Kernel Fusion in NVIDIA CUDA: Boosting Performance

Optimizing code for GPUs can significantly enhance performance, and one effective technique is kernel fusion. This method can improve memory bandwidth and minimize the overhead associated with launching multiple kernels. In this post, we will delve into how kernel fusion operates within NVIDIA CUDA, along with strategies for its implementation.

Understanding Kernel Fusion

A common challenge in GPU programming arises from the speed of GPU computations. Despite high-bandwidth device memory, the GPU kernel may not be fully utilized. Kernel fusion addresses this inefficiency by merging multiple GPU operations into a single device kernel. By doing so, intermediate results can be stored in registers, eliminating the need for them to travel through global memory or requiring separate kernel launches.

The Role of CUDA Graphs

CUDA Graphs provide another layer of optimization, capturing a sequence of kernel launches, memory copies, and synchronizations into a single reusable object. This allows the host to dispatch the entire sequence with one call. However, it's important to note that while CUDA Graphs improve host-side performance, they do not merge kernel bodies, which means that kernels within a graph still operate independently and pass intermediate results through global memory.

Combining Approaches for Maximum Efficiency

Both kernel fusion and CUDA Graphs can be utilized together for enhanced performance. For instance, consider a simple operation: summing the absolute values of an array. A basic implementation might use two separate kernels: one for computing the absolute values and another for calculating the sum. While this is functional, it is not optimal.

Implementing Kernel Fusion

To optimize the operation, we can rewrite the two kernels into a single kernel, effectively creating a combined sum_abs_kernel. This approach removes the need for an intermediate buffer, which was previously necessary to transfer data between the two separate kernels.

template <typename Config>
__global__ void sum_abs_kernel(Config config, cuda::std::span<const float> x, cuda::std::span<float> out) {
    using BlockReduce = cub::BlockReduce<float, BLOCK_THREADS>;
    __shared__ typename BlockReduce::TempStorage temp_storage;
    float thread_sum = 0.0f;
    const auto tid = cuda::gpu_thread.rank(cuda::grid, config);
    const auto stride = cuda::gpu_thread.count(cuda::grid, config);
    for (size_t i = tid; i < x.size(); i += stride) {
        thread_sum += fabsf(x[i]);
    }
    float block_sum = BlockReduce(temp_storage).Sum(thread_sum);
    if (x == 0) {
        cuda::atomic_ref<float, cuda::thread_scope_device> r(out[0]);
        r.fetch_add(block_sum, cuda::memory_order_relaxed);
    }
}

Benefits of Kernel Fusion

When implemented correctly, kernel fusion leads to significant performance improvements. In our example, the computation of the absolute value is done inline, utilizing registers instead of reading from an intermediate buffer. This not only speeds up the operation but also minimizes memory access, which is a crucial factor in achieving optimal performance on GPUs.

Conclusion

In summary, kernel fusion is a powerful technique for optimizing CUDA code by reducing memory traffic and launch overhead. By merging operations into single kernels, developers can achieve better performance and utilize GPU resources more effectively. When combined with CUDA Graphs, the potential for efficiency is further enhanced, allowing for more complex operations to be executed swiftly.

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