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Computer Science
Containerization and Orchestration
GPU Scheduling and Resource Management in Containerized Environments
1. Foundational Concepts
2. GPU Hardware Integration
3. Core Mechanisms for GPU Management in Kubernetes
4. GPU Allocation and Sharing Strategies
5. Advanced GPU Scheduling
6. Monitoring and Observability
7. Ecosystem and Tooling
8. Security and Compliance
9. Performance Optimization
10. Challenges and Future Directions
Advanced GPU Scheduling
Kubernetes Scheduler Limitations
Default Scheduler Constraints
Topology Unawareness
Single-Pod Scheduling
Limited Resource Types
GPU-Specific Challenges
Interconnect Topology
Memory Locality
Batch Job Requirements
Topology-Aware Scheduling
GPU Interconnect Technologies
NVLink Architecture
PCIe Topology
InfiniBand Integration
Network Fabric Considerations
NUMA Awareness
CPU-GPU Affinity
Memory Locality
Performance Optimization
Topology Discovery
Hardware Topology Detection
Node Labeling Strategies
Scheduler Integration
Placement Algorithms
Locality-Aware Placement
Bandwidth Optimization
Latency Minimization
Gang Scheduling
Distributed Training Requirements
All-or-Nothing Allocation
Synchronous Execution
Deadlock Prevention
Gang Scheduling Algorithms
Coscheduling Strategies
Resource Reservation
Backfilling Techniques
Implementation Approaches
Volcano Scheduler
Yunikorn Scheduler
Custom Scheduler Extensions
Multi-Tenant Scheduling
Fair-Share Scheduling
Weighted Fair Queuing
Proportional Share
Deficit Round Robin
Priority-Based Scheduling
Priority Classes
Preemption Policies
Priority Inheritance
Quota Management
Resource Quotas
Namespace Isolation
User-Based Quotas
Batch and HPC Scheduling
Job Queue Management
Priority Queues
FIFO Scheduling
Shortest Job First
Resource Packing
Bin Packing Algorithms
Fragmentation Reduction
Utilization Optimization
Backfill Scheduling
Conservative Backfill
Aggressive Backfill
EASY Backfill
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4. GPU Allocation and Sharing Strategies
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6. Monitoring and Observability