During March 2026, ZZQ enhanced the PyTorch repository by improving profiling capabilities for distributed training workflows. They developed a feature in C++ that annotates symmetric memory CUDA operations with process group metadata, allowing profiler traces to correlate GPU kernel events with distributed communication context. This involved modifying the data model to store group names on allocation info and propagating metadata through various CUDA operations. ZZQ also created automated tests to validate metadata visibility under CPU and CUDA profiling. Leveraging skills in CUDA, distributed computing, and performance profiling, their work deepened observability and enabled more precise performance tuning for distributed workloads.