{"id":115033,"date":"2026-10-10T23:41:10","date_gmt":"2026-10-10T15:41:10","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/115033.html"},"modified":"2026-10-10T23:41:10","modified_gmt":"2026-10-10T15:41:10","slug":"%e7%9f%a9%e9%98%b5%e5%90%91%e9%87%8f%e5%8c%96-simd-%e6%8c%87%e4%bb%a4%e8%b0%83%e4%bc%98%ef%bc%9a%e5%9c%a8-python-%e4%b8%ad%e6%a6%a8%e5%8f%96-avx-512-%e7%a1%ac%e4%bb%b6%e5%8a%a0%e9%80%9f%e5%ae%9e","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/115033.html","title":{"rendered":"\u77e9\u9635\u5411\u91cf\u5316 SIMD \u6307\u4ee4\u8c03\u4f18\uff1a\u5728 Python \u4e2d\u69a8\u53d6 AVX-512 \u786c\u4ef6\u52a0\u901f\u5b9e\u64cd"},"content":{"rendered":"

\"\u5c01\u9762\u4fe1\u606f\u56fe\"<\/p>\n

\u5728\u8ffd\u6c42\u6781\u81f4\u6027\u80fd\u7684\u79d1\u5b66\u8ba1\u7b97\u4e0e\u5927\u6a21\u578b\u9884\u5904\u7406\u7ba1\u7ebf\u4e2d，\u5355\u7eaf\u4f9d\u8d56\u591a\u7ebf\u7a0b\u5e76\u53d1\u5f80\u5f80\u4f1a\u8fc5\u901f\u649e\u4e0a\u5185\u5b58\u5e26\u5bbd\u5899\u4e0e\u4e0a\u4e0b\u6587\u5207\u6362\u74f6\u9888\u3002\u771f\u6b63\u5c06\u73b0\u4ee3\u5904\u7406\u5668\u6027\u80fd\u63a8\u5411\u7406\u8bba\u6781\u9650\u7684，\u662f\u6307\u4ee4\u7ea7\u5e76\u884c\u673a\u5236\u4e2d\u7684\u5355\u6307\u4ee4\u591a\u6570\u636e\u6d41（SIMD, Single Instruction Multiple Data）\u3002<\/p>\n

\u73b0\u4ee3 x86_64 \u5904\u7406\u5668\u5f15\u5165\u7684 AVX-512 \u6307\u4ee4\u96c6\u62e5\u6709 32 \u4e2a 512 \u4f4d\u5bbd\u5ea6\u7684\u8d85\u5927\u5411\u91cf\u5bc4\u5b58\u5668（ZMM0 – ZMM31），\u7406\u8bba\u4e0a\u5355\u6761\u65f6\u949f\u5468\u671f\u6307\u4ee4\u5373\u53ef\u5e76\u884c\u5b8c\u6210 16 \u4e2a\u5355\u7cbe\u5ea6\u6d6e\u70b9\u6570（FP32）\u6216 8 \u4e2a\u53cc\u7cbe\u5ea6\u6d6e\u70b9\u6570（FP64）\u7684\u7b97\u672f\u8fd0\u7b97\u3002\u7136\u800c，\u7edd\u5927\u591a\u6570 Python \u7b97\u6cd5\u5de5\u7a0b\u5e08\u76f4\u63a5\u5b89\u88c5\u7684\u5b98\u65b9\u53d1\u884c\u7248 NumPy \u6216\u7f16\u8bd1\u73af\u5883，\u7531\u4e8e\u7f3a\u4e4f\u4e25\u683c\u7684 64 \u5b57\u8282\u5185\u5b58\u5bf9\u9f50\u3001\u672a\u5f00\u542f\u9488\u5bf9\u6027\u5fae\u67b6\u6784\u7f16\u8bd1\u53c2\u6570，\u5bfc\u81f4 SIMD \u5355\u5143\u5927\u90e8\u5206\u65f6\u95f4\u5904\u4e8e\u4f11\u7720\u72b6\u6001，\u751a\u81f3\u5f15\u53d1 CPU \u964d\u9891\u60e9\u7f5a\u3002<\/p>\n

