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.
The newest resource at AHPCC has 100 compute nodes, with 12 compute nodes on order. GPU and GPU-ready nodes are Dell R740, non-GPU nodes are Dell R640, which are different packaging but have the same software interface.
There are 49 public standard compute nodes with two Xeon Gold 6130 processors, 32 cores, and 192 GB of memory, which use the queues
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
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.
pinnacle and public condo usage is in general reserved for jobs that can use at least one of 1) the entire complement of 32 or 24 cores per node, 2) the V100 GPUs, or 3) the 192 GB of memory (that is more than 64 GB of
trestles) or 768 GB of memory (that is more than 192 GB of standard nodes) in standard or high-memory nodes, or 4) its 100 Gb/s Infiniband for multi-node computing. This excludes smaller jobs that can run adequately inside the core and memory footprint of single
razor nodes, that is
razor-1: 12 cores and 24 GB,
razor-2: 16 cores and 32 GB,
trestles: 32 cores and 64 GB.
In addition, the
gpu queues and nodes are reserved for jobs that use the GPU (since the GPU costs almost as much as the node does, and filling the CPU makes the GPU unavailable). Also the large-memory
himem queues and nodes are reserved for jobs that use more than 192 GB of shared memory, that is cannot run on the standard nodes. If you don't know the memory usage of your job, test jobs are allowable on
himem queues; however production jobs that don't use more than 192 GB are not.
These efficient-use requirements do not apply to condo owners on their own nodes.
trestles includes about 200 public nodes with four AMD 6136 8-core 2.4 GHz processes, 64 GB of memory, and about 90 GB of temporary space in '/local_scratch/'. This generation of AMD processors has about half the per-core compute capacity of recent Intel processors, so the
trestles 32-core nodes are roughly equivalent in computational power per-node in scalable programs to
razor-2 16-core nodes below. However the
nodes have twice the memory. The
trestles cluster also includes a number of Intel condo nodes. The
trestles nodes are connected with slow but reasonably reliable Mellanox QDR Infiniband.
trestles works well with scalable programs that fit into one node: 32 cores and 64 GB of memory. Multi-node programs on
trestles will run better on
pinnacle, which in one node runs about the same as two to five
trestles nodes, and has a much better network for larger jobs. Single-core to 12-core jobs will run better on
razor-2 if the memory footprint of 24 or 32 GB works, as Intel cores are much faster.
If your job fits into one node of 32 cores and 64 GB, please use
trestles to allow
pinnacle to run larger jobs.
razor-1 cluster has about 100 nodes with dual Intel X5670 processor, 12 cores, and 24 GB of memory. The
razor-2 cluster has about 80 nodes with dual Intel E5-2670v1 processor, 16 cores, and 32 GB of memory. The
razor clusters have large local disks of about 900 GB capacity. They are connected with now troublesome QLogic QDR Infiniband.
razor clusters also work well with programs that fit their core and memory footprint in one node, that is 12 cores and 24 GB on
razor-1 and 16 cores and 32 GB on
razor-2. The QLogic Infiniband on
razor has become unreliable and multi-node jobs are not recommended. Jobs that require multiple nodes on
razor are better run on
pinnacle in one node or multiple nodes with EDR Infiniband. The
razor clusters are also recommended for single core jobs.
razor-1 is generally less crowded if the single-core job uses less than 24 GB of memory.
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 email@example.com with questions. Here “memory” refers to shared memory of one node.
trestles, though low-core count jobs will be slow compared with Intel
himem06/himem72or high memory
pinnaclemultiple nodes standard
comp01/comp06/comp72will run much faster but probably start the job more slowly because of the job queue.
trestlesif memory is less than 64 GB.
pinnaclewill run much faster but will probably start the job more slowly because of the job queue.