{"id":176,"date":"2014-12-22T15:23:36","date_gmt":"2014-12-22T07:23:36","guid":{"rendered":"http:\/\/planckscale.info\/?p=176"},"modified":"2016-12-19T18:31:29","modified_gmt":"2016-12-19T10:31:29","slug":"%e7%ae%97%e6%b3%95%e6%8f%8f%e8%bf%b0%e4%b8%8e%e6%80%a7%e8%83%bd%e4%bc%98%e5%8c%96%e7%9a%84%e8%a7%a3%e8%80%a6-halide%e8%af%ad%e8%a8%80-%ef%bc%881%ef%bc%89","status":"publish","type":"post","link":"https:\/\/mindspectrum.xyz\/en\/2014\/12\/22\/%e7%ae%97%e6%b3%95%e6%8f%8f%e8%bf%b0%e4%b8%8e%e6%80%a7%e8%83%bd%e4%bc%98%e5%8c%96%e7%9a%84%e8%a7%a3%e8%80%a6-halide%e8%af%ad%e8%a8%80-%ef%bc%881%ef%bc%89\/","title":{"rendered":"\u7b97\u6cd5\u63cf\u8ff0\u4e0e\u6027\u80fd\u4f18\u5316\u7684\u89e3\u8026\u2014\u2014Halide\u8bed\u8a00 \uff081\uff09"},"content":{"rendered":"
\u7248\u6743\u58f0\u660e\uff1a\u539f\u521b\u4f5c\u54c1\uff0c\u6b22\u8fce\u8f6c\u8f7d\uff0c\u4f46\u8f6c\u8f7d\u8bf7\u4ee5\u8d85\u94fe\u63a5\u5f62\u5f0f\u6ce8\u660e\u6587\u7ae0\u6765\u6e90(planckscale.info<\/a>)\u3001\u4f5c\u8005\u4fe1\u606f\u548c\u672c\u58f0\u660e\uff0c\u5426\u5219\u5c06\u8ffd\u7a76\u6cd5\u5f8b\u8d23\u4efb\u3002<\/strong><\/h5>\n

\u7a0b\u5e8f\u7684\u7ed3\u6784\u548c\u8fd0\u884c\u6548\u7387\u5e38\u5e38\u88ab\u4eba\u4eec\u770b\u4f5c\u662f\u96be\u4ee5\u8c03\u548c\u7684\u3002\u8fd9\u4e2a\u4e8b\u5b9e\u6e90\u4e8e\u6211\u4eec\u628a\u4e00\u4e2a\u6570\u5b66\u4e0a\u7ed3\u6784\u6e05\u6670\u826f\u597d\u7684\uff08\u6bd4\u5982\uff0c\u7528\u9012\u5f52\u5f62\u5f0f\u523b\u753b\u7684\uff09\u7b97\u6cd5\u6620\u5c04\u5230\u4e00\u4e2a\u73b0\u5b9e\u7684\u4e0d\u5b8c\u7f8e\u7684\u8ba1\u7b97\u6a21\u578b\u4e0a\uff0c\u8fd9\u4e2a\u6a21\u578b\u8ba1\u7b97\u662f\u6709\u4ee3\u4ef7\u7684\uff0c\u8981\u6781\u5c0f\u5316\u8fd9\u4e2a\u4ee3\u4ef7\u5c31\u9700\u8981\u5c3d\u53ef\u80fd\u7684\u91cd\u7528\u4e2d\u95f4\u8ba1\u7b97\u7ed3\u679c\uff0c\u51cf\u5c11\u4f9d\u8d56\u589e\u52a0\u6307\u4ee4\u7ea7\u5e76\u884c\uff0c\u5145\u5206\u5229\u7528\u7a7a\u95f4\u548c\u65f6\u95f4\u7684Locality\u548c\u5b58\u50a8\u4f53\u7cfb\u7684\u5206\u7ea7\u7ed3\u6784\uff0c\u7b49\u7b49…<\/p>\n

