Ada-MK: Adaptive MegaKernel Optimization via DAG-Based Search for LLM Inference (arxiv.org)

Researchers have introduced Ada-MK, a novel approach to optimizing MegaKernel execution in large language model (LLM) inference, specifically tailored for low-latency applications like online advertising. This groundbreaking method addresses the critical problem of kernel launch overhead, which can account for 14.6% of end-to-end inference time. By using a three-dimensional shared-memory constraint model and employing an MLIR-based fine-grained Directed Acyclic Graph (DAG) offline search, Ada-MK solves the tension between portability and efficiency on resource-constrained GPUs, eliminating runtime branching and optimizing execution paths. This development is significant for the AI/ML community as it represents the first industrial deployment of MegaKernel in a commercial online advertising system, achieving up to 23.6% higher single-batch throughput compared to standard TensorRT-LLM and 50.2% over vLLM. By embedding MegaKernel as a plugin within TensorRT-LLM, Ada-MK effectively bridges the gap between high-throughput prefill and low-latency decoding. Such advancements could pave the way for further optimizations in LLMs and enhance real-time performance across various applications, making it a crucial step toward more efficient AI implementations.