Key changes:
- stress_test: use torch.cuda.mem_get_info() for free memory instead of total,
allocate 40% to avoid OOM when other processes occupy GPU memory
- benchmark: fix D2D efficiency by comparing to NVLink per-direction bandwidth
(not HBM), add H2D/D2H efficiency against PCIe peak
- nccl_test: implement direct binary → mpirun → torchrun fallback chain,
fix min_bw None bug when YAML value is empty
- report: update memory section to use per-metric peak fields
- install_deps.sh: add NCCL compatibility detection, enhance CUDA version
detection with CUDA_HOME/standard paths, improve _map_cuda_tag logging
- gpu_info: parse CUDA version from nvidia-smi header (query field removed
in newer drivers)
- health_check: parse throttle_reasons bitmask properly, ignore gpu_idle bit
- gpu_tester: fix suite summary to exclude metadata keys from pass count
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- Expand GPU specs database to include A100/A800 with Ampere architecture parameters
- Rename h200_tester.py to gpu_tester.py for architecture-neutral branding
- Add driver/CUDA compatibility validation per GPU generation
- Enhance report module with HTML and Markdown output formats
- Improve nvbandwidth binary discovery (system paths, DCGM locations)
- Add pyproject.toml with uv for dependency management
- Update install_deps.sh, configs, and README for multi-architecture support
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