\u4e8e\u662f\u957f\u4e45\u4ee5\u6765\uff0c\u7a0b\u5e8f\u4e2d\u63cf\u8ff0\u7b97\u6cd5\u8981\u201d\u505a\u4ec0\u4e48\u201c\u7684\u903b\u8f91\uff0c\u63a9\u76d6\u5728\u4e86\u7528\u6765\u4f18\u5316\u6027\u80fd\uff0c\u63cf\u8ff0\u201d\u600e\u4e48\u505a\u201c\u7684\u829c\u6742\u903b\u8f91\u4e4b\u4e0b\u3002\u5f53\u7136\uff0c\u6211\u4eec\u4e5f\u6709\u80fd\u591f\u53ea\u901a\u8fc7\u6e05\u6670\u523b\u753b\u201d\u505a\u4ec0\u4e48\u201c\u6765\u7f16\u7a0b\u7684\u8bed\u8a00\uff08\u5982\u51fd\u6570\u5f0f\u8bed\u8a00\u5bb6\u65cf\uff0c\u5c24\u5176\u662f\u4ee5Haskell\u4e3a\u4ee3\u8868\u7684\u7eaf\u51fd\u6570\u5f0f\u8bed\u8a00\uff09\uff0c\u8fd9\u4e9b\u8bed\u8a00\u6536\u8d77\u4e86\u8ba9\u7528\u6237\u81ea\u5df1\u89c4\u5212\u8ba1\u7b97\u8fc7\u7a0b\u7684\u6743\u9650\uff0c\u628a\u8ba1\u7b97\u8fc7\u7a0b\u7684\u4f18\u5316\u4ea4\u7ed9\u7f16\u8bd1\u5668\u81ea\u52a8\u5b8c\u6210\u3002\u8fd9\u5bfc\u81f4\u4e86\u4eba\u4eec\u5bf9\u5176\u6548\u7387\u7684\u4e0d\u4fe1\u4efb\uff0c\u4e8b\u5b9e\u4e0a\u8fd9\u4e2a\u95ee\u9898\u4e5f\u786e\u5b9e\u666e\u904d\u5b58\u5728\uff0c\u806a\u660e\u7684\u7f16\u8bd1\u5668\u53ea\u662f\u5c11\u6570\uff0c\u800c\u4e14\u4ed6\u4eec\u4e5f\u672a\u5fc5\u80fd\u4fdd\u8bc1\u7ed9\u51fa\u4e00\u4e2a\u9ad8\u5ea6\u4f18\u5316\u7684\u7a0b\u5e8f\u3002\u6240\u4ee5\u81f3\u4eca\uff0c\u5bf9\u9ad8\u6027\u80fd\u6709\u8981\u6c42\u7684\u5404\u79cd\u5e93\u4ecd\u7136\u91c7\u7528C\u7b49\u63d0\u4f9b\u4e86\u5e95\u5c42\u64cd\u4f5c\u7684\u8bed\u8a00\u5b9e\u73b0\u3002<\/p>\n

\u6027\u80fd\u4f18\u5316\u4f1a\u7834\u574f\u8f6f\u4ef6\u7ed3\u6784\u8fd9\u4e00\u70b9\u5e26\u6765\u4e86\u65e0\u5c3d\u7684\u82e6\u607c\uff0c\u548c\u98ce\u9669\u3002\u8fd9\u4f7f\u5f97\u6211\u4eec\u6bcf\u505a\u4e00\u6b21\u4f18\u5316\u90fd\u9700\u8981\u683c\u5916\u614e\u91cd\u3002\u6027\u80fd\u4f18\u5316\u540e\u7684\u4ee3\u7801\u57fa\u672c\u88ab\u51dd\u56fa\uff0c\u4e0d\u518d\u5177\u6709\u826f\u597d\u7684\u53ef\u7ef4\u62a4\u3001\u53ef\u4fee\u6539\u6027\u8d28\uff0c\u8fd9\u6837\u6211\u4eec\u8de8\u5e73\u53f0\u79fb\u690d\u6216\u8005\u6539\u53d8\u4f18\u5316\u65b9\u6848\u65f6\u5c31\u4f1a\u9762\u4e34\u4ee3\u7801\u9700\u8981\u5927\u7247\u91cd\u5199\u7684\u98ce\u9669\u3002\u6240\u4ee5\u4eba\u4eec\u786e\u7acb\u4e86\u201d\u4e0d\u6210\u719f\u7684\u4f18\u5316\u662f\u4e07\u6076\u4e4b\u6e90\u201c\u8fd9\u6837\u7684\u4fe1\u6761\uff0c\u5f3a\u8feb\u81ea\u5df1\u628a\u6027\u80fd\u4f18\u5316\u7559\u5230\u6700\u540e\u4e00\u6b65\uff0c\u4e07\u4e0d\u5f97\u5df2\u65f6\u91c7\u7528\u6700\u6210\u719f\u6700\u4fdd\u5b88\u7684\u601d\u8def\u53bb\u4f18\u5316\u3002<\/p>\n

