Operand & broadcast layouts — 1-D / scalar / mixed-operand / broadcast (NumSharp vs NumPy 2.4.2)

The layout classes the per-operand layout grid (benchmark/layout) can't express. ratio = NumPy_ms / NumSharp_ms — >1.0 = NumSharp faster. ✅≥1.0 🟡≥0.5 🟠≥0.2 🔴<0.2. 1M elements, best-of-3.

case	f64	f32	f16	i32	i64	c128	geomean
1-D contiguous (a+a)	2.55 ✅	2.18 ✅	0.62 🟡	2.04 ✅	2.54 ✅	2.38 ✅	1.87 ✅
1-D strided a[::2]	1.83 ✅	1.36 ✅	0.52 🟡	1.37 ✅	1.77 ✅	1.86 ✅	1.34 ✅
1-D reversed a[::-1]	2.26 ✅	2.09 ✅	0.56 🟡	2.00 ✅	2.14 ✅	2.23 ✅	1.71 ✅
array + scalar	2.63 ✅	2.11 ✅	0.63 🟡	1.80 ✅	2.17 ✅	2.58 ✅	1.81 ✅
scalar + array	2.35 ✅	2.10 ✅	0.64 🟡	1.98 ✅	2.48 ✅	2.59 ✅	1.85 ✅
mixed C + F	2.12 ✅	2.01 ✅	0.62 🟡	1.98 ✅	2.02 ✅	2.26 ✅	1.70 ✅
mixed C + T	2.59 ✅	2.13 ✅	0.62 🟡	2.04 ✅	2.20 ✅	2.51 ✅	1.84 ✅
binary broadcast +row(1,C)	2.72 ✅	2.28 ✅	0.63 🟡	2.00 ✅	2.42 ✅	2.42 ✅	1.89 ✅
binary broadcast +col(R,1)	2.68 ✅	2.14 ✅	0.56 🟡	1.99 ✅	2.45 ✅	2.82 ✅	1.88 ✅
col-broadcast unary (inner stride-0)	2.55 ✅	1.76 ✅	0.86 🟡	1.69 ✅	2.66 ✅	6.09 ✅	2.18 ✅

Worst 12 cells

key	NumSharp ms	NumPy ms	ratio
1d_strided	f16	2.6414	1.3865	0.52 🟡
1d_rev	f16	5.3054	2.9810	0.56 🟡
bcast_col	f16	5.2996	2.9861	0.56 🟡
mix_C_T	f16	5.3156	3.2865	0.62 🟡
1d_C	f16	4.7503	2.9588	0.62 🟡
mix_C_F	f16	5.3110	3.3155	0.62 🟡
bcast_row	f16	4.7682	2.9983	0.63 🟡
scalar_rhs	f16	4.7137	2.9711	0.63 🟡
scalar_lhs	f16	4.6575	2.9643	0.64 🟡
colbcast_unary	f16	0.4898	0.4232	0.86 🟡
1d_strided	f32	0.2652	0.3608	1.36 ✅
1d_strided	i32	0.2798	0.3832	1.37 ✅

60 comparable cells.