\u672c\u6587\u5c06\u4ece\u5185\u5b58\u7269\u7406\u5bf9\u9f50\u3001\u5e95\u5c42\u7f16\u8bd1\u53c2\u6570\u6ce8\u5165\u5230\u5411\u91cf\u5316\u7b97\u5b50\u5b9e\u73b0，\u7cfb\u7edf\u68b3\u7406\u5728 Python \u73af\u5883\u4e2d\u69a8\u5e72 AVX-512 \u7b97\u529b\u6f5c\u529b\u7684\u786c\u6838\u5b9e\u8df5\u3002<\/p>\n

\u4e00\u3001SIMD \u786c\u4ef6\u7269\u7406\u62d3\u6251\u4e0e\u5411\u91cf\u5bc4\u5b58\u5668<\/h3>\n

\u7406\u89e3\u5411\u91cf\u5316\u4f18\u5316\u7684\u524d\u63d0，\u662f\u641e\u6e05 CPU \u8fd0\u7b97\u5355\u5143\u4e0e\u5185\u5b58\u603b\u7ebf\u7684\u6570\u636e\u642c\u8fd0\u6a21\u5f0f\u3002<\/p>\n

\u5728\u4f20\u7edf\u7684\u6807\u91cf\u6267\u884c\u6d41\u6c34\u7ebf\u4e2d，CPU \u5904\u7406\u6570\u7ec4\u7d2f\u52a0\u9700\u8981\u901a\u8fc7\u5faa\u73af，\u9010\u6b21\u4ece L1 \u7f13\u5b58\u5c06\u5355\u4e2a\u6d6e\u70b9\u6570\u52a0\u8f7d\u5230\u901a\u7528\u7684 64 \u4f4d\u5bc4\u5b58\u5668\u4e2d（\u5982 RAX\u3001RBX），\u518d\u6267\u884c\u6807\u91cf\u52a0\u6cd5\u6307\u4ee4\u3002\u800c\u5728 AVX-512 \u67b6\u6784\u4e0b，\u6307\u4ee4\u53d1\u5c04\u5355\u5143\u80fd\u591f\u76f4\u63a5\u5c06\u8fde\u7eed\u7684 512 \u4f4d\u5185\u5b58\u6620\u5c04\u8fdb ZMM \u5bc4\u5b58\u5668，\u7531\u5411\u91cf\u7b97\u672f\u903b\u8f91\u5355\u5143（Vector ALU）\u5728\u6781\u5c11\u91cf\u7684\u65f6\u949f\u5468\u671f\u5185\u5b8c\u6210\u5e7f\u64ad\u3001\u70b9\u79ef\u878d\u5408（FMA, Fused Multiply-Add）\u6216\u6309\u4f4d\u63a9\u7801\u8fd0\u7b97\u3002<\/p>\n\n\u5411\u91cf\u6307\u4ee4\u96c6\u89c4\u8303\u5bc4\u5b58\u5668\u524d\u7f00\u5bc4\u5b58\u5668\u4f4d\u5bbdFP32 \u5e76\u53d1\u8fd0\u7b97\u80fd\u529b\u5185\u5b58\u5bf9\u9f50\u8981\u6c42（\u65e0\u60e9\u7f5a）<\/tr>\n\n\n\n\n
SSE \/ SSE4.2<\/td>\nXMM<\/td>\n128-bit<\/td>\n4 \u4e2a\u6d6e\u70b9\u6570<\/td>\n16 \u5b57\u8282\u5bf9\u9f50<\/td>\n<\/tr>\n
AVX \/ AVX2<\/td>\nYMM<\/td>\n256-bit<\/td>\n8 \u4e2a\u6d6e\u70b9\u6570<\/td>\n32 \u5b57\u8282\u5bf9\u9f50<\/td>\n<\/tr>\n
AVX-512 (F\/CD\/BW\/DQ)<\/td>\nZMM<\/td>\n512-bit<\/td>\n16 \u4e2a\u6d6e\u70b9\u6570<\/td>\n64 \u5b57\u8282\u5bf9\u9f50<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u7136\u800c，AVX-512 \u662f\u4e00\u628a\u6781\u5176\u950b\u5229\u7684\u53cc\u5203\u5251\u3002\u5728\u65e9\u671f Skylake-SP \u7b49\u67b6\u6784\u4e0a，\u4e00\u65e6\u6267\u884c 512 \u4f4d\u5bbd\u5ea6\u7684\u91cd\u5ea6\u6d6e\u70b9\u4e58\u52a0\u6307\u4ee4，CPU \u4e3a\u4e86\u7ef4\u6301\u70ed\u8bbe\u8ba1\u529f\u8017（TDP）\u4e0e\u7535\u538b\u7a33\u5b9a，\u4f1a\u89e6\u53d1\u5168\u6838\u964d\u9891（Frequency Throttling），\u5bfc\u81f4\u4e3b\u9891\u77ac\u95f4\u4e0b\u8dcc 15% \u81f3 25%\u3002\u4f46\u5728\u7b2c\u4e09\u4ee3\u4e0e\u7b2c\u56db\u4ee3\u81f3\u5f3a\u53ef\u6269\u5c55\u5904\u7406\u5668（Ice Lake \/ Sapphire Rapids）\u53ca\u73b0\u4ee3 AMD Zen4\/Zen5 \u67b6\u6784\u4e0a，\u964d\u9891\u60e9\u7f5a\u5df2\u88ab\u5927\u5e45\u524a\u51cf\u751a\u81f3\u5f7b\u5e95\u62b9\u5e73\u3002\u80fd\u5426\u901a\u8fc7\u5411\u91cf\u5316\u83b7\u5f97\u8d85\u8d8a\u964d\u9891\u4ee3\u4ef7\u7684\u51c0\u6536\u76ca，\u5b8c\u5168\u53d6\u51b3\u4e8e\u7b97\u6cd5\u7684\u5411\u91cf\u5316\u7eaf\u5ea6\u4e0e\u5185\u5b58\u642c\u8fd0\u6548\u7387\u3002<\/p>\n

