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CUDA Kernel Optimization: Advanced GPU Performance Engineering

Master advanced CUDA kernel optimization techniques for high-performance GPU computing, covering memory patterns, warp efficiency, occupancy optimization, and cutting-edge performance profiling.

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🎯 Production Optimization Techniques

In this section

Multi-GPU Scaling Patterns#

Distributed Computing Architecture:#

🏒 Scaling Strategies:

Pattern Communication Efficiency Complexity
Data Parallel Minimal 90%+ Low
Model Parallel Heavy 60-80% High
Pipeline Parallel Moderate 70-85% Medium
Hybrid Approach Mixed 85%+ Very High

Real-time Performance Monitoring#

Production Monitoring Framework:#

πŸ“Š Key Performance Metrics:

  • Kernel Launch Overhead: <10ΞΌs target
  • Memory Transfer Efficiency: >80% peak bandwidth
  • Compute Utilization: >70% theoretical peak
  • Power Efficiency: Performance per watt optimization

πŸ”§ Optimization Maintenance:

  • Performance Regression Testing: Automated benchmarking
  • Hardware-Specific Tuning: Architecture-aware optimization
  • Workload Adaptation: Dynamic performance scaling
  • Continuous Profiling: Production performance monitoring
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