code wiki / _hdl_build / nx_roofline_test.nx
nx_roofline_test.nx
buildroot/runtime/_hdl_build/nx_roofline_test.nx
about
nx_roofline_test.nx -- the team DIAGNOSES the real bottleneck of the targets (llama.cpp /
ggml), and proves the intelligent lever. Three checks, exit 0 only if all hold:
(1) the Q4 quantized MATVEC of single-token decode is MEMORY-bound on a real consumer CPU --
matching the measured reality (AVX2 micro-opt = +0.8%). So SIMD is the WRONG lever.
(2) the SAME math, but a batched/reused matmul (each weight reused many times), is COMPUTE-
bound -- so the search-governor/superopt IS the right lever there. The roofline tells the
team WHICH regime it is in, per kernel, per chip.
(3) the data-movement lever (Q4 -> Q2, half the weight bytes) gives ~1.8x in the memory-bound
regime -- ~225x more than the SIMD micro-opt. "mental math" (move less) >> brute force.
license_tier: ORIGINAL
dependencies 2 imports · 0 importers
imports: nx_roofline.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
| none |
functions
| 15 | func rt_puts(s: *u8) -> i64 { var n: i64 = 0; while s[n] != (0 as u8) { n = n + 1 } sys_write(1, s, n); return 0 } |
| 16 | func rt_num(v: i64) -> i64 { let bb: *u8 = sys_mmap(28); var m: i64=v; if m<0 {m=0-m}; let t: *u8 = sys_mmap(28); var k: i64=0; if m==0 {t[0]=48;k=1}; while m>0 {t[k]=48+(m%10); m=m/10; k=k+1}; var i: i64=0; while i<k {bb[i]=t[k-1-i]; i=i+1}; sys_write(1, bb, k); return 0 } |
| 18 | func main() -> i64 |