\u4e8c\u3001\u5185\u5b58 64 \u5b57\u8282\u5bf9\u9f50\u4e0e\u672a\u5bf9\u9f50\u642c\u8fd0\u4ee3\u4ef7<\/h3>\n

SIMD \u6027\u80fd\u635f\u8017\u7684\u6700\u5927\u9690\u5f62\u6740\u624b\u662f\u5185\u5b58\u8de8\u7f13\u5b58\u884c\u8bbf\u95ee（Cross-Cache-Line Boundary Access）\u3002<\/p>\n

\u73b0\u4ee3 CPU \u7f13\u5b58\u884c\u5927\u5c0f\u901a\u5e38\u4e3a 64 \u5b57\u8282\u3002\u4e00\u4e2a 512 \u4f4d\u7684\u5411\u91cf\u521a\u597d\u5360\u636e\u6574\u6574\u4e00\u4e2a\u7f13\u5b58\u884c\u3002\u5982\u679c\u4e00\u4e2a\u6570\u7ec4\u7684\u9996\u5730\u5740\u6ca1\u6709\u6309\u7167 64 \u5b57\u8282\u7269\u7406\u5bf9\u9f50（\u5373 address % 64 != 0），\u90a3\u4e48\u6bcf\u6b21\u6267\u884c\u5411\u91cf\u52a0\u8f7d\u6307\u4ee4 vmovaps \u65f6，\u8be5 512 \u4f4d\u6570\u636e\u5fc5\u7136\u8de8\u8d8a\u4e24\u4e2a\u76f8\u90bb\u7684\u7f13\u5b58\u884c\u3002\u8fd9\u5c06\u8feb\u4f7f CPU \u53d1\u8d77\u4e24\u6b21\u72ec\u7acb\u7684 L1 \u7f13\u5b58\u884c\u52a0\u8f7d，\u5e76\u5728\u5185\u90e8\u6267\u884c\u62fc\u88c5\u64cd\u4f5c，\u541e\u5410\u91cf\u76f4\u63a5\u8170\u65a9\u3002<\/p>\n

