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--- equipment [2020/01/24 21:12] – root
+++ equipment [2025/10/15 19:51] (current) – external edit 127.0.0.1
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-=== Selecting Resources ===
+=== Equipment/Selecting Resources ===
 We describe the resources available at AHPCC and how to select the best one for your computing job.
 Computing resources are presently divided into four clusters that use separate schedulers.  This will be condensed in the future, as all logins will be moved to ''pinnacle.uark.edu'' and all schedulers are migrated to Slurm, with one or multiple Slurm schedulers to be determined.
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 There are 7 public high-memory compute nodes with two Xeon Gold 6126 processors, 24 cores, and 768 GB of memory, which use the queues ''himem06/himem72''.  These have fewer cores of higher frequency since they are used largely for bioinformatics in which many codes are not efficiently threaded.
 There are 19 public GPU nodes, like standard compute nodes but with a single NVidia V100 Tesla GPU which use the queues ''gpu06/gpu72''.
 There are 25 condo nodes: 20 Wang, standard compute nodes with also NVMe local drives; one Alverson standard compute, one Alverson high-memory compute, one Kaman high-memory compute with two V100s, and two Bernhard with two AMD 7351 and 256 GB of memory. These use the queues  ''condo'' for system owners and ''pcon06'' for the public, with appropriate modifiers to select the right nodes, see [[ queues ]].  Please do not submit ''condo,pcon06'' jobs without any modifiers, such jobs will be assigned randomly by the scheduler and will be killed.
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  These efficient-use requirements do not apply to condo owners on their own nodes.
+== Overall Recommendations ==
+We recommend the following clusters depending on the needs of your program and system load.
+These are rules of thumb  not covering every possible situation, contact hpc-support@listserv.uark.edu with questions.  Here "memory" refers to shared memory of one node.
+  * GPU-capable
+    * use ''pinnacle'' GPU queues
+  * not GPU-capable
+    * 1 to 12 cores and up to 24 GB memory: use ''razor-1''
+    * 1 to 16 cores and up to 32 GB memory: use ''razor-2''
+    * Up to 32 cores and up to 64 GB memory: use ''trestles'', though low-core count jobs will be slow compared with Intel
+    * more than 64 GB shared memory, or all 32 cores: use ''pinnacle'' standard ''comp01/comp06/comp72''
+    * more than 192 GB shared memory: use ''pinnacle'' ''himem06/himem72'' or high memory ''razor/trestles'' nodes
+    * more than 32 cores: use ''pinnacle'' multiple nodes standard ''comp01/comp06/comp72''
+Discretionary cases:
+  * anything requiring two or more ''razor/trestles'' nodes: ''pinnacle'' standard ''comp01/comp06/comp72'' will run much faster but probably start the job more slowly because of the job queue.
+  * 1 node, 32 cores and less than 192 GB memory: use ''pinnacle'' standard or ''trestles'' if memory is less than 64 GB.  ''pinnacle'' will run much faster but will probably start the job more slowly because of the job queue.