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CPU Jobs for Data Analysis and Processing

The cpu partition provides high-memory nodes for data analysis, preprocessing, and workflow orchestration. These nodes do not have GPUs — use them for work that benefits from many CPU cores and large memory rather than GPU acceleration.

CPU Jobs on AICR

AICR is not meant for CPU workloads. A small CPU-only partition is provided as a convenience for small data analysis tasks related to your GPU jobs. Heavy CPU workloads should be done on your home institution's cluster.

When to Use the CPU Partition

  • Data preprocessing: cleaning, transforming, or merging large datasets before GPU training
  • Post-processing: aggregating results, generating reports, statistical analysis
  • Workflow orchestration: coordinating multi-step pipelines that launch GPU jobs

For GPU-accelerated work (training, inference, anything using CUDA), use the rtx-* or b200-* partitions instead. See GPU Jobs for more information.

CPU Partition Specs

Property Value
Nodes AMD EPYC 9745 (5 nodes)
Cores per node 128
Memory per node 1 TB
GPUs None
Max wall time 24 hours
Default time 15 minutes

Example: Basic CPU Job

The following is an example job script for a CPU job with 16 cores and 32GB of RAM.

#!/bin/bash
#SBATCH --job-name=data_analysis
#SBATCH --partition=cpu
#SBATCH --nodes=1
#SBATCH --cpus-per-task=16
#SBATCH --mem=32G
#SBATCH --time=04:00:00
#SBATCH --output=%x-%j.out

module load miniforge3

python analyze_data.py

See Also