1 Overview
2 Requesting Resources
- 2.1 Requesting CPUs
- 2.2 Requesting Memory
- 2.3 Requesting GPUs
  - 2.3.1 GPU Varieties Available
- 2.4 Requesting FPGAs
- 2.5 Requesting the Grace Hopper ARM
3 Additional Help

Overview

Sol is a homogenous supercomputer. Homogeneous supercomputers feature processors and interconnects that are of the same type, brand, and architecture. This uniformity simplifies system management and optimization. This page describes the hardware within Sol for reference

Node Type	CPU	Memory	Accelerator

Node Type	CPU	Memory	Accelerator
Standard Compute	128 Cores (2x AMD EPYC 7713 Zen3)	512 GiB	N/A
High Memory	128 Cores (2x AMD EPYC 7713 Zen3)	2048 GiB	N/A
GPU A100	48 Cores (2x AMD EPYC 7413 Zen3)	512 GiB	4x NVIDIA A100 80GiB
GPU A30	48 Cores (2x AMD EPYC 7413 Zen3)	512 GiB	3x NVIDIA A30 24GiB
GPU MIG	48 Cores (2x AMD EPYC 7413 Zen3)	512 GiB	16x NVIDIA A100 sliced into 20GiB and 10GiB “slices”
Xlienx FPGA	48 Cores (2x AMD EPYC 7443 Zen3)	256 GiB	1x Xilinx U280
Bitaware FPGA	52 Cores (Intel Xeon Gold 6230R)	376 GiB	1x BittWare 520N-MX
NEC FPGA	48 Cores (2x AMD EPYC 9274F Zen4)	512 GiB	1x NEC Vector Engine
GraceHopper	72 Cores (NVIDIA Grace CPU aarch64)	512 GiB	1x NVIDIA GH200 480GB

There is privately owned hardware that may have slightly different specs. See the Sol Status Page for the full features of every node

Requesting too many resources would led to a long job queueing time.

Using too many resources would cost a large amount of fairshare points, then led to a long job queueing time. Check the efficiency of a completed test job can help with determining an appropriate amount of resource to request.

Requesting Resources

Requesting CPUs

To request a given number of CPUs sharing the same node, you can use the following in your SBATCH:

#SBATCH -N 1 # Number of Nodes
#SBATCH -c 5 #Number of Cores per task
or
interactive -N 1 -c 5

This will create a job with 5 CPU cores on one node.

To request a given number of CPUs spread across multiple nodes, you can use the following:

#SBATCH -N 2-4    # number of nodes to allow tasks to spread across (MIN & MAX)
#SBATCH -n 10    # number of TASKS
#SBATCH -c 5     # CPUs per TASK
or
interactive -N 2-4 -n 10 -c 5

The above example will allocate a total of 50 cores spread across as few as 2 nodes or as many as 4 nodes.

Take note of the inclusion or omission of -N:

#SBATCH -c 5     # CPUs per TASK
#SBATCH -n 10    # number of TASKS
or
interactive -n 10 -c 5

This reduced example will still allocate 50 cores, 5 cores per task on any number of available nodes. Note, that unless you are using MPI-aware software, you will likely prefer to always add -N, to ensure that each job worker has sufficient connectivity.

-c and -n have similar effects in Slurm in allocating cores, but -n is the number of tasks, and -c is the number of cores per task. MPI processes bind to a task, so the general rule of thumb is for MPI jobs to allocate tasks, while serial jobs allocate cores, and hybrid jobs allocate both.

See the official Slurm documentation for more information: https://slurm.schedmd.com/sbatch.html

Requesting Memory

Cores and memory are de-coupled: if you need only a single CPU core but ample memory, you can do so like this:

If you do not specify --mem, you will be allocated 2GiB per CPU core OR 24GiB per GPU

To request more than 512GiB of memory, you will need to use the highmem partition

To request all available memory on a node:

This will allocate all CPU cores memory (up to 2TiB depending on the node) to your job. This will prevent any other jobs from landing on this node. Only use this if you truly need that much memory

Requesting GPUs

To request a GPU, you can specify the -G option within your job request.

This will allocate the first available GPU that fits your job request:

To request multiple GPUs specify a number greater than 1:

To request a specific number of GPUs per node when running multi-node:

To request a specific type of GPU (a100 for example):

GPU Varieties Available

Below is a table demonstrating the available GPU instance sizes you can allocate:

GPU Name	GPU Memory	Slice Count
`a100`	80GB, 40GB	4 per node, NVLINKed
`a30`	24GB	4 per node, NVLINKed
`1g.20gb`	20GB	4 per node
`2g.20gb`	20GB	12 per node
`h100` (Privately Owned)	96GB	4 -8 per node, NVLINKed

The a100s can come in two varieties, as seen above.

To guarantee a 80GB a100, include this feature: #SBATCH -C a100_80. This can be done also with interactive -C a100_80 (a100_40 is also provided). To request more than one a100s while specifying the variety:

Requesting FPGAs

Sol has two nodes with a Field Programmable Gate Array (FPGA) accelerator. One is an Intel-based node with a Bitaware 520N-MX FPGA, the other is an AMD-based node with a Xilinx U280. Because there is only FPGA per node, it is recommended to allocate the entire node.

Bitaware:

Xilinx:

Or via the web portal using the “Additional Sbatch options” section:

Note there should not be a space between “-L” and the FPGA name on the web portal

Requesting the Grace Hopper ARM

The GraceHopper is a specialized unit running the ARM architecture aarch64, which is separate and non-compatible with x86_64 applications. While this node frequently is idle, unless your application is designed for this less-common architecture, you should expect compiled applications to fail on execution.

Requesting this node requires doing so exclusively.

Research Computing

Sol Hardware - How to Request