Compute Hardware Overview

Anunna's compute nodes can be categorized into CPU compute nodes and GPU compute node, which are segregated into different partitions, according to the hardware they provide.

This page summarises provides an overview of the hardware available at each node type and provides information on how to select the appropriate hardware for your job.

CPU Compute Nodes

Standard compute nodes for jobs that do not need a GPU. These make up the main partition and handle the bulk of the cluster's work.

At the moment, there are three generations of nodes in the cluster. Each generation corresponds to a specific CPU architecture and it is recommended you do not mix them.

In order to select a specific CPU model, constrain a job to a specific model with, for example, --constraint='gen3'. (see Choosing a node (constraints)

CPU Compute Nodes Overview

Nodes	Generation	Number of Nodes	CPU Maker	CPU Architecture	Sockets	Cores per Socket	Cores per Node	RAM (GB)	contraint
node1[00 - 28]	2	29	Intel	Skylake	2	16	32	375	gen2
node2[00 - 72]	3	73	AMD	Zen3 Milan	2	64	128	1000	gen3
node30[0, 2, 3]	4	3	AMD	Zen5 Turin	2	96	192	2250	gen4

GPU Compute Nodes

Nodes equipped with GPUs, for accelerated for workloads such as rendering, AI and deep learning.

The GPUs are distributed along two different partition, gpu and gpu_amd for Nvidia e AMD gpus respectively.

In order to allocate a GPU for a job, first the appropriate partition needs to be selected. Afterwards, at least one gpu needs to be allocated by using the command

--gres=gpu:<N>

Where <N> corresponds to the number of GPUs allocated for the job. If more than one GPU is desired, please make sure that the number of nodes in your SLURM script or srun command is set to 1. Otherwise SLURM may try to allocate GPUs in different nodes which will slow down you job considerably.

NVIDIA GPU Nodes

Available in the gpu partition. The cluster has several NVIDIA GPU models e.g. A100, L40s, A6000, and V100. The scheduler attempts to hand out the L40s first, then A100s, then A6000s, then V100s; the price per GPU-hour is the same for all of them.

In order to select a specific GPU model, constrain a job to a specific model with, for example, --constraint='A100'. (see Choosing a node (constraints)

AMD GPU Nodes

Available in the gpu_amd partition. Requested the same way as NVIDIA GPUs, using the gpu_amd partition. Since there is only one AMD GPU model, constraints are not necessary.

GPU Compute Nodes Overview

node	CPU Maker	Partition	CPU Architecture	Sockets	Cores per Socket	Cores per Node	RAM (GB)	GPU Maker	GPU Type	GPUs per Node	VRAM (GB)	VRAM Type	constraint
gpu100	Intel	gpu	Skylake	2	16	32	375	Nvidia	V100	4	16	HBM2	V100
gpun20[0 - 3]	Intel	gpu	Skylake	2	16	32	500	Nvidia	A100	4	80	HBM2e	A100
gpuxn200	Intel	gpu	Sapphire Rapids	2	32	64	500	Nvidia	RTX A6000	2	48	GDDR6	A6000
node301	AMD	gpu	Zen5 Turin	2	96	192	2250	Nvidia	L40s	4	48	GDDR6	L40S
gpua201	Intel	gpu_amd	Skylake	2	16	32	375	AMD	MI210	4	64	HBM2e	MI210

Available GPU Models

Anunna has a variety of GPU models and depending on the type of workflow some GPUs are going to be better suited then others. Here is a brief description of each GPU:

• NVIDIA V100 (16 GB HBM2) – Strong general-purpose training GPU for FP32/FP16 workloads, well-suited for classic deep learning training and HPC compute.
• NVIDIA A6000 (48 GB GDDR6) – High end workstation GPU optimized for for high-throughput inference and small- to mid-scale training, rendering and AI workloads.
• NVIDIA A100 (80 GB HBM2e) – Top-tier large-scale AI GPU with excellent mixed-precision performance, ideal for massive model training and multi-GPU scaling.
• NVIDIA L40S (48 GB GDDR6) – Optimized for high-throughput inference and small- to mid-scale training, great performance-per-watt for production AI workloads.

• AMD MI210 (64 GB HBM2e) – Strong HPC-leaning training GPU with high FP64/FP32 and solid BF16/FP16 training performance, best when ROCm fits the software stack.

See also

• Cluster Architecture Overview
• Choosing a node (constraints)
• Partitions / Queues