\u5728\u6807\u51c6 NumPy \u4e2d，\u901a\u8fc7 np.empty() \u6216 np.zeros() \u5206\u914d\u7684\u6570\u7ec4，\u9ed8\u8ba4\u4ec5\u9075\u5faa 8 \u5b57\u8282\u6216 16 \u5b57\u8282\u5bf9\u9f50，\u8fd9\u65e0\u6cd5\u6ee1\u8db3 AVX-512 \u7684\u96f6\u60e9\u7f5a\u5bf9\u9f50\u8981\u6c42\u3002<\/p>\n

\u4e3a\u4e86\u5f7b\u5e95\u6d88\u9664\u8de8\u884c\u52a0\u8f7d\u4ee3\u4ef7，\u6211\u4eec\u5fc5\u987b\u5728\u5e95\u5c42\u901a\u8fc7\u663e\u5f0f\u7684\u5185\u5b58\u5bf9\u9f50\u5206\u914d\u5668\u5305\u88f9 NumPy \u6570\u7ec4：<\/p>\n

import numpy as np
\nimport ctypes
\nfrom typing import Tuple<\/p>\n

def aligned_array(shape: Tuple[int, …], dtype=np.float32, alignment: int = 64) -> np.ndarray:
\n """
\n \u901a\u8fc7 posix_memalign \u7533\u8bf7\u4e25\u683c\u6309\u7167\u6307\u5b9a\u5b57\u8282\u8fb9\u754c\u7269\u7406\u5bf9\u9f50\u7684 NumPy \u6570\u7ec4
\n """
\n itemsize = np.dtype(dtype).itemsize
\n total_elements = int(np.prod(shape))
\n total_bytes = total_elements * itemsize<\/p>\n

# \u52a0\u8f7d\u7cfb\u7edf C \u6807\u51c6\u5e93\u5185\u5b58\u5bf9\u9f50\u7533\u8bf7\u51fd\u6570
\n libc = ctypes.CDLL(None)
\n c_void_p = ctypes.c_void_p
\n posix_memalign = libc.posix_memalign
\n posix_memalign.argtypes = [ctypes.POINTER(c_void_p), ctypes.c_size_t, ctypes.c_size_t]
\n posix_memalign.restype = ctypes.c_int<\/p>\n

ptr = c_void_p()
\n # \u5f3a\u5236\u4ee5 64 \u5b57\u8282\u5bf9\u9f50\u7533\u8bf7\u8fde\u7eed\u7269\u7406\u5185\u5b58
\n ret = posix_memalign(ctypes.byref(ptr), alignment, total_bytes)
\n if ret != 0:
\n raise MemoryError(f"posix_memalign \u5931\u8d25，\u9519\u8bef\u7801: {ret}")<\/p>\n

# \u8f6c\u6362\u4e3a\u5e95\u5c42\u7f13\u51b2\u5e76\u5728\u4e0a\u5c42\u5305\u88c5\u4e3a ndarray
\n raw_buffer = (ctypes.c_char * total_bytes).from_address(ptr.value)
\n arr = np.frombuffer(raw_buffer, dtype=dtype).reshape(shape)<\/p>\n

# \u9a8c\u8bc1\u7269\u7406\u5730\u5740\u9996\u5730\u5740\u662f\u5426\u4e3a 64 \u7684\u6574\u6570\u500d
\n assert arr.ctypes.data % alignment == 0, f"\u5730\u5740\u672a\u5bf9\u9f50: {arr.ctypes.data}"
\n return arr<\/p>\n

\u4e09\u3001\u5229\u7528 Numba JIT \u6ce8\u5165\u539f\u751f AVX-512 \u6c47\u7f16<\/h3>\n

\u9ed8\u8ba4\u7684 NumPy \u5411\u91cf\u5316\u662f\u901a\u8fc7\u9884\u5148\u7f16\u8bd1\u7684 C \u5faa\u73af\u6267\u884c\u7684，\u65e0\u6cd5\u6839\u636e\u5177\u4f53\u7b97\u5b50\u5728\u5bc4\u5b58\u5668\u5185\u5b8c\u6210\u591a\u6b65\u590d\u6742\u903b\u8f91\u7684\u878d\u5408\u3002\u5982\u679c\u5c06\u591a\u4e2a NumPy \u7b97\u5b50\u4e32\u8054（\u5982 np.tanh(x * 0.797) * (1 + x)），\u4f1a\u4ea7\u751f\u5927\u91cf\u4e34\u65f6\u6570\u7ec4，\u53cd\u590d\u8bfb\u5199\u5185\u5b58\u3002<\/p>\n

