code wiki / _hdl_build / nx_f32_hw_matmul_perf.nx
nx_f32_hw_matmul_perf.nx
buildroot/runtime/_hdl_build/nx_f32_hw_matmul_perf.nx
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nx_f32_hw_matmul_perf.nx -- MEASURED head-to-head: software vs hardware f32 GEMM (both sovereign).
Times nx_f32_matmul (bits-up SOFTWARE f32) vs nx_f32_hw_matmul (SSE addss/mulss) on the SAME inputs,
clock read IN-ORGAN (sys_now_ms). Reports MFLOP/s for each + the speedup. This is a legitimate
measured comparison (both are OURS -- no 3rd-party); it quantifies how much the SSE hardware rung
buys, and what gap to BLAS remains (packed SIMD addps/mulps + blocking = the next rung).
Sovereign: imports nx_f32_matmul + nx_f32_hw_matmul + nx_syscalls. license_tier: ORIGINAL
dependencies 3 imports · 0 importers
imports: nx_f32_matmul.nxnx_f32_hw_matmul.nxnx_syscalls.nx
imported by: nobody (leaf or entry point)
call flow from main pre-order; caps 40 nodes / depth 6 declared; ↻ = already shown
structs
| none |
consts
| 11 | const K_MAGIC_32768: i64 = 32768 |
functions
| 14 | func time_mm(which: i64, A: *i64, B: *i64, C: *i64, N: i64) -> i64 |
| 33 | func run_pair(N: i64) -> i64 |
| 50 | func main() -> i64 |