Megakernels on Mac: What Fusion Saves and What It Costs (abhishek.it)

A recent effort to run a 27-billion-parameter model on Mac using a megakernel approach aimed for high efficiency, achieving speeds of 200 tokens per second for extensive coding tasks. Despite the innovative design intended to optimize GPU performance by merging operations, the prototype ultimately proved to be 2.7% slower than separate optimized kernels. This outcome highlights the challenges inherent in maximizing GPU bandwidth and efficiently managing operational dependencies within machine learning models. By combining multiple operations into a single GPU kernel, developers intended to streamline memory usage and reduce latency. However, the research revealed significant variations in memory access speeds for different operations, which complicated the expected benefits of fusion. Overall, while the project achieved a slight improvement over its prior iteration, it illustrates the complexities of kernel scheduling and memory management in high-performance computing environments. The findings emphasize the need for ongoing exploration into optimization techniques that balance fusion benefits with the unique operational requirements of various model components.