SLURM-native software GPU slicing for NVIDIA clusters using memory limits and compute time-slicing.
MPhil DiS and MPhil Economics and Data Science - Research Computing and Software Development
UAB Research Computing Documentation
A suite of bioinformatics data processing and analysis pipelines, software, and training resources for common methods.
The Multitenant Apps framework for Open OnDemand
quick lessons for reproducible research practices
🎶 A simple and elegant research computing orchestrator.
Reader for the Research Computing Starter Pack workshop series.
A science inference cluster — every model, from protein folding to LLMs, behind one OpenAI/Anthropic-compatible endpoint and a single key. Built to run beside HPC so agentic AI researchers can drive simulations and model inference together. Self-deploying on RKE2 with KServe/Knative + HAMi GPU.
Web-based Slurm job advisor — partition recommendations, queue pressure, pending job explainer, and GPU efficiency tracking for HPC clusters
Imaging Scripts, Terraform IAC, and Documentation for the RCC
Local MCP server, CLI, and agent skill for safe Slurm job management on ETH Zürich's Euler HPC cluster (free public share). Submit, monitor, cancel, and retrieve jobs; check quotas; transfer data. 48 cores / 128 GiB / no GPUs enforced.
Use Compute Canada CVMFS software (e.g. gcc, CP2K) inside GitHub Actions for CI/CD & testing
Open OnDemand on Eureka — Apache + OIDC + Let's Encrypt, pre-loaded with Jupyter, RStudio, VS Code, MATLAB, and ParaView. Reproducible deployment for the AMII × UAlberta PAICE cluster.
A Claude Code skill that teaches Claude to use the AAU AI Cloud GPU cluster (Slurm, Singularity, batch scripts, and policy-aware workflows).
UCSB PSTAT Department Computing Wiki
Deploy HubZero on AWS with Terraform or CDK — EC2, RDS, S3, ALB, WAF, CloudFront, EFS, CloudWatch on Amazon Linux 2023
Example from the computing workshop "Reproducible Research: Best Practices for R and HPC Clusters"
Automated pipeline that mirrors the Digital Research Alliance of Canada MediaWiki docs into a RAGFlow knowledge base, generates a MkDocs Material site via LLM, and exports thousands of Q&A pairs as fine-tuning data — hash-driven, multilingual, weekly scheduled.
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