hpc:accounting
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| hpc:accounting [2025/12/08 12:43] – [Agregate usage of all users of a given PI] Yann Sagon | hpc:accounting [2025/12/10 07:41] (current) – [sreport examples] Yann Sagon | ||
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| - | {{METATOC 1-5}} | + | {{METATOC 1-8}} |
| ====== Utilization and accounting ====== | ====== 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: | 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: | ||
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| We use this model because our cluster is heterogeneous, | We use this model because our cluster is heterogeneous, | ||
| - | We also bill memory usage because some jobs consume very little CPU but require large amounts of memory, which means an entire compute node is occupied. This ensures that jobs using significant memory resources are accounted for fairly. | + | We also **account for memory usage** because some jobs consume very little CPU but require large amounts of memory, which means an entire compute node is occupied. This ensures that jobs using significant memory resources are accounted for fairly. |
| - | 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 guarantees consistent, transparent, | ||
| - | You can check the up to date conversion details by inspecting the parameters of any partition on the clusters. The same conversion table is applied | + | ==== Conversion Rules extract (see below for details) ==== |
| + | * **1 CPU core = 1 CPUh per hour** | ||
| + | * **1 GB RAM = 0.25 CPUh per hour** | ||
| + | * **1 GPU A100 (40 GB) = 5 CPUh per hour** | ||
| + | |||
| + | ==== Example Calculation ==== | ||
| + | Suppose you request: | ||
| + | * **2 CPUs** | ||
| + | * **20 GB RAM** | ||
| + | * **1 GPU A100** | ||
| + | |||
| + | The cost per hour is calculated as: | ||
| + | * CPU: 2 × 1 CPUh = **2 CPUh** | ||
| + | * RAM: 20 GB × 0.25 CPUh = **5 CPUh** | ||
| + | * GPU: 1 × 5 CPUh = **5 CPUh** | ||
| + | |||
| + | **Total per hour = 2 + 5 + 5 = 12 CPUh** | ||
| + | |||
| + | This approach guarantees consistent, transparent, | ||
| + | |||
| + | You can check the up to date conversion details by inspecting the parameters of any partition on the clusters. The same conversion table is applied | ||
| < | < | ||
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| 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. | 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. | ||
| + | === Usage details of a given PI === | ||
| + | < | ||
| + | (baobab)-[sagon@login1] $ ug_slurm_usage_per_user.py --pi **** --report-type account --start 2025-01-01 | ||
| + | -------------------------------------------------------------------------------- | ||
| + | |||
| + | Cluster/ | ||
| + | |||
| + | Usage reported in TRES Hours | ||
| + | |||
| + | -------------------------------------------------------------------------------- | ||
| + | |||
| + | Cluster | ||
| + | --------- | ||
| + | bamboo | ||
| + | baobab | ||
| + | yggdrasil | ||
| + | Total usage: 1.14M | ||
| + | </ | ||
| + | |||
| === Usage details of all PIs associated with a private group === | === Usage details of all PIs associated with a private group === | ||
| Line 218: | Line 256: | ||
| === sreport examples === | === sreport examples === | ||
| + | |||
| + | <note important> | ||
| Here are some examples that can give you a starting point : | Here are some examples that can give you a starting point : | ||
hpc/accounting.1765197807.txt.gz · Last modified: by Yann Sagon