How to find available partitions on ARC: Difference between revisions

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If a partition contains nodes with different hardware configurations then  
If a partition contains nodes with different hardware configurations then  
the specs for these nodes will be shown on additional lines without a partition name (see the <code>bigmem</code> partition, for example).
the specs for these nodes will be shown on additional lines without a partition name (see the <code>bigmem</code> partition, for example).
=== Example 1: cpu2019 ===
The <code>cpu2019</code> partition, for example, has '''40 compute nodes''' (second column) in total.
Each node in that partition (out of those 40) has '''40 CPUs''' and '''185000 MB of RAM''' (about 180gb of RAM).
There are no GPUs in the nodes in this partition.
The last column shows the node name pattern, the names of the nodes in the partition go from '''fc22''' to '''fc61'''.
=== Example 2: gpu-v100 ===

Revision as of 22:49, 19 January 2023

ARC is a relatively large and very heterogeneous cluster. It has lots of compute nodes and some of these nodes are very different in their hardware specifications and performance capabilities. On ARC, nodes with similar specifications are grouped into SLURM partitions.

To use ARC effectively and to its full potential it is important that the researchers who use ARC can find available partitions and see important features of the nodes in those partitions.

For this purpose on ARC, a special command, arc.hardware is provided:

$ arc.hardware

Node specifications per partition:
      ================================================================================
           Partition |   Node    CPUs    Memory        GPUs  Node list
                     |  count   /node      (MB)       /node  
      --------------------------------------------------------------------------------
              bigmem |      2      80   3000000              fm[1-2]
                     |      1      40   4127000      a100:4  mmg1
                     |      1      40   8256000      a100:2  mmg2
             cpu2013 |     14      16    120000              h[1-14]
        cpu2017-bf05 |     16      28    245000              s[1-16]
                     |     20      28    188000              th[1-20]
             cpu2019 |     40      40    185000              fc[22-61]
        cpu2019-bf05 |     87      40    185000              fc[1-21,62-127]
             cpu2021 |     17      48    185000              mc[1-11,14-19]
        cpu2021-bf05 |     21      48    185000              mc[23-43]
        cpu2021-bf24 |      7      48    381000              mc[49-55]
             cpu2022 |     52      52    256000              mc[73-124]
        cpu2022-bf24 |     16      52    256000              mc[57-72]
            gpu-a100 |      6      40    515000      a100:2  mg[1-6]
            gpu-v100 |     13      40    753000      v100:2  fg[1-13]
             lattice |    196       8     11800              cn[169-364]
            parallel |    572      12     23000              cn[0513-0544,0557-1096]
                     |      4      12     23000     m2070:2  cn[0553-0556]
              single |    168       8     11800              cn[001-168]
      ================================================================================

The output table shows the list of the partitions available for use for this specific user (holder of this account).

The left column shows partition names and the rest of the table shows information about the nodes in the partition. If a partition contains nodes with different hardware configurations then the specs for these nodes will be shown on additional lines without a partition name (see the bigmem partition, for example).

Example 1: cpu2019

The cpu2019 partition, for example, has 40 compute nodes (second column) in total.

Each node in that partition (out of those 40) has 40 CPUs and 185000 MB of RAM (about 180gb of RAM).

There are no GPUs in the nodes in this partition.

The last column shows the node name pattern, the names of the nodes in the partition go from fc22 to fc61.

Example 2: gpu-v100