CUDA ReLU Activation Optimization Guide

Introduction

ReLU (Rectified Linear Unit) is the most common activation function in deep learning. Despite its simplicity (max(0,x)), efficient CUDA implementation leverages vectorization and fusion with preceding operations for optimal performance.

Common Performance Issues

• Separate kernel launch overhead
• Not fused with GEMM/Conv
• Poor vectorization
• Unnecessary memory traffic

Optimization Techniques

__global__ void relu_vec4(float4* x, float4* y, int n) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (idx < n) {
        float4 v = x[idx];
        y[idx] = make_float4(fmaxf(0,v.x), fmaxf(0,v.y), fmaxf(0,v.z), fmaxf(0,v.w));
    }
}

Implementation Comparison

Before (Naive Implementation)

One element per thread.

__global__ void relu_naive(float* x, float* y, int n) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (idx < n) y[idx] = fmaxf(0, x[idx]);
}

After (Optimized Implementation)

Fuse ReLU into GEMM for zero overhead activation.

// In cuBLAS/CUTLASS GEMM epilogue:
// C = max(0, alpha*A*B + beta*C)
cublasLtMatmulDescSetAttribute(desc, CUBLASLT_MATMUL_DESC_EPILOGUE,
                                &CUBLASLT_EPILOGUE_RELU, sizeof(epilogue));

Performance Results

Frequently Asked Questions

Related Guides

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