GPU Scheduling and Resource Management in Containerized Environments

9.

Performance Optimization

9.1.

GPU Workload Optimization

9.1.1.

Memory Access Patterns

9.1.1.1.

Coalesced Memory Access

9.1.1.2.

Memory Bandwidth Utilization

9.1.1.3.

Cache Optimization

9.1.2.

Compute Optimization

9.1.2.1.

Occupancy Maximization

9.1.2.2.

Warp Efficiency

9.1.2.3.

Instruction Throughput

9.1.3.

Multi-GPU Scaling

9.1.3.1.

Data Parallelism

9.1.3.2.

Model Parallelism

9.1.3.3.

Pipeline Parallelism

9.2.

Container Performance

9.2.1.

Resource Right-Sizing

9.2.1.1.

9.2.1.2.

Memory Allocation

9.2.1.3.

I/O Optimization

9.2.2.

Container Overhead

9.2.2.1.

Runtime Overhead

9.2.2.2.

Networking Overhead

9.2.2.3.

Storage Overhead

9.2.3.

Placement Optimization

9.2.3.1.

9.2.3.2.

9.2.3.3.

Anti-Affinity Policies

9.3.

Cluster-Level Optimization

9.3.1.

Resource Utilization

9.3.1.1.

Bin Packing Efficiency

9.3.1.2.

Fragmentation Reduction

9.3.1.3.

9.3.2.

Scheduling Optimization

9.3.2.1.

Queue Management

9.3.2.2.

Priority Tuning

9.3.2.3.

Preemption Strategies

9.3.3.

9.3.3.1.

Horizontal Pod Autoscaling

9.3.3.2.

Vertical Pod Autoscaling

9.3.3.3.

Cluster Autoscaling

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10. Challenges and Future Directions