zulifeng
375d439abb
- gpu_specs: 新增 H20/H20-3e (中国合规版 H200) 规格定义，并修复 GPU 名称匹配顺序，避免 "H200" 被 "H20" 子串误匹配 - benchmark(compute): 引入 L2 cache 规避的 matrix pool 轮换 + 可选 torch.compile(max-autotune)，FP8 增加 _scaled_mm 探测， 显著提升 FP16/BF16/FP8 实测吞吐准确性 - benchmark(memory): nvbandwidth 增加 --disableAffinity 规避 fabricmanager NVML 不兼容；全 0 结果时自动回退到 PyTorch； D2D 平均值排除对角线零值 - nccl: 各通信操作 (AllReduce/AllToAll/Broadcast 等) 使用独立 带宽阈值比例，避免 AllToAll 误报 WARN - rdma: 仅按 link_layer=InfiniBand 过滤端口，无 IB 硬件或全 DOWN 时直接 SKIP 而非报错 - stress: 计算矩阵尺寸封顶 4096，并改为先并发派发再统一同步， 修复 8 卡串行执行导致 duration 严重超时的问题 - report: 兼容 RDMA SKIP 状态与 PyTorch 回退场景的 Memory 判定， 避免回退结果被误判为 FAIL - config: 新增 benchmark.compute.use_compile 开关 Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
64 lines
1.1 KiB
YAML
64 lines
1.1 KiB
YAML
# GPU type: auto-detect or override to a100/a800/h100/h200/b200/b300
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gpu_type: auto
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benchmark:
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memory:
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size_mb: 4096
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iterations: 10
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nvbandwidth_buffer_mb: 512
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nvbandwidth_samples: 3
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compute:
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dtypes:
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- fp32
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- tf32
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- fp16
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- bf16
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- fp8
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matrix_size: 4096
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warmup: 10
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iterations: 100
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use_compile: false
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health:
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temp_warning: 80
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temp_critical: 90
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power_limit: null # null = auto-detect from GPU TDP per gpu_specs.py
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nccl:
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min_bandwidth_gbps: null # null = auto-detect (40% of GPU NVLink BW)
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test_allreduce: true
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test_alltoall: true
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test_broadcast: true
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test_reduce_scatter: false
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test_allgather: false
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test_sendrecv: false
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stress:
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duration_sec: 60
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use_doubles: false
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use_tensor_cores: true
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memory_pct: 90
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gpus: all
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rdma:
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min_bandwidth_gbps: 50
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max_latency_us: 10
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ib_iterations: 1000
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msg_size: 65536
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ib_device: null
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ib_port: 1
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training:
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model: gpt2
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batch_size: 8
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seq_length: 2048
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num_steps: 50
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dtype: bf16
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report:
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output_dir: ./reports
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format: json
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tools:
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install_dir: /opt/gpu-test-tools
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