NVIDIA Developer
Kernel Fusion in NVIDIA CUDA: Optimizing Memory Traffic and Launch Overhead
What happened
This NVIDIA technical post explains how kernel fusion optimizes GPU performance by combining multiple sequential operations into a single GPU kernel. This reduces memory traffic to high-bandwidth device memory and minimizes kernel launch overhead, addressing the bottleneck where GPU compute speed outpaces memory bandwidth.
Why it matters
It explains a foundational optimization technique used to accelerate deep learning inference and training engines.
The take
Crucial for low-level library developers (like those writing custom attention kernels or custom inference engines), but too low-level for the average LLM application developer.
Do this
Read if you are writing custom CUDA kernels or optimizing low-level inference engine performance.
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