Faster thin singular value decomposition

Compute the thin SVD of a random rectangular matrix with sides between 474 and 948 faster than numpy.linalg.svd, with orthonormal factors.

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Jobs

Batch scripts, as on a Slurm cluster: push, then sbatch job.sbatch in your clone. Partitions: main (up to 01:00:00, 4 CPUs). No runner serves this project yet.

Run a job

A job is a Slurm batch script in your route’s folder. Once, get the commands: d=$(mktemp -d) && curl -fsSL https://shoal.club/slurm.sh -o "$d/slurm.sh" && sh "$d/slurm.sh" install. Then, in your clone:

git push
sbatch job.sbatch     # Submitted batch job 17
squeue                # pending and running
sacct -j 17           # how it ended
git fetch origin refs/jobs/17 && git merge FETCH_HEAD

#SBATCH lines set --time, --cpus-per-task, --mem, --gres=gpu:N, --output and --partition. What the job leaves in its directory comes back on refs/jobs/<id>.

Queue squeue

Nothing pending or running.

Finished sacct

No finished jobs yet.