gpu-optimizer

GPU Optimizer

Expert GPU optimization for consumer GPUs with 8–24GB VRAM. Evidence-based patterns only.

Hardware Profile

Fill in your hardware before applying optimizations:

Key constraint: VRAM capacity determines which strategies apply. Patterns below are annotated with minimum VRAM requirements where relevant.

Optimization Categories

1. XGBoost GPU Acceleration

DMatrix vs QuantileDMatrix:

# GPU-optimized: QuantileDMatrix is 1.8x faster
dtrain = xgb.QuantileDMatrix(X_train.astype(np.float32))
dval = xgb.QuantileDMatrix(X_val.astype(np.float32))

# Standard: DMatrix (use for inference only)
dtest = xgb.DMatrix(X_test.astype(np.float32))

Critical Parameters:

params = {
    'tree_method': 'hist',        # GPU-accelerated histogram
    'device': 'cuda:0',           # Explicit GPU device
    'max_bin': 256,               # Higher bins = better splits (VRAM permitting)
    'grow_policy': 'depthwise',   # vs 'lossguide' for imbalanced data
    'predictor': 'gpu_predictor', # GPU inference
}

# Training with explicit device
model = xgb.train(params, dtrain, num_boost_round=100)

GPU Verification (fail-fast):

def verify_gpu():
    """Verify XGBoost GPU availability. Raises if unavailable."""
    import subprocess
    try:
        result = subprocess.run(["nvidia-smi"], capture_output=True, text=True)
        if result.returncode != 0:
            raise RuntimeError("nvidia-smi failed - no GPU available")
    except FileNotFoundError:
        raise RuntimeError("nvidia-smi not found - no GPU available")

    build_info = xgb.build_info()
    if not build_info.get("USE_CUDA"):
        raise RuntimeError("XGBoost not compiled with CUDA support")