How to find available partitions on ARC: Difference between revisions
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the researchers who use ARC can find available partitions and see important features of the nodes in those partitions. | 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, <code>arc.hardware</code> is provided: | For this purpose on ARC, a special command, <code>arc.hardware</code>, is provided: | ||
<pre> | <pre> | ||
$ arc.hardware | $ arc.hardware | ||
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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). | |||
Note, that it means, that these nodes have about '''4gb of RAM per 1 CPU'''. | |||
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 === | |||
The <code>gpu-v100</code> partition has '''13 nodes''' in total. | |||
Each of these nodes has '''40 CPUs''' and '''753000 MB of RAM''' (about 735 gb of RAM), | |||
this comes out as '''18 gb per 1 CPU''', approximately. | |||
Each node also has '''two V100 nVidia GPUs''', this is shown in the "GPUs/node" column. | |||
The name pattern shows that the names for the nodes go from '''fg1''' to '''fg13'''. | |||
'''Please note''', that GPU partitions can only be used for computations that require use of GPUs. | |||
[[Category:Guides]] | |||
[[Category:How-Tos]] | |||
[[Category:ARC]] |
Latest revision as of 22:46, 20 September 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). Note, that it means, that these nodes have about 4gb of RAM per 1 CPU.
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
The gpu-v100
partition has 13 nodes in total.
Each of these nodes has 40 CPUs and 753000 MB of RAM (about 735 gb of RAM), this comes out as 18 gb per 1 CPU, approximately.
Each node also has two V100 nVidia GPUs, this is shown in the "GPUs/node" column.
The name pattern shows that the names for the nodes go from fg1 to fg13.
Please note, that GPU partitions can only be used for computations that require use of GPUs.