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