Compute nodes

DAIC compute nodes are high-performance servers with multiple CPUs, large memory, and, on many nodes, one or more GPUs. The cluster is heterogeneous: nodes vary in processor types, memory sizes, GPU configurations, and performance characteristics.

If your application requires specific hardware features, you must request them explicitly in your job script (see Submitting jobs).

CPUs

All compute nodes have multiple CPUs (sockets), each with multiple cores. Most nodes support hyper-threading, which allows two threads per physical core. The number of cores per node is listed in the List of all nodes section.

Request CPUs based on how many threads your program can use. Oversubscribing doesn’t improve performance and may waste resources. Undersubscribing may slow your job due to thread contention.

To request CPUs for your jobs, see Job scripts.

GPUs

Many nodes in DAIC include one or more NVIDIA GPUs.GPU types differ in architecture, memory size, and compute capability. The table that follows summarizes the main GPU types in DAIC. For a per-node overview, see the List of all nodes section.

To request GPUs in your job, use --gres=gpu:<type>:<count>. See GPU jobs for more information.

In table 1: the headers denote:

Model

The official product name of the GPU

Architecture

The hardware design used in the GPU, which defines its specifications and performance characteristics. Each architecture (e.g., Ampere, Turing, Volta) represents a different GPU generation.

Compute capability

A version number indicating the features supported by the GPU, including CUDA support. Higher values offer more advanced functionality.

CUDA cores

The number of processing cores available on the GPU. More CUDA cores allow more parallel operations, improving performance for parallelizable workloads.

Memory

The total internal memory on the GPU. This memory is required to store data for GPU computations. If a model’s memory is insufficient, performance may be severely affected.

Memory

Each node has a fixed amount of RAM, shown in the List of all nodes section. Jobs may only use the memory explicitly requested using --mem or --mem-per-cpu. Exceeding the allocation may result in job failure.

Memory cannot be shared across nodes, and unused memory cannot be reallocated.

For memory-efficient jobs, consider tuning your requested memorey to match your code’s peak usage closely. Fore more information, see Slurm basics.

List of all nodes

The following table gives an overview of current nodes and their characteristics. Use the search bar to filter by hostname, GPU type, or any other column, and select columns to be visible.