\u771f\u7684\u53ea\u80fd\u63a5\u53d7\u8fd9\u4e2a\u73b0\u72b6\u5417\uff1f\u6211\u4eec\u77e5\u9053\u62bd\u8c61\u548c\u89e3\u8026\u662f\u8f6f\u4ef6\u7684\u7075\u9b42\uff0c\u5f53\u4e24\u4ef6\u4e0d\u540c\u76ee\u7684\u7684\u4e8b\u60c5\u7ea0\u7f20\u5728\u540c\u4e00\u6bb5\u4ee3\u7801\u4e2d\u65f6\uff0c\u610f\u5473\u7740\u9700\u8981\u628a\u4e24\u4ef6\u4e8b\u60c5\u5404\u81ea\u62bd\u8c61\u51fa\u6765\uff0c\u89e3\u8026\u6210\u7b80\u5355\u72ec\u7acb\u7684\u903b\u8f91\u3002\u8fd9\u91cc\u6211\u4eec\u6b63\u9762\u5bf9\u4e00\u4e2a\u5178\u578b\u7684\u89e3\u8026\u95ee\u9898\uff1a\u7b97\u6cd5\u63cf\u8ff0\uff08\u505a\u4ec0\u4e48\uff09\u548c\u6027\u80fd\u4f18\u5316\uff08\u600e\u4e48\u505a\uff09\u9700\u8981\u88ab\u89e3\u8026\u3002\u51fd\u6570\u5f0f\u8bed\u8a00\u867d\u7136\u80fd\u505a\u5230\u8fd9\u4e2a\u89e3\u8026\uff0c\u4f46\u662f\u5b83\u628a\u4f18\u5316\u5de5\u4f5c\u4ea4\u7ed9\u4e0d\u90a3\u4e48\u9760\u8c31\u7684\u7f16\u8bd1\u5668\u4e86\uff0c\u90a3\u80fd\u5426\u628a\u8fd9\u4e2a\u4f18\u5316\u5de5\u4f5c\u4ea4\u8fd8\u7ed9\u8bbe\u8ba1\u8005\u81ea\u5df1\u5462\uff1f\u8bbe\u60f3\u6211\u4eec\u5982\u679c\u80fd\u591f\u72ec\u7acb\u6784\u9020\u4e24\u4e2a\u903b\u8f91\uff0c\u5c31\u53ef\u4ee5\u5229\u7528\u5982\u7eaf\u51fd\u6570\u5f0f\u8bed\u8a00\u8fd9\u6837\u5177\u6709\u5f3a\u5927\u63cf\u8ff0\u529b\u7684\u5de5\u5177\uff0c\u5be5\u5be5\u6570\u7b14\u523b\u753b\u51fa\u7b97\u6cd5\uff1b\u7136\u540e\u518d\u9488\u5bf9\u5177\u4f53\u7684\u786c\u4ef6\u5e73\u53f0\uff0c\u5b9e\u73b0\u76f8\u5e94\u7684\u4f18\u5316\u8ba1\u7b97\u65b9\u6848\u3002\u4e0d\u540c\u7684\u5e73\u53f0\u95f4\u7684\u79fb\u690d\u53ea\u9700\u8981\u66ff\u6362\u8fd9\u4e2a\u4f18\u5316\u65b9\u6848\u90e8\u5206\u3002\u66f4\u597d\u7684\u4e00\u70b9\u662f\uff0c\u6211\u4eec\u53ef\u4ee5\u5c1d\u8bd5\u66f4\u6fc0\u8fdb\u7684\u4f18\u5316\u65b9\u6848\uff0c\u6d4b\u8bd5\u5404\u79cd\u5404\u6837\u7684\u65b9\u6848\uff0c\u8fd9\u4e0d\u8fc7\u662f\u4e2a\u66ff\u6362\u800c\u5df2\uff0c\u800c\u4e14\u5bf9\u7b97\u6cd5\u7684\u529f\u80fd\u672c\u8eab\u6ca1\u6709\u5f71\u54cd\u3002<\/p>\n

