Advanced CUDA Optimization Guide

This guide covers advanced CUDA optimization techniques with interactive examples.

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Memory Coalescing

Memory coalescing is crucial for GPU performance. Here's a comparison:

CUDA

// Bad: Non-coalesced access
__global__ void badKernel(float* data) {
  int idx = blockIdx.x * blockDim.x + threadIdx.x;
  // Strided access pattern - BAD!
  data[idx * 32] = idx;
}

// Good: Coalesced access
__global__ void goodKernel(float* data) {
  int idx = blockIdx.x * blockDim.x + threadIdx.x;
  // Sequential access pattern - GOOD!
  data[idx] = idx;
}

Performance Tip

Always ensure consecutive threads access consecutive memory locations for optimal memory throughput.

Benchmark Results

Here's a comparison of different memory access patterns:

Memory Access Pattern Performance

Coalesced Access

2.1ms10x faster

Strided Access (stride=2)

8.4ms2.5x faster

Random Access

21.2msbaseline

Shared Memory Optimization

Be careful of bank conflicts when using shared memory!

__shared__ float sharedData[256];

// Avoid bank conflicts
int tid = threadIdx.x;
sharedData[tid] = globalData[tid];
__syncthreads();

Using shared memory effectively can provide up to 100x speedup over global memory access!

Key Takeaways

Memory coalescing is the #1 optimization
Shared memory reduces global memory access
Bank conflicts can hurt shared memory performance
Always profile your code with nsight

Performance Tip

Start with coalesced memory access, then add shared memory optimizations for maximum impact.