Compute all standard aggregates over an f64 column slice. Dispatches sum/min/max to SIMD kernels (AVX-512/AVX2/NEON) via `ts_runtime()`. Falls back to scalar with Kahan compensation.
(values: &[f64])
| 37 | /// Dispatches sum/min/max to SIMD kernels (AVX-512/AVX2/NEON) via |
| 38 | /// `ts_runtime()`. Falls back to scalar with Kahan compensation. |
| 39 | pub fn aggregate_f64(values: &[f64]) -> AggResult { |
| 40 | if values.is_empty() { |
| 41 | return AggResult { |
| 42 | min: f64::NAN, |
| 43 | max: f64::NAN, |
| 44 | first: f64::NAN, |
| 45 | last: f64::NAN, |
| 46 | ..Default::default() |
| 47 | }; |
| 48 | } |
| 49 | |
| 50 | // Filter out NaN values for SIMD paths (SIMD min/max don't handle NaN correctly). |
| 51 | let has_nan = values.iter().any(|v| v.is_nan()); |
| 52 | |
| 53 | let (sum, min, max, count) = if has_nan { |
| 54 | // Slow path: skip NaN values. |
| 55 | let mut s = 0.0f64; |
| 56 | let mut comp = 0.0f64; |
| 57 | let mut mn = f64::INFINITY; |
| 58 | let mut mx = f64::NEG_INFINITY; |
| 59 | let mut c = 0u64; |
| 60 | for &v in values { |
| 61 | if v.is_nan() { |
| 62 | continue; |
| 63 | } |
| 64 | c += 1; |
| 65 | let y = v - comp; |
| 66 | let t = s + y; |
| 67 | comp = (t - s) - y; |
| 68 | s = t; |
| 69 | if v < mn { |
| 70 | mn = v; |
| 71 | } |
| 72 | if v > mx { |
| 73 | mx = v; |
| 74 | } |
| 75 | } |
| 76 | (s, mn, mx, c) |
| 77 | } else { |
| 78 | // Fast path: SIMD dispatch for clean data. |
| 79 | let rt = ts_runtime(); |
| 80 | let s = (rt.sum_f64)(values); |
| 81 | let mn = (rt.min_f64)(values); |
| 82 | let mx = (rt.max_f64)(values); |
| 83 | (s, mn, mx, values.len() as u64) |
| 84 | }; |
| 85 | |
| 86 | AggResult { |
| 87 | count, |
| 88 | sum, |
| 89 | min, |
| 90 | max, |
| 91 | first: values[0], |
| 92 | last: values[values.len() - 1], |
| 93 | } |
| 94 | } |
| 95 | |
| 96 | /// Compute aggregates over an i64 column slice. |