CommFuse: Hiding Tail Latency via Communication Decomposition and Fusion for Distributed LLM Training

Researchers introduce CommFuse, a novel communication-computation overlap technique that eliminates tail latency in distributed LLM training by decomposing collective operations into peer-to-peer communications. The method improves efficiency for both tensor parallelism and data parallelism across GPU/TPU/NPU clusters, achieving higher throughput and model FLOPS utilization compared to existing solutions.

CommFuse addresses a critical bottleneck in distributed LLM training where communication overhead between accelerators significantly reduces computational efficiency. As language models scale to unprecedented sizes, workloads must partition across multiple processors, creating substantial data movement challenges that limit performance gains. This research tackles the specific problem of tail latency—where slower operations delay entire batches—by replacing traditional collective operations with optimized peer-to-peer communication patterns.

The technical innovation decomposes reduce-scatter and all-gather operations into finer-grained P2P communications, enabling deeper computational overlap. This approach matters because tail latency disproportionately impacts distributed systems where the slowest communication path determines overall performance. By enabling more granular scheduling of both communication and computation, CommFuse achieves an exact algorithm for reducing overhead that traditional data slicing methods cannot match. The solution's versatility across data parallelism and multiple tensor parallelism strategies increases its practical applicability.

For the AI infrastructure industry, this represents meaningful progress toward more efficient large-scale model training. Organizations running distributed LLM workloads could reduce training time and energy consumption, lowering operational costs and environmental impact. The improvements in Model FLOPS Utilization directly translate to better resource utilization on expensive hardware clusters. However, adoption depends on integration into existing distributed training frameworks and validation across diverse hardware configurations and model architectures.

• →CommFuse eliminates tail latency in distributed LLM training through decomposed peer-to-peer communication replacing conventional collective operations
• →The method achieves higher Model FLOPS Utilization and throughput compared to existing communication-computation overlap techniques
• →Solution supports multiple parallelism strategies including data parallelism and tensor parallelism variants, increasing implementation flexibility
• →Fine-grained computation scheduling enables deeper overlap between communication and computation phases
• →Improved training efficiency reduces operational costs and energy consumption for large-scale LLM deployments