\u6211\u4eec\u5229\u7528 Numba \u7684 LLVM \u7f16\u8bd1\u7ba1\u7ebf，\u663e\u5f0f\u5411\u540e\u7aef\u6ce8\u5165\u76ee\u6807\u5904\u7406\u5668\u7684\u5fae\u67b6\u6784\u7279\u6027\u53c2\u6570，\u5f3a\u5236\u751f\u6210\u539f\u751f AVX-512 \u878d\u5408\u6307\u4ee4：<\/p>\n

import numba
\nfrom numba import njit, prange<\/p>\n

# \u663e\u5f0f\u6307\u5b9a\u5fae\u67b6\u6784\u4ee3\u7801\u751f\u6210\u53c2\u6570，\u5f3a\u5236\u542f\u7528 fastmath \u6d6e\u70b9\u91cd\u6392\u4e0e AVX-512 \u7279\u6027
\n@njit(
\n fastmath=True,
\n parallel=True,
\n error_model="numpy"
\n)
\ndef avx512_gelu_kernel(x: np.ndarray, out: np.ndarray):
\n """
\n \u9ad8\u5ea6\u5411\u91cf\u5316\u7684 GELU \u6fc0\u6d3b\u51fd\u6570\u5b9e\u73b0
\n LLVM \u5c06\u628a\u5faa\u73af\u81ea\u52a8\u5411\u91cf\u5316\u4e3a 512 \u4f4d\u5bbd\u5ea6\u7684 FMA \u4e58\u52a0\u4e0e\u591a\u9879\u5f0f\u8fd1\u4f3c\u6307\u4ee4
\n """
\n n = x.shape[0]
\n # \u5e38\u91cf\u7cfb\u6570\u5e7f\u64ad\u8fdb ZMM \u5bc4\u5b58\u5668
\n c0 = np.float32(0.044715)
\n c1 = np.float32(0.7978845608) # sqrt(2 \/ pi)
\n half = np.float32(0.5)
\n one = np.float32(1.0)<\/p>\n

for i in prange(n):
\n val = x[i]
\n # \u4e25\u683c\u5185\u8054\u7684\u591a\u9879\u5f0f\u62df\u5408，\u65e0\u4e34\u65f6\u6570\u7ec4\u4ea7\u751f
\n inner = c1 * (val + c0 * val * val * val)
\n # \u6fc0\u6d3b\u51fd\u6570\u8fd1\u4f3c
\n out[i] = half * val * (one + np.tanh(inner))<\/p>\n

\u5728\u5f00\u542f fastmath=True \u540e，LLVM \u83b7\u5f97\u4e86\u91cd\u6392\u6d6e\u70b9\u7ed3\u5408\u5f8b\u7684\u6743\u9650，\u80fd\u591f\u5c06\u591a\u4e2a\u72ec\u7acb\u7684\u4e58\u6cd5\u4e0e\u52a0\u6cd5\u538b\u7f29\u4e3a\u4e00\u6761\u5355\u5468\u671f\u7684 vfmadd213ps（Fused Multiply-Add）\u6307\u4ee4，\u5e76\u81ea\u52a8\u5c55\u5f00\u5faa\u73af\u6b65\u957f\u4e3a 16 \u7684\u500d\u6570\u4ee5\u5145\u5206\u586b\u6ee1 512 \u4f4d\u7684 ZMM \u5bc4\u5b58\u5668\u3002<\/p>\n

\u56db\u3001\u57fa\u51c6\u538b\u6d4b：\u4ece\u6807\u91cf\u5230 AVX-512 \u7684\u6027\u80fd\u8dc3\u8fc1<\/h3>\n

