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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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πŸš€ Advanced Performance Profiling

In this section

Nsight Compute Analysis Workflow#

Profiling Pipeline:#

Kernel Launch β†’ Hardware Counters β†’ Bottleneck Analysis β†’ Optimization
     ↓               ↓                    ↓                  ↓
  Profile Data   Performance Metrics   Root Cause      Implementation

πŸ” Key Performance Indicators:

Metric Category Primary Indicators Optimization Focus
Memory L1/L2 hit rates, bandwidth utilization Access patterns
Compute ALU utilization, instruction throughput Algorithm efficiency
Control Flow Branch divergence, predication efficiency Conditional logic
Occupancy Active warps, register usage Resource allocation

Roofline Model Application#

Performance Boundaries Visualization:#

πŸ“Š Roofline Analysis Framework:

  • Peak Performance Line: Maximum computational throughput
  • Memory Bandwidth Ceiling: Data transfer limitations
  • Operational Intensity: Compute-to-memory ratio
  • Performance Optimization Path: Route to peak efficiency

🎯 Optimization Targets by Arithmetic Intensity:

Intensity Range Bottleneck Optimization Strategy
< 1 FLOPs/Byte Memory Bound Cache optimization, vectorization
1-10 FLOPs/Byte Balanced Mixed optimization approach
> 10 FLOPs/Byte Compute Bound ALU utilization, instruction-level parallelism
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