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chore: import upstream snapshot with attribution
2026-07-13 12:38:16 +08:00

153 lines
4.4 KiB
Python

"""Shared-memory budget accounting for the fused IPM kernel.
Fused layout (fp32), one block per LP, all state in shared memory::
A NC * NV constraint matrix (resident)
c NV cost vector (resident)
x NV IPM state (resident)
ata NC * NC KKT matrix / Cholesky factor
rhs NC ax2c, then delta
d NV aliased with r = A.T @ delta
S_elems = NC*NV + NC*NC + 3*NV + NC
Dynamic shared-memory cap per block (with opt-in via
``cudaFuncAttributeMaxDynamicSharedMemorySize``):
A100 SM_80 164 KB practical 160 KB
H100 SM_90 227 KB practical 223 KB <- default target
H200 SM_90 227 KB
H20 SM_90 227 KB
B200 SM_100 228 KB practical 224 KB
"""
from __future__ import annotations
from dataclasses import dataclass
# Per-block slack reserved for cuBLASDx workspace and CUDA runtime state.
_RUNTIME_PAD_BYTES = 256
# fp32
_BYTES_PER_ELEM = 4
# Practical per-block dynamic shmem caps (bytes)
GPU_BUDGETS_BYTES: dict[str, int] = {
"a100": 160 * 1024,
"h100": 223 * 1024,
"h200": 223 * 1024,
"h20": 223 * 1024,
"b200": 224 * 1024,
}
@dataclass(frozen=True)
class ShmemBreakdown:
nc: int
nv: int
a_bytes: int
c_bytes: int
x_bytes: int
ata_bytes: int
rhs_bytes: int
d_bytes: int
pad_bytes: int
@property
def total_bytes(self) -> int:
return (
self.a_bytes
+ self.c_bytes
+ self.x_bytes
+ self.ata_bytes
+ self.rhs_bytes
+ self.d_bytes
+ self.pad_bytes
)
def as_kib(self) -> float:
return self.total_bytes / 1024.0
def shmem_bytes(nc: int, nv: int, bytes_per_elem: int = _BYTES_PER_ELEM) -> int:
"""Exact byte count for the fused layout with the given (NC, NV)."""
return bytes_per_elem * (nc * nv + nc * nc + 3 * nv + nc) + _RUNTIME_PAD_BYTES
def breakdown(
nc: int, nv: int, bytes_per_elem: int = _BYTES_PER_ELEM
) -> ShmemBreakdown:
"""Per-array byte breakdown — useful for debugging shmem pressure."""
b = bytes_per_elem
return ShmemBreakdown(
nc=nc,
nv=nv,
a_bytes=b * nc * nv,
c_bytes=b * nv,
x_bytes=b * nv,
ata_bytes=b * nc * nc,
rhs_bytes=b * nc,
d_bytes=b * nv,
pad_bytes=_RUNTIME_PAD_BYTES,
)
def gpu_budget_bytes(gpu: str) -> int:
key = gpu.lower()
if key not in GPU_BUDGETS_BYTES:
raise ValueError(
f"unknown gpu '{gpu}', expected one of {sorted(GPU_BUDGETS_BYTES)}"
)
return GPU_BUDGETS_BYTES[key]
def fits(nc: int, nv: int, gpu: str = "h100") -> bool:
return shmem_bytes(nc, nv) <= gpu_budget_bytes(gpu)
def assert_fits(nc: int, nv: int, gpu: str = "h100") -> None:
"""Raise if the fused kernel will not fit on the target GPU."""
used = shmem_bytes(nc, nv)
cap = gpu_budget_bytes(gpu)
if used > cap:
raise ValueError(
f"fused IPM kernel needs {used/1024:.1f} KiB of shared memory for "
f"NC={nc}, NV={nv}, but {gpu} allows {cap/1024:.1f} KiB/block. "
f"Either reduce problem size or switch to a tiled design."
)
def max_nc_for_nv(nv: int, gpu: str = "h100") -> int:
"""Largest NC that fits for a given NV. Solves
4 * (NC^2 + (NV+1)*NC + 3*NV) + pad <= cap
via the quadratic formula (monotone in NC). Returns 0 if even NC=1 overflows.
"""
cap = gpu_budget_bytes(gpu)
b = _BYTES_PER_ELEM
# cap - pad >= b * (NC^2 + (NV+1)*NC + 3*NV)
rhs = (cap - _RUNTIME_PAD_BYTES) / b - 3 * nv
if rhs <= 0:
return 0
# NC^2 + (NV+1)*NC - rhs <= 0
import math
disc = (nv + 1) ** 2 + 4 * rhs
nc_max = int((-(nv + 1) + math.sqrt(disc)) / 2.0)
while nc_max > 0 and shmem_bytes(nc_max, nv) > cap:
nc_max -= 1
return max(nc_max, 0)
def report(nc: int, nv: int, gpu: str = "h100") -> str:
"""Human-readable summary — used by kernels on init for logging."""
bd = breakdown(nc, nv)
cap = gpu_budget_bytes(gpu)
status = "FITS" if bd.total_bytes <= cap else "OVER BUDGET"
return (
f"[shmem] NC={nc} NV={nv} gpu={gpu} | "
f"A={bd.a_bytes/1024:.1f}K "
f"ata={bd.ata_bytes/1024:.1f}K "
f"rest={(bd.c_bytes+bd.x_bytes+bd.rhs_bytes+bd.d_bytes)/1024:.1f}K | "
f"total={bd.total_bytes/1024:.1f}K / {cap/1024:.1f}K {status}"
)