\u89e3\u8026\u5de5\u4f5c\u7684\u96be\u5ea6\u4e00\u5b9a\u7a0b\u5ea6\u4e0a\u53d6\u51b3\u4e8e\u8981\u89e3\u8026\u7684\u4e24\u4e2a\u6982\u5ff5\u662f\u5426\u80fd\u591f\u6e05\u6670\u7684\u533a\u5206\u5f00\u6765\u3002\u7b97\u6cd5\u63cf\u8ff0\u548c\u6027\u80fd\u4f18\u5316\u7684\u89e3\u8026\u662f\u4e0d\u5bb9\u6613\u7684\uff0c\u56e0\u4e3a\u4e00\u822c\u8bf4\u6765\u8fd9\u4e24\u4e2a\u6982\u5ff5\u4e0d\u6613\u533a\u5206\u3002\u4f46\u5728\u56fe\u50cf\u5904\u7406\u8fd9\u6837\u7684\u9886\u57df\u91cc\uff0c\u8ba1\u7b97\u5177\u6709\u5178\u578b\u7684\u6a21\u5f0f\uff08\u6570\u636e\u5728pipeline\u4e0a\u6d41\u52a8\uff0c\u88ab\u5404\u4e2a\u8282\u70b9\u4f9d\u6b21\u5904\u7406\uff09\uff0c\u6211\u4eec\u4ecd\u7136\u53ef\u4ee5\u628a\u4e8c\u8005\u5f88\u597d\u5730\u89e3\u8026\u3002<\/p>\n

Halide<\/a>\u5c31\u662f\u8fd9\u6837\u4e00\u95e8\u8bed\u8a00\u3002<\/p>\n

Halide\u662f\u7531MIT\u3001Adobe\u548cStanford\u7b49\u673a\u6784\u5408\u4f5c\u5b9e\u73b0\u7684\u56fe\u50cf\u5904\u7406\u8bed\u8a00\uff0c\u5b83\u7684\u6838\u5fc3\u601d\u60f3\u5373\u89e3\u8026\u7b97\u6cd5\u548c\u4f18\u5316\uff0c\u4e8b\u5b9e\u4e5f\u8bc1\u660e\u5b83\u662f\u6210\u529f\u7684\uff0c\u5728\u5404\u79cd\u5b9e\u4f8b\u4e2d\u5b83\u5747\u4ee5\u51e0\u5206\u4e4b\u4e00\u7684\u4ee3\u7801\u91cf\u5b9e\u73b0\u51fa\u540c\u7b49\u6216\u8005\u6570\u500d\u4e8e\u624b\u5de5C++\u4ee3\u7801\u7684\u6548\u80fd\uff0c\u66f4\u4e0d\u7528\u63d0\u4ee3\u7801\u7684\u53ef\u7ef4\u62a4\u6027\u548c\u5f00\u53d1\u6548\u7387\u3002<\/p>\n

\u5148\u4e0a\u4f8b\u5b50\uff08\u6765\u81eaHalide\u7684\u6587\u732e”Decoupling Algorithms from Schedules for Easy Optimization of Image Processing Pipelines”\uff09\uff0c\u4e09\u4e2a\u7248\u672c\u7684\u56fe\u50cf\u6a21\u7cca\u7b97\u6cd5\uff0c\u4ee5\u53ca\u4ed6\u4eec\u5404\u81ea\u7684\u6027\u80fd\u3002<\/p>\n

\r\n\r\n\r\n\r\nvoid box_filter_3x3(const Image & in, Image & blury) {\r\n\tImage blurx(in.width(), in.height()); \/\/ allocate blurx array\r\n\tfor (int y = 0; y &lt in.height(); y++)\r\n\tfor (int x = 0; x &lt in.width(); x++)\r\n\t\tblurx(x, y) = (in(x - 1, y) + in(x, y) + in(x + 1, y)) \/ 3;\r\n\tfor (int y = 0; y &lt in.height(); y++)\r\n\tfor (int x = 0; x &lt in.width(); x++)\r\n\t\tblury(x, y) = (blurx(x, y - 1) + blurx(x, y) + blurx(x, y + 1)) \/ 3;\r\n}\r\n\r\n\r\n\r\n<\/pre>\n
9.96 ms\/megapixel
\n(quad core x86)<\/p>\n
