As training scales grow, collective communication libraries (CCL) increasingly face anomalies arising from complex interactions among hardware, software, and environmental factors. These anomalies typically manifest as slow/hang communication, the most frequent and time-consuming category to diagnose. However, traditional diagnostic methods remain inaccurate and inefficient, frequently requiring hours or even days for root cause analysis. To address this, we propose CCL-D, a high-precision diagnostic system designed to detect and locate slow/hang anomalies in large-scale distributed training. CCL-D integrates a rank-level real-time probe with an intelligent decision analyzer. The probe measures cross-layer anomaly metrics using a lightweight distributed tracing framework to monitor communication traffic. The analyzer performs automated anomaly detection and root-cause location, precisely identifying the faulty GPU rank. Deployed on a 4,000-GPU cluster over one year, CCL-D achieved near-complete coverage of known slow/hang anomalies and pinpointed affected ranks within 6 minutes—substantially outperforming existing solutions.

Lossless compression is a classic technique for reducing data storage and transmission requirements. Asymmetric Numeral Systems (ANS) is a high-throughput, high-ratio lossless compression algorithm, but it lacks effective support for multi-byte data and cross-platform compatibility. To address this issue, we propose an Adaptive Data Mapping (ADM) scheme, which maps multi-byte integer data into single-byte space based on the data’s characteristics, improving the compression ratio of ANS while maintaining low encoding redundancy. We also optimize the ADM algorithm and the ANS encoder for GPU and CPU architectures, respectively, and combine them to create an efficient and portable ANS encoding method for multi-byte integer data, called MANS. Experimental results show that MANS improves compression ratios by an average of 1.24 ×, achieves 870.27MB/s throughput on CPUs, and delivers up to 288.45 × and 135.86 × speedups on an NVIDIA A100 and an AMD MI210 GPU compared to the CPU version—demonstrating its efficiency and portability across platforms.