Kernel Fusion in NVIDIA CUDA Optimizes Memory Traffic and Launch Overhead
This article explains how kernel fusion in NVIDIA CUDA can improve GPU performance by enhancing memory bandwidth utilization and reducing kernel launch overhead, providing practical ways to apply these optimizations in CUDA code.
There are many ways to optimize code for GPUs. This post explains how kernel fusion can improve memory bandwidth and reduce kernel launch overhead, along with multiple ways to apply it in NVIDIA CUDA code. A common bottleneck in GPU programming is that GPU compute is so fast that even high-bandwidth device memory does not fully utilize the GPU kernel. Kernel fusion addresses this by combining kernels to optimize memory traffic and reduce launch overhead, thus improving performance and efficiency in GPU applications.