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name: cuda-optimization description: Optimize CUDA kernels and GPU code for performance. Use when reviewing CUDA code, analyzing performance, or suggesting GPU optimizations. version: 1.0.0

CUDA Optimization Skill

This skill helps optimize CUDA and GPU code for better performance.

When I activate

I automatically activate when you:

• Review CUDA kernel code (.cu files)
• Ask about GPU performance or optimization
• Mention memory coalescing, occupancy, or shared memory
• Request profiling analysis or bottleneck identification
• Discuss parallel algorithm efficiency

What I do

Performance Analysis

I analyze CUDA code for:

• Memory access patterns: Detect uncoalesced accesses, bank conflicts
• Thread configuration: Evaluate block size, grid size, occupancy
• Synchronization: Check for unnecessary synchronization points
• Memory hierarchy: Assess use of shared memory, constant memory, texture memory
• Warp efficiency: Identify divergence and suboptimal thread utilization

Optimization Suggestions

I provide specific recommendations:

• Coalescing memory accesses
• Optimal thread block configurations
• Shared memory usage patterns
• Reduction strategies
• Stream parallelism opportunities
• Compute vs memory-bound analysis

Code Examples

I show before/after code examples demonstrating:

Copy// ❌ Uncoalesced access
__global__ void slow(float* data, int stride) {
    int idx = threadIdx.x + blockIdx.x * blockDim.x;
    data[idx * stride] = idx;  // Poor pattern
}

// ✅ Coalesced access
__global__ void fast(float* data) {
    int idx = threadIdx.x + blockIdx.x * blockDim.x;
    data[idx] = idx;  // Sequential pattern
}

Tools I use

I leverage:

• NVIDIA Nsight Copilot (GPT-OSS-120B) for deep CUDA expertise
• Static code analysis for pattern detection
• Architecture-specific optimization knowledge (Ampere, Hopper, Ada)
• Best practices from CUDA Programming Guide

Output format

My suggestions include:

1. Issue identification with line numbers
2. Performance impact estimate (low/medium/high)
3. Specific fix with code example
4. Architecture notes if relevant

Focus on actionable, measurable improvements.