3D Compute Manager Week 4: Storage Infrastructure & Advanced Job Scheduling
Distributed storage meets intelligent workload management
Distributed Storage That Just Works
Real GPU clusters need real storage infrastructure. You can't just assume infinite disk space or ignore what happens when nodes fail.
Cluster Storage Aggregation: Each cluster now tracks total storage capacity across all nodes. When you create datasets, they consume real storage space with actual limits. Try to cache more data than your cluster can handle, and the system will stop you - just like real hardware.
Ceph Integration Behind the Scenes: We're simulating a full Ceph distributed storage implementation. The system automatically pools NVMe drives from individual nodes into a resilient storage cluster with configurable erasure coding for redundancy.
Automatic Resilvering: When nodes join or leave a cluster, the storage system automatically rebalances data to maintain redundancy levels. Your jobs keep running during this process, though performance may be impacted - exactly like real distributed storage behavior.
Advanced Job Scheduling: Three Tiers
Most GPU scheduling systems force you to choose between fairness and efficiency. We've built something better with three distinct job types.
Dedicated Jobs: Run until completion, with subsequent jobs queuing behind them. Perfect for production training runs where you need guaranteed, uninterrupted compute time. Works like traditional SLURM clusters.
Time-Cycled Jobs: Jobs automatically pause and resume on a configurable schedule, allowing multiple workloads to share the same hardware fairly. Even large jobs are guaranteed compute time. Powered by our open-source cycling-utils library with atomic, corruption-resistant checkpoints.
Cycle Jobs: Launch in 10-15 seconds and run in 90-second increments for rapid testing. These ultra-high-priority jobs can interrupt lower-priority workloads for immediate testing, then release resources back to the queue.
Automatic Dependency Management
Dataset Auto-Caching: Jobs automatically trigger dataset downloads when scheduled. No manual pre-staging required - the system handles dependencies intelligently.
Container Snapshots: Jobs wait for required container images and datasets before entering the execution queue, preventing resource waste on incomplete jobs.
Storage-Aware Scheduling: The scheduler considers both compute and storage requirements, ensuring jobs only run when all dependencies can be satisfied.
Real-World Performance Alignment
Launch Times: Cycle and interruptible jobs start in 10-15 seconds on real clusters, enabling genuine rapid iteration at cluster scale.
Migration Speed: Cross-cluster job migration takes 5-15 minutes in production, powered by our 60GB/sec inter-cloud data transfer capabilities.
Business Logic Matching: The scheduling algorithms in the 3D Manager now match our production platform exactly. This isn't a demo approximation - it's the same decision-making logic that manages real enterprise GPU workloads.
Building Production Infrastructure
Storage Resilience: Distributed storage with automatic failure handling prevents data loss and maintains performance under various failure scenarios.
Workload Flexibility: Multiple scheduling paradigms let teams choose the right approach for different job types instead of forcing everything into a single queue model.
Development Velocity: Rapid testing capabilities eliminate the traditional bottleneck where infrastructure access limits iteration speed.
What's Next
Next week we're focusing on advanced visualization and management tools for large-scale deployments. When you're managing thousands of nodes, you need sophisticated filtering, grouping, and search capabilities to maintain operational efficiency.
We're also expanding the cost tracking system to provide job-level expense analysis and multi-dimensional cost breakdowns across providers, regions, and workload types.
Strong Compute provides visual GPU infrastructure management across all major cloud providers. Subscribe to words.strongcompute.com for weekly product updates and follow ourYouTube channel for video demos of new features.
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