\u4ee3\u78011. \u00a0C++\u5b9e\u73b0\u56fe\u50cf\u6a21\u7cca\uff0c\u7ed3\u6784\u826f\u597d\u4f46\u6548\u7387\u5dee\u3002<\/p>\n
\r\n\r\n\r\n\r\nvoid box_filter_3x3(const Image & in, Image & blury) {\r\n\t__m128ione_third = _mm_set1_epi16(21846);\r\n#pragmaomp parallel for\r\n\tfor (int yTile = 0; yTile &lt in.height(); yTile += 32) {\r\n\t\t__m128ia, b, c, sum, avg;\r\n\t\t__m128i blurx[(256 \/ 8)*(32 + 2)]; \/\/ allocate tile blurx array\r\n\t\tfor (int xTile = 0; xTile &lt in.width(); xTile += 256) {\r\n\t\t\t__m128i*blurxPtr = blurx;\r\n\t\t\tfor (int y = -1; y &lt 32 + 1; y++) {\r\n\t\t\t\tconst uint16_t *inPtr = & (in[yTile + y][xTile]);\r\n\t\t\t\tfor (int x = 0; x &lt 256; x += 8) {\r\n\t\t\t\t\ta = _mm_loadu_si128((__m128i*)(inPtr - 1));\r\n\t\t\t\t\tb = _mm_loadu_si128((__m128i*)(inPtr + 1));\r\n\t\t\t\t\tc = _mm_load_si128((__m128i*)(inPtr));\r\n\t\t\t\t\tsum = _mm_add_epi16(_mm_add_epi16(a, b), c);\r\n\t\t\t\t\tavg = _mm_mulhi_epi16(sum, one_third);\r\n\t\t\t\t\t_mm_store_si128(blurxPtr++, avg);\r\n\t\t\t\t\tinPtr += 8;\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t\tblurxPtr = blurx;\r\n\t\t\tfor (int y = 0; y &lt 32; y++) {\r\n\t\t\t\t__m128i*outPtr = (__m128i*)(& (blury[yTile + y][xTile]));\r\n\t\t\t\tfor (int x = 0; x &lt 256; x += 8) {\r\n\t\t\t\t\ta = _mm_load_si128(blurxPtr + (2 * 256) \/ 8);\r\n\t\t\t\t\tb = _mm_load_si128(blurxPtr + 256 \/ 8);\r\n\t\t\t\t\tc = _mm_load_si128(blurxPtr++);\r\n\t\t\t\t\tsum = _mm_add_epi16(_mm_add_epi16(a, b), c);\r\n\t\t\t\t\tavg = _mm_mulhi_epi16(sum, one_third);\r\n\t\t\t\t\t_mm_store_si128(outPtr++, avg);\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t}\r\n\t}\r\n}\r\n\r\n\r\n\r\n<\/pre>\n
11x fasterthan a
\nna\u00efve implementation
\n0.9 ms\/megapixel
\n(quad core x86)<\/p>\n
\u4ee3\u78012. \u00a0\u4e0a\u4e00\u6bb5\u4ee3\u7801\u7684\u4f18\u5316\u7248\u672c\uff0c\u6548\u7387\u597d\u4f46\u7ed3\u6784\u6027\u7834\u574f\u3002<\/p>\n
\r\n\r\n\r\n\r\nFunc halide_blur(Func in) {\r\n\tFunc tmp, blurred;\r\n\tVar x, y, xi, yi;\r\n\t\/\/ The algorithm\r\n\ttmp(x, y) = (in(x - 1, y) + in(x, y) + in(x + 1, y)) \/ 3;\r\n\tblurred(x, y) = (tmp(x, y - 1) + tmp(x, y) + tmp(x, y + 1)) \/ 3;\r\n\t\/\/ The schedule\r\n\tblurred.tile(x, y, xi, yi, 256, 32)\r\n\t\t.vectorize(xi, 8).parallel(y);\r\n\ttmp.chunk(x).vectorize(x, 8);\r\n\treturn blurred;\r\n}\r\n\r\n\r\n\r\n<\/pre>\n
