Physics MCP Server

--- title: Distributed Computing & Collaboration Tools kind: reference header_svg: src: "/assets/svg/tool-distributed-hero.svg" static: "/assets/svg/tool-distributed-hero-static.svg" title: "Distributed Computing & Collaboration Tools" animate: true theme_variant: "auto" reduced_motion: "auto" --- {% assign header_svg = page.header_svg %} {% include header-svg.html %} # Distributed Computing & Collaboration Tools The Distributed Computing tool enables collaborative physics research and education by providing remote job submission, session sharing, lab notebook capabilities, and artifact versioning with full provenance tracking. ## Core Capabilities ### Remote Job Submission - **Slurm Integration**: Submit jobs to HPC clusters - **Kubernetes Support**: Run jobs on cloud computing platforms - **Job Monitoring**: Real-time status updates and log streaming - **Artifact Retrieval**: Automatic download of results and outputs ### Session Sharing - **Multi-User Access**: Share analysis sessions with collaborators - **Role Management**: Control read/write permissions - **Expiring Links**: Time-limited access for security - **Real-time Collaboration**: Multiple users working simultaneously ### Lab Notebook - **Signed Entries**: Cryptographically signed notebook entries - **Tool Provenance**: Track which tools were used for each result - **Artifact Thumbnails**: Visual previews of generated content - **Version Control**: Complete history of all changes ### Artifact Versioning - **Content Addressing**: SHA-256 hashes for all artifacts - **Lineage Tracking**: Complete dependency chains - **Git Integration**: Version control for all generated content - **Reproducibility**: Recreate any previous state exactly ## Usage Examples ### Submit Remote Job ```json { "tool": "distributed_collaboration", "params": { "action": "job_submit", "job_type": "slurm", "script": "#!/bin/bash\n#SBATCH --job-name=physics_analysis\npython analyze_data.py", "resources": { "nodes": 2, "cpus_per_node": 16, "memory": "32GB", "time": "2:00:00" }, "artifacts": ["input_data.csv", "analysis_script.py"] } } ``` ### Share Analysis Session ```json { "tool": "distributed_collaboration", "params": { "action": "session_share", "session_id": "physics_lab_2024", "participants": [ { "email": "student@university.edu", "role": "read_write" }, { "email": "professor@university.edu", "role": "admin" } ], "expires": "2024-12-31T23:59:59Z" } } ``` ### Create Lab Notebook Entry ```json { "tool": "distributed_collaboration", "params": { "action": "lab_notebook", "entry": { "title": "Quantum Oscillator Analysis", "content": "Analyzed the energy levels of a quantum harmonic oscillator", "tools_used": ["cas", "plot", "quantum"], "artifacts": ["energy_levels.png", "wavefunctions.svg"], "conclusions": "Energy levels follow E_n = (n + 1/2)ℏω" }, "signature": "professor_digital_signature" } } ``` ### Version Artifacts ```json { "tool": "distributed_collaboration", "params": { "action": "artifact_versioning", "artifact_path": "results/analysis_2024_01_15.png", "metadata": { "experiment": "quantum_oscillator", "parameters": {"mass": 1.0, "frequency": 2.0}, "tools_used": ["plot", "quantum"], "commit_hash": "abc123def456" }, "lineage": ["raw_data.csv", "processed_data.h5"] } } ``` ## Educational Applications ### Collaborative Learning - **Group Projects**: Students can work together on complex analyses - **Peer Review**: Students can review and comment on each other's work - **Mentor Access**: Professors can guide students in real-time - **Knowledge Sharing**: Best practices and solutions shared across the class ### Research Collaboration - **Multi-Institution**: Researchers from different universities can collaborate - **Data Sharing**: Secure sharing of experimental data and results - **Reproducibility**: All analyses can be exactly reproduced by others - **Publication Support**: Complete provenance for published results ### Remote Learning - **Virtual Labs**: Students can access powerful computing resources remotely - **Asynchronous Work**: Students can work on analyses at their own pace - **Resource Sharing**: Efficient use of institutional computing resources - **Accessibility**: Equal access to computing resources for all students ## Advanced Features ### Job Orchestration ```json { "tool": "distributed_collaboration", "params": { "action": "job_submit", "workflow": [ { "step": "data_preprocessing", "script": "preprocess.py", "dependencies": ["raw_data.csv"] }, { "step": "analysis", "script": "analyze.py", "dependencies": ["preprocessed_data.h5"] }, { "step": "visualization", "script": "plot_results.py", "dependencies": ["analysis_results.json"] } ] } } ``` ### Real-time Collaboration ```json { "tool": "distributed_collaboration", "params": { "action": "session_share", "real_time": true, "features": { "live_cursor": true, "voice_chat": true, "screen_sharing": true } } } ``` ### Automated Backup ```json { "tool": "distributed_collaboration", "params": { "action": "artifact_versioning", "auto_backup": true, "backup_schedule": "daily", "retention_policy": "30_days" } } ``` ## Security and Privacy ### Access Control - **Authentication**: Multi-factor authentication support - **Authorization**: Role-based access control - **Audit Logs**: Complete record of all access and changes - **Encryption**: All data encrypted in transit and at rest ### Data Protection - **Privacy**: Sensitive data can be kept private - **Compliance**: Meet institutional and regulatory requirements - **Backup**: Automated backup and disaster recovery - **Version Control**: Complete history of all changes ## Integration with Other Tools ### Complete Research Workflow ```json { "tool": "experiment_orchestrator", "params": { "dag": [ { "tool": "distributed_collaboration", "action": "job_submit", "script": "collect_data.py" }, { "tool": "data", "action": "import_hdf5", "file": "collected_data.h5" }, { "tool": "ml_ai_augmentation", "action": "symbolic_regression_train" }, { "tool": "distributed_collaboration", "action": "artifact_versioning", "results": "from_previous_step" } ] } } ``` ### Publication Pipeline ```json { "tool": "export_tool", "params": { "export_type": "overleaf", "artifacts": "from_distributed_collaboration", "provenance": "included" } } ``` ## Performance Considerations ### Resource Management - **Queue Management**: Intelligent job queuing and scheduling - **Load Balancing**: Distribute work across available resources - **Resource Monitoring**: Real-time monitoring of system resources - **Cost Optimization**: Minimize compute costs while meeting deadlines ### Network Optimization - **Data Compression**: Compress large datasets for transfer - **Incremental Sync**: Only transfer changed data - **CDN Integration**: Use content delivery networks for global access - **Bandwidth Management**: Throttle transfers to avoid network congestion ## Best Practices ### Collaboration - **Clear Communication**: Document all changes and decisions - **Regular Backups**: Ensure important work is always backed up - **Version Control**: Use meaningful commit messages - **Access Management**: Regularly review and update access permissions ### Resource Usage - **Efficient Scripts**: Write efficient code to minimize resource usage - **Resource Requests**: Request appropriate resources for your jobs - **Cleanup**: Clean up temporary files and unused resources - **Monitoring**: Monitor job progress and resource usage ### Security - **Strong Authentication**: Use strong passwords and multi-factor authentication - **Regular Updates**: Keep software and systems up to date - **Access Reviews**: Regularly review who has access to what - **Incident Response**: Have a plan for handling security incidents