Tools-CC-Bench: a Benchmark Suite for Collective Communication with Compression in HPC and AI Workloads
Haozhe Fan Wei Wang Xingchen Liu Man Liu Xingjian Tian Haoquan Long Zedong Liu Daran Sun Jinwu Yang Bo Yang Jie Liu Yonggang Che Hairui Zhao Guangming Tan Dingwen Tao
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Abstract
Distributed HPC and LLM workloads increasingly require efficient communication for scalability, yet growing data movement has become a major performance bottleneck. Communication compression can reduce this overhead and complement execution-level optimizations, but its benefits remain difficult to assess because existing benchmarks lack support for diverse backends, realistic datasets, application-specific accuracy metrics, and overlap-induced resource contention. We present CC-Bench, a lightweight, extensible, and application-oriented benchmark suite for evaluating communication compression under realistic execution conditions. CC-Bench uses declarative application-environment modeling to decouple profiling logic from communication libraries, datasets, and fidelity metrics, enabling portable cross-library evaluation. It further combines function-level interception and hardware counter monitoring to characterize per-phase latency, hardware utilization, numerical fidelity, and computation interference. With representative datasets from HPC and LLM workloads, CC-Bench evaluates three compression-enabled communication libraries on CPU and GPU clusters, revealing accuracy-performance trade-offs and bottlenecks to guide practical deployment and optimization.引用本文复制引用
Haozhe Fan,Wei Wang,Xingchen Liu,Man Liu,Xingjian Tian,Haoquan Long,Zedong Liu,Daran Sun,Jinwu Yang,Bo Yang,Jie Liu,Yonggang Che,Hairui Zhao,Guangming Tan,Dingwen Tao.Tools-CC-Bench: a Benchmark Suite for Collective Communication with Compression in HPC and AI Workloads[EB/OL].(2026-09-13)[2026-09-21].https://arxiv.org/abs/2609.08739.学科分类
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