| { | |
| "name": "loop_114", | |
| "op_type": "loop_114", | |
| "description": "Compute auto-correlation of an integer array with widening accumulation", | |
| "tags": [ | |
| "simd-loop" | |
| ], | |
| "axes": { | |
| "n": { | |
| "type": "var", | |
| "description": "axis n" | |
| }, | |
| "lags": { | |
| "type": "var", | |
| "description": "axis lags" | |
| }, | |
| "scale": { | |
| "type": "const", | |
| "value": 4, | |
| "description": "const scale" | |
| } | |
| }, | |
| "inputs": { | |
| "data": { | |
| "shape": [ | |
| "n" | |
| ], | |
| "dtype": "int16" | |
| } | |
| }, | |
| "outputs": { | |
| "res": { | |
| "shape": [ | |
| "lags" | |
| ], | |
| "dtype": "int16", | |
| "description": "Output array" | |
| } | |
| }, | |
| "reference": "import numpy as np\n\ndef run(data):\n n = int(data.shape[0])\n scale = 4\n d = data.astype(np.int64)\n res = np.zeros(n, dtype=np.int16)\n for lag in range(n):\n acc = int(((d[:n - lag] * d[lag:]) >> scale).sum())\n res[lag] = np.int16(acc >> 16)\n return res\n", | |
| "simd_loop_meta": { | |
| "output_inplace": false, | |
| "array_pad": 0, | |
| "scratch": [], | |
| "axes_order": [ | |
| "n", | |
| "lags", | |
| "scale" | |
| ] | |
| } | |
| } | |