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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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πŸ—οΈ Occupancy and Resource Optimization

In this section

Register Pressure Management#

Resource Allocation Strategy:#

🎯 Occupancy Factors:

Resource Limit per SM Impact on Occupancy
Registers 65,536 Primary bottleneck
Shared Memory 164KB Secondary bottleneck
Thread Blocks 32 Rarely limiting
Warps 64 Thread count dependent

βš–οΈ Register vs Performance Trade-offs:

High Register Usage (>63 regs/thread)
β”œβ”€β”€ Pros: Complex algorithms, reduced memory traffic
└── Cons: Low occupancy, poor latency hiding

Low Register Usage (<32 regs/thread)
β”œβ”€β”€ Pros: High occupancy, better throughput
└── Cons: More memory operations, potential spills

Dynamic Resource Allocation#

Adaptive Block Size Selection:#

πŸ“ Block Size Optimization Matrix:

Workload Type Optimal Block Size Occupancy Target Register Budget
Memory Bound 256-512 threads 75%+ <40 registers
Compute Bound 128-256 threads 50%+ <60 registers
Mixed Workload 256 threads 60%+ <50 registers
Section 3 of 7
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