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Computing resources on Ares are assigned based on PLGrid computing grants (more information can be found here: Obliczenia w PLGrid). To perform computations on Ares you need to obtain a computing grant and also apply for Ares access , service through the PLGrid portal (https://aplikacje.plgrid.pl/service/dostep-do-klastra-ares-w-osrodku-cyfronet/).

If your grant is active, and you have applied for the service access, the request should be accepted in about half an hour, please . Please report any issues through the helpdesk.

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Ares is using Slurm resource manager, jobs should be submitted to the following partitions:

If you are unsure of how to properly configure your job on Ares please consult this guide: Job configuration

Accounts and computing grants

Ares uses a new naming scheme of naming accounts for CPU and GPU computing accounts, which are supplied by the -A parameter in sbatch command. Currently, accounts are named in the following manner:

grants. CPU-only grants are named: grantname-cpu, while GPU accounts use grantname-gpu suffix. Please mind that sbatch -A grantname won't work on its own. You need to add the -cpu or , -cpu-bigmem, or -gpu suffix! Available computing grants, with respective account names (allocations), can be viewed by using the hpc-grants command.

Resource allocated on Ares doesn't use normalization, which was used on Prometheus and previous clusters. 1 hour of CPU time equals 1 hour spent on a computing core , similar to GPU time. Resources used to calculate account billing include CPUs, memory, and GPUs. Jobs on CPU partitions are always billed in CPU hours, while jobs on GPU partitions are always billed in GPU hours. If your job uses the default amount of memory per core, or less, then the job is billed simply for the time spent using CPUswith a proportional amount of memory (consult the table above). The billing system accounts for jobs with more memory than the proportional amount. If the job uses more memory for each allocated CPU than the proportional amount (consult the table above), then the job , it will be billed as it would use have used more CPUs. The billed amount billed can be calculated by dividing the used memory used by the proportional memory per core and rounding the result to the closest and larger integer. Jobs on CPU partitions are always billed in CPU hours.

The same principle applies was applied to GPUsGPU resources, where a GPU has the respective amount of the GPU-hour is a billing unit, and there are proportional memory per GPU and proportional CPUs per GPU defined (consult the table above).

The cost of running a job can be expressed as a simple algorithm for CPUs:

cost_cpu    = job_cpus_used * job_duration
cost_memory = ceil(job_memory_used/memory_per_cpu) * job_duration
final_cost  = max(cost_cpu, cost_memory)

and for GPUsand for GPUs, where a GPU has the respective amount of memory per GPU and CPUs per GPU, respectively:

cost_gpu    = job_gpus_used * job_duration
cost_cpu    = ceil(job_cpus_used/cpus_per_gpu) * job_duration
cost_memory = ceil(job_memory_used/memory_per_gpu) * job_duration
final_cost  = max(cost_gpu, cost_cpu, cost_memory)

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Current usage, capacity and other storage attributes can be checked by issuing the hpc-fs command.

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and the environment can be purged by:

module purge

Sample job scripts

Example job scripts are available on this page: Sample scripts

More information

Ares is following Prometheus' configuration and usage patterns. Prometheus documentation can be found here: https://kdm.cyfronet.pl/portal/Prometheus:Basics