CUDA Kernel Optimization: Advanced GPU Performance Engineering

Shared Memory Bank Conflict Elimination#

Matrix Transpose Optimization Pattern:#

📊 Memory Access Flow Diagram:

Global Memory → Shared Memory Tile → Transposed Output
     ↓               ↓                    ↓
  Coalesced      Bank Conflict         Coalesced
   Access        Elimination           Access

🔧 Key Optimization Strategy:

• Shared Memory Padding: Add +1 element to tile dimensions to eliminate bank conflicts
• Two-Phase Access: Separate coalesced read and write phases with synchronization
• Coordinate Transformation: Map input coordinates to transposed output coordinates

💡 Bank Conflict Prevention Techniques:

L2 Cache-Aware Access Patterns#

L2 Cache-Optimized Reduction Architecture:#

🏢 Memory Hierarchy Flow:

Global Memory (L2 Cache) → Vectorized Loads → Shared Memory → Warp Primitives
      ↓                      ↓                   ↓              ↓
 128-byte lines         4-element vectors    Tree reduction   Final reduction

⚡ Optimization Techniques:

• Vectorized Memory Access: Load 4 floats simultaneously when aligned
• Cache Line Awareness: Align memory accesses to 128-byte boundaries
• Hybrid Reduction: Combine shared memory tree reduction with warp primitives
• Stride Optimization: Use power-of-2 strides for conflict-free access

📈 Performance Characteristics:

• Memory Bandwidth: 90%+ theoretical peak through vectorization
• Reduction Efficiency: O(log n) complexity with minimal synchronization
• Warp Utilization: 100% active threads during critical reduction phases