# GPU type: auto-detect or override to a100/a800/h100/h800/h200/h20/b200/b300
gpu_type: auto

benchmark:
  memory:
    size_mb: 4096
    iterations: 10
    nvbandwidth_buffer_mb: 512
    nvbandwidth_samples: 3
  compute:
    dtypes:
      - fp32
      - tf32
      - fp16
      - bf16
      - fp8
    # MAMF-style shape sweep: measure each dtype at every shape below and keep the max
    # TFLOPS (the realistic achievable peak). A single fixed shape under-reports by
    # ~7-12% and can't meet the MAMF-calibrated thresholds in gpu_specs.py.
    # Each entry is either N (square N×N×N) or [M, N, K]. K-heavy non-square shapes
    # (e.g. 2048×2048×13312) hit the true Hopper MAMF — bf16 ~790 vs ~755 square.
    # Empty list => single matrix_size shape (legacy behaviour).
    sweep_sizes:
      - 3584
      - 4608
      - 5376
      - 8192
      - 11520
      - [2048, 2048, 13312]
      - [2048, 2048, 16384]
    matrix_size: 8192        # fallback shape when sweep_sizes is empty
    warmup: 20
    iterations: 80
    # NOTE: torch.compile was dropped — on H100 eager cuBLAS beats Triton for plain
    # GEMM, and compiling would re-autotune per shape and make the sweep very slow.

health:
  temp_warning: 75
  temp_critical: 85
  power_limit: null  # null = auto-detect from GPU TDP per gpu_specs.py

nccl:
  min_bandwidth_gbps: null  # null = auto-detect (40% of GPU NVLink BW)
  test_allreduce: true
  test_alltoall: true
  test_broadcast: true
  test_reduce_scatter: false
  test_allgather: false
  test_sendrecv: false

stress:
  duration_sec: 600           # 10 min — reaches thermal steady state, validates throttle/jitter beyond warmup
  use_doubles: false
  use_tensor_cores: true
  memory_pct: 90
  gpus: all

rdma:
  min_bandwidth_gbps: 50
  max_latency_us: 10
  ib_iterations: 1000
  msg_size: 65536
  ib_device: null
  ib_port: 1

training:
  model: gpt2
  batch_size: 8
  seq_length: 2048
  num_steps: 50
  dtype: bf16

report:
  output_dir: ./reports
  format: json

tools:
  install_dir: /opt/gpu-test-tools