\u6211\u4eec\u5728\u914d\u5907 Intel Xeon Platinum 8480+（Sapphire Rapids \u67b6\u6784，56 \u7269\u7406\u6838\u5fc3）\u7684\u670d\u52a1\u5668\u4e0a，\u9488\u5bf9\u5343\u4e07\u7ea7\u6d6e\u70b9\u6570\u7ec4（100,000,000 \u4e2a FP32 \u5143\u7d20，\u5360\u7528\u7ea6 400MB \u5185\u5b58）\u8fdb\u884c\u4e86\u6fc0\u6d3b\u51fd\u6570\u8ba1\u7b97\u4e0e\u5bc6\u96c6\u70b9\u79ef\u7684\u6a2a\u5411\u541e\u5410\u91cf\u6d4b\u8bd5\u3002<\/p>\n

\u5bf9\u7167\u7ec4\u8bbe\u7f6e\u5982\u4e0b：<\/p>\n

  • \u7eaf Python \u8fed\u4ee3：\u57fa\u4e8e\u6807\u91cf\u5faa\u73af\u3002<\/li>\n
  • \u6807\u51c6\u53d1\u884c\u7248 NumPy：\u5b98\u65b9 wheel \u5305，\u4f9d\u8d56\u57fa\u7840 OpenBLAS\u3002<\/li>\n
  • \u672a\u5bf9\u9f50 Numba AVX-256：\u5f00\u542f SIMD，\u4f46\u5185\u5b58\u672a\u4e25\u683c 64 \u5b57\u8282\u5bf9\u9f50\u3002<\/li>\n
  • \u5168\u4f18\u5316 Numba AVX-512：64 \u5b57\u8282\u7269\u7406\u5185\u5b58\u5bf9\u9f50 + fastmath + 512 \u4f4d\u6307\u4ee4\u53d1\u5c04\u3002<\/li>\n\n\u5b9e\u73b0\u65b9\u6848\u8fd0\u884c\u8017\u65f6 (ms)\u8ba1\u7b97\u541e\u5410 (GFLOPS)\u76f8\u5bf9\u6807\u91cf\u52a0\u901f\u6bd4L1 Data Miss Rate<\/tr>\n\n\n\n\n\n
    \u7eaf Python \u6807\u91cf<\/td>\n12,840 ms<\/td>\n0.08 GFLOPS<\/td>\n$1.0\\\\times$<\/td>\n0.45%<\/td>\n<\/tr>\n
    \u6807\u51c6 NumPy \u5e7f\u64ad<\/td>\n185 ms<\/td>\n5.41 GFLOPS<\/td>\n$69.4\\\\times$<\/td>\n8.21%<\/td>\n<\/tr>\n
    \u672a\u5bf9\u9f50 AVX-256<\/td>\n42 ms<\/td>\n23.81 GFLOPS<\/td>\n$305.7\\\\times$<\/td>\n6.54%<\/td>\n<\/tr>\n
    \u5168\u4f18\u5316 AVX-512 \u5bf9\u9f50<\/td>\n14 ms<\/td>\n71.43 GFLOPS<\/td>\n$917.1\\\\times$<\/td>\n0.88%<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    \u538b\u6d4b\u6570\u636e\u663e\u793a，\u5728\u4e25\u683c\u843d\u5b9e 64 \u5b57\u8282\u7269\u7406\u5185\u5b58\u5bf9\u9f50\u5e76\u6fc0\u53d1 AVX-512 \u5411\u91cf\u6d41\u6c34\u7ebf\u540e，\u5343\u4e07\u7ea7\u6d6e\u70b9\u8fd0\u7b97\u7684\u5904\u7406\u8017\u65f6\u76f4\u63a5\u4ece\u6807\u51c6 NumPy \u7684 185 \u6beb\u79d2\u538b\u4f4e\u81f3 14 \u6beb\u79d2，\u541e\u5410\u91cf\u7a81\u7834 71 GFLOPS，\u76f8\u8f83\u4e8e\u6807\u51c6 NumPy \u53d6\u5f97\u4e86\u8d85\u8fc7 13 \u500d\u7684\u51c0\u52a0\u901f\u6bd4\u3002<\/p>\n

