Anatomy of a high-performance EP kernel (fergusfinn.com)

A new analysis on high-performance expert parallelism (EP) kernels for large language models (LLMs) has been unveiled, highlighting the intricacies of optimizing GPU communication during inference. The discussion centers on "wide Expert Parallelism" (wideEP) as a superior approach for managing model inference at scale, specifically in mixture of experts (MoE) architectures. Unlike fixed architecture communication patterns seen in Tensor or Pipeline Parallelism, wideEP's dynamic routing determines which tokens go to which GPUs at runtime, creating challenges in GPU communication that the DeepEP library aims to address. This kernel architecture significantly boosts throughput by innovating how data is allocated and communicated across GPUs, adapting in real time to the variable demands of token routing. Two main strategies are proposed for optimizing communication: a throughput-optimized approach that efficiently gathers and compresses data for processing, and a latency-optimized route that minimizes overhead during time-sensitive decoding phases. By enhancing the efficiency and speed of LLM inference, these advancements hold the potential to transform large-scale AI deployments, ultimately making powerful models more accessible and responsive in practical applications.