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Automatically merge multiple CUDA kernels into optimized single-kernel implementations, reducing launch overhead and improving memory locality.
Kernel fusion combines multiple GPU kernels that execute sequentially into a single kernel, eliminating redundant memory transfers and kernel launch overhead. This is particularly effective for deep learning pipelines and iterative algorithms.
Automatic Detection: RightNow AI automatically identifies fusable kernel patterns in your codebase and suggests optimizations.
Kernel fusion adapts to your GPU architecture, optimizing for register count, shared memory capacity, and SM efficiency.
RightNow AI uses abstract syntax tree parsing to understand kernel semantics and dependencies:
Automated validation ensures fusion correctness:
// Kernel 1: Add bias
__global__ void add_bias(float* data, const float* bias, int n) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < n) {
data[idx] += bias[idx]; // Write to global memory
}
}
// Kernel 2: Apply ReLU activation
__global__ void relu(float* data, int n) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < n) {
data[idx] = fmaxf(0.0f, data[idx]); // Read from global memory
}
}
// Host code: Two kernel launches
add_bias<<<grid, block>>>(data, bias, n);
relu<<<grid, block>>>(data, n); // Launch overhead + memory round-trip// Fused kernel: Add bias + ReLU
__global__ void add_bias_relu_fused(float* data, const float* bias, int n) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < n) {
// Both operations in single kernel
float val = data[idx] + bias[idx];
data[idx] = fmaxf(0.0f, val); // No intermediate global memory write
}
}
// Host code: Single kernel launch
add_bias_relu_fused<<<grid, block>>>(data, bias, n); // 2x reduction in launch overheadPerformance Improvement: Fused kernel eliminates one kernel launch (5-10 μs) and one global memory round-trip, resulting in 1.5-2x speedup for small problem sizes.
Use the AI chat to request custom fusion:
You: "Fuse the normalize and scale kernels in model.cu"
RightNow AI: "I've analyzed the kernels and identified a fusion opportunity.
The fused kernel will:
- Eliminate 1 kernel launch overhead (~8 μs)
- Remove intermediate global memory write (reduces bandwidth by 25%)
- Improve L2 cache hit rate from 82% to 94%
Estimated speedup: 1.7x for batch size 256
Would you like me to generate the fused kernel code?"RightNow AI automatically validates fusion safety:
Kernel fusion is not beneficial in all scenarios:
Learn more: See Real-Time Profiling to measure fusion impact and AI Optimization for advanced fusion strategies.