(from https://www.rc.virginia.edu/userinfo/rivanna/queues/)
| Partition | Max time / job | Max nodes / job | Max cores / job | Max cores / node | Max memory / core | Max memory / node / job | SU Charge Rate |
|---|---|---|---|---|---|---|---|
standard | 7 days | 1 | 40 | 40 | 9GB | 375GB | 1.00 |
parallel | 3 days | 25 | 1000 | 40 | 9GB | 375GB | 1.00 |
largemem | 4 days | 1 | 16 | 16 | 60GB | 975GB | 1.00 |
gpu | 3 days | 4 | 10 | 10 | 32GB | 375GB | 3.00 * |
dev | 1 hour | 2 | 8 | 4 | 6GB | 36GB | 0.00 |
Sample slurm scripts: https://www.rc.virginia.edu/userinfo/rivanna/slurm/
Numba Information/Notes:
Multiprocessing Example
- Let's say we need to parallelize 10 parallel tasks in each job, and let's say we need to submit 20 jobs (so 200 tasks in total)
Main.py >>>def run_replica(i):
job_number = sys.argv[1]
replica_number = 10*int(sys.argv[1]) + iif __name__ == '__main__':
jobs = []
for i in range(10):
p = multiprocessing.Process(target=run_replica, args=(i,))
jobs.append(p)
p.start()
job.slurm >>>#!/bin/sh
#SBATCH --nodes=20
#SBATCH --ntasks-per-node=10
#SBATCH --time=10:00:00
#SBATCH --output=slurm.out
#SBATCH --error=slurm.err
#SBATCH --partition=parallel
#SBATCH -A spinquest_standard
#SBATCH --array=0-20
module load openmpi
srun python3 Main.py $SLURM_ARRAY_TASK_ID