate**: the purchase date of the node
+  * **months remaining in prod. (Jan 2025)**: the number of months the node remains the property of the research group, the reference date is indicated in parenthesis. In this example it is January 2025.
+  * **billing**: the [[hpc:accounting#resource_accounting_uniformization|billing]] value of the compute node
+You can modify the reference year if you want to "simulate" the hardware you'll have in your private partition in a given year. To do so, use the argument ''<nowiki>--reference-year</nowiki>'' of the script.
 ===== Job accounting =====
-If you are interested in your HPC usage, group usage, job wait time, etc., we have the right tools for you.
 ==== OpenXDMoD ====
@@ Line 34: / Line 133: @@
 To get reporting about your past jobs, you can use ''sreport'' (https://slurm.schedmd.com/sreport.html).
+We wrote a helper that you can use to get your past resource usage on the cluster. This script can display the resource utilization
+  * for each user of a given account (PI)
+  * total usage of a given account (PI)
+<code>
+(baobab)-[sagon@login1 ~]$ ug_slurm_usage_per_user.py -h
+usage: ug_slurm_usage_per_user.py [-h] [--user USER] [--start START] [--end END] [--pi PI] [--cluster CLUSTER] [--all_users] [--report_type {user,account}] [--time_format TIME_FORMAT] [--verbose]
+Retrieve HPC utilization statistics for a user within a specified time range.
+options:
+  -h, --help            show this help message and exit
+  --user USER           The username to retrieve utilization for.
+  --start START         Start date (default: first day of current month).
+  --end END             End date (default: current time).
+  --pi PI               Specify the PI (account) manually (optional). If not provided, it will be auto-detected.
+  --cluster CLUSTER     Specify the cluster manually (optional). If not provided, all the clusters will be selected.
+  --all_users           If you want to see utilization of all users of a given account (PI)
+  --report_type {user,account}
+                        Report type: UserUtilizationByAccount or AccountUtilizationByUser
+  --time_format TIME_FORMAT
+                        Specify the time formt for the reporting. Default is by hours. You can use Minutes or Seconds
+  --verbose             Print verbose msgs
+</code>
+By default when you run this script, it will print your past usage of the current month, for all the accounts you are member of.
+=== sreport examples ===
 Here are some examples that can give you a starting point :
@@ Line 82: / Line 214: @@
-==== Differences between sreport, sacct and sshare ====
-  * sacct: Displays only account jobs, excluding time requested via reservation. If duplicate jobs exist, only one is returned.
-  * sreport: By default, the report is truncated if a job's wall time overlaps the report's time span. For jobs using a reservation, the idle requested time is distributed among all users with access to the reservation.
-  * sshare: Avoid using sshare as an accounting reference; the displayed values are adjusted due to fairshare calculations.
-==== resource accounting uniformization ====
-We charge usage uniformly by converting GPU hours and memory usage into CPU hour equivalents, leveraging the [[https://slurm.schedmd.com/tres.html|TRESBillingWeights]] functionality provided by SLURM.
-A CPU hour represents one hour of processing time by a single CPU core.
-For GPUs, SLURM assigns a conversion factor to each GPU model through TRESBillingWeights (see below the conversion table), reflecting its computational performance relative to a CPU. Similarly, memory usage is also converted into CPU hour equivalents based on predefined weights, ensuring that jobs consuming significant memory resources are accounted for fairly.
-For example, a job using a GPU with a weight of 10 for 2 hours and memory equivalent to 5 CPU hours would be billed as 25 CPU hours. This approach ensures consistent, transparent, and fair resource accounting across all heterogeneous components of the cluster.