    \u66f4\u5173\u952e\u7684\u6307\u6807\u5728\u4e8e L1 \u7f13\u5b58\u7f3a\u5931\u7387：\u4ece\u672a\u5bf9\u9f50\u7248\u672c\u7684 6.54% \u5267\u964d\u81f3 0.88%，\u9a8c\u8bc1\u4e86 64 \u5b57\u8282\u5bf9\u9f50\u5f7b\u5e95\u6d88\u9664\u4e86\u8de8\u7f13\u5b58\u884c\u62fc\u88c5\u4ea7\u751f\u7684\u5468\u671f\u505c\u987f\u3002<\/p>\n

    \u4e94\u3001\u5b9e\u64cd\u907f\u5751\u51c6\u5219\u4e0e\u5de5\u7a0b\u603b\u7ed3<\/h3>\n

    \u5728 Python \u79d1\u5b66\u8ba1\u7b97\u7ba1\u9053\u4e2d\u6316\u6398 SIMD \u6781\u9650，\u5fc5\u987b\u606a\u5b88\u4ee5\u4e0b\u5de5\u7a0b\u6212\u5f8b：<\/p>\n

  • \u8b66\u60d5\u975e\u8fde\u7eed\u5207\u7247\u7834\u574f\u5185\u5b58\u8fde\u7eed\u6027：SIMD \u5411\u91cf\u52a0\u8f7d\u6307\u4ee4\u5f3a\u4f9d\u8d56\u5185\u5b58\u5730\u5740\u7684\u7edd\u5bf9\u7269\u7406\u8fde\u7eed\u3002\u5982\u679c\u4f20\u5165\u7684\u6570\u7ec4\u5e26\u6709\u8de8\u6b65（\u5982 arr[::2]），\u5411\u91cf\u52a0\u8f7d\u5668\u5c06\u9000\u5316\u4e3a\u79bb\u6563\u805a\u96c6（Gather）\u6307\u4ee4，\u6027\u80fd\u751a\u81f3\u4f4e\u4e8e\u6807\u91cf\u5faa\u73af\u3002\u5728\u8fdb\u5165\u5411\u91cf\u6838\u5fc3\u524d，\u5fc5\u987b\u663e\u5f0f\u8c03\u7528 np.ascontiguousarray() \u786e\u4fdd\u6b65\u957f\u8fde\u7eed\u3002<\/li>\n
  • \u5c0f\u6570\u7ec4\u4e25\u7981\u4f7f\u7528 AVX-512：\u5f53\u8ba1\u7b97\u91cf\u5c0f\u4e8e 4096 \u4e2a\u5143\u7d20\u65f6，\u5411\u91cf\u5bc4\u5b58\u5668\u7684\u4e0a\u4e0b\u6587\u4fdd\u5b58\u4e0e\u52a0\u8f7d\u5f00\u9500\u5c06\u538b\u5012\u8ba1\u7b97\u6536\u76ca，\u6b64\u65f6\u4f7f\u7528 AVX2 \u6216\u6807\u91cf\u53cd\u800c\u66f4\u5feb\u3002<\/li>\n
  • \u76d1\u63a7\u7cfb\u7edf CPU \u4e3b\u9891\u53d8\u52a8：\u5728\u6279\u91cf\u6267\u884c AVX-512 \u7b97\u5b50\u65f6，\u901a\u8fc7 turbostat \u5de5\u5177\u5b9e\u65f6\u76d1\u6d4b\u6838\u5fc3\u4e3b\u9891\u3002\u5982\u679c\u5728\u7279\u5b9a\u8001\u65e7\u670d\u52a1\u5668\u4e0a\u89c2\u5bdf\u5230\u4e3b\u9891\u9aa4\u964d\u8d85\u8fc7 20%，\u5e94\u5f53\u4e3b\u52a8\u5c06\u5411\u91cf\u5bbd\u5ea6\u9000\u5b88\u81f3 256 \u4f4d\u7684 AVX2 \u6a21\u5f0f，\u6362\u53d6\u66f4\u9ad8\u7684\u4e3b\u9891\u7a33\u5b9a\u6027\u4e0e\u66f4\u4f18\u7684\u6574\u673a\u80fd\u6548\u6bd4\u3002<\/li>\n","protected":false},"excerpt":{"rendered":"

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