Gang scheduling is the allocation model requiring all requested resources for a distributed job to be available simultaneously before start - it avoids deadlock and partial-allocation failures in tightly coupled multi-GPU training workloads.

What Is Gang scheduling?

• Definition: All-or-nothing launch policy for jobs that need complete synchronized resource sets.
• Distributed Need: Many training frameworks cannot progress if only a subset of ranks starts.
• Queue Behavior: Large gang jobs may wait longer but run correctly once admitted.
• Failure Prevention: Prevents resource fragmentation and partial-start deadlocks.

Why Gang scheduling Matters

• Correctness: Ensures distributed process groups initialize with full world size as required.
• Scheduler Efficiency: Avoids wasting resources on jobs that cannot make progress.
• Operational Reliability: Reduces partial allocation churn and repeated launch failures.
• Predictable Runtime: Once started, gang jobs are less likely to stall due to missing peers.
• Scale Readiness: Essential for stable operation of large synchronized training jobs.

How It Is Used in Practice

• Admission Control: Reserve full requested resource bundle before signaling job start.
• Fragmentation Management: Use backfill and packing strategies to reduce long wait times for gang jobs.
• Queue Policy: Separate large gang queues from small jobs to improve overall fairness and utilization.

Gang scheduling is a necessary orchestration policy for synchronized distributed training - full-cohort admission prevents partial-start failures and improves large-job reliability.