帮大模型提速80%，华为拿出昇腾推理杀手锏FlashComm，三招搞定通算瓶颈

Core Viewpoint - The article discusses the optimization of large model inference communication through Huawei's FlashComm technology, addressing the challenges posed by the exponential growth of model parameters and the need for efficient communication strategies in distributed computing environments [2][6][17]. Group 1: Communication Challenges - The rapid increase in the scale of clusters and inference concurrency in large language models has led to significant communication pressures, particularly with the expansion of Mixture of Experts (MoE) models, where the number of experts and total parameters grows exponentially [6][18]. - Traditional communication strategies, such as AllReduce, face limitations in high concurrency scenarios, leading to increased end-to-end inference latency due to bandwidth constraints [6][8]. Group 2: FlashComm Innovations - FlashComm1 optimizes AllReduce communication by decomposing it into ReduceScatter and AllGather operations, resulting in a 26% performance improvement in inference [7][11]. - FlashComm2 redefines the balance between computation and communication by transforming three-dimensional tensors into two-dimensional matrices, achieving a 33% increase in overall inference speed [7][14]. - FlashComm3 leverages multi-stream parallelism to enhance the efficiency of MoE model inference, resulting in a throughput increase of 25%-30% during the decoding phase [7][15]. Group 3: Future Directions - The Huawei team aims to continue innovating in areas such as multi-stream parallelism, automatic weight prefetching, and model parallelism to further enhance the performance of large model inference systems [17][18].