0.9 ms\/megapixel<\/p>\n
\u4ee3\u78013. \u00a0Halide\u4ee3\u7801\uff0c\u6e05\u6670\u7b80\u77ed\uff0c\u4e14\u4e00\u6837\u9ad8\u6548\u3002<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u770b\u5230\uff0c\u5728Halide\u6240\u5b9e\u73b0\u7684\u7248\u672c\u4e2d\uff0c\u4ee3\u7801\u5206\u6210\u4e24\u90e8\u5206\uff0c\u4e00\u90e8\u5206\u662f\u63cf\u8ff0\u7b97\u6cd5\u7684algorithm\u90e8\u5206\uff0c\u91c7\u7528\u5178\u578b\u7684\u51fd\u6570\u5f0f\u98ce\u683c\u5b9a\u4e49\u51fa\u8981\u8ba1\u7b97\u4ec0\u4e48\uff1b\u53e6\u4e00\u90e8\u5206\u5219\u662f\u6307\u5b9a\u201d\u5982\u4f55\u8ba1\u7b97\u201c\u7684schedule\u90e8\u5206\u3002Halide\u76ee\u524d\u6ca1\u6709\u81ea\u5df1\u7684\u8bed\u6cd5\u89e3\u6790\u5668\uff0c\u5b83\u7684\u524d\u7aef\u76f4\u63a5\u5d4c\u5165\u5728C++\u91cc\uff0c\u4f5c\u4e3a\u4e00\u4e2a\u5e93\u6765\u4f7f\u7528\u3002\u6211\u4eec\u6784\u9020\u51fa\u4e00\u4e2a\u56fe\u50cf\u5904\u7406\u7b97\u6cd5\u540e\uff0c\u53ef\u4ee5\u628a\u5b83\u7f16\u8bd1\u5230\u8bf8\u5982x86\/SSE, ARM v7\/NEON, CUDA, Native Client, OpenCL\u5404\u79cd\u5e73\u53f0\u4e0a\u3002<\/p>\n
\u7528schedule\u8fd9\u6837\u4e00\u4e2a\u6982\u5ff5\u6765\u62bd\u8c61\u5404\u79cd\u5404\u6837\u7684\u5e95\u5c42\u4f18\u5316\u6280\u5de7\u662f\u6574\u4e2a\u65b9\u6848\u91cc\u6700\u5173\u952e\u7684\u4e00\u73af\u3002\u4e0d\u540c\u5e73\u53f0\u6709\u4e0d\u540c\u7684\u4f18\u5316\u6280\u5de7\uff0c\u600e\u6837\u624d\u80fd\u7528\u4e00\u4e2a\u7edf\u4e00\u7684\u89c2\u70b9\u53bb\u5904\u7406\u5b83\u4eec\uff0c\u4f7f\u5f97\u6211\u4eec\u80fd\u5728\u4e00\u4e2a\u8db3\u591f\u7b80\u5355\u3001\u4e0e\u5e95\u5c42\u7ec6\u8282\u65e0\u5173\u7684\u4e16\u754c\u89c2\u91cc\u5904\u7406\u4f18\u5316\u95ee\u9898\uff1fHalide\u7528\u8fd9\u6837\u7684\u89c2\u70b9\u6765\u7edf\u4e00\u5404\u79cd\u6027\u80fd\u4f18\u5316\u65b9\u6cd5\uff1a\u5b83\u4eec\u90fd\u662f\u63a7\u5236\u5b58\u50a8\u6216\u8ba1\u7b97\u987a\u5e8f\u7684\u624b\u6bb5\u3002<\/strong>\u8fd9\u6837\uff0cHalide\u901a\u8fc7\u63d0\u4f9b\u4e00\u7cfb\u5217\u63a7\u5236\u8ba1\u7b97\u8fc7\u7a0b\u4e2d\u5b58\u50a8\u548c\u8ba1\u7b97\u987a\u5e8f\u7684\u5de5\u5177\u800c\u5e2e\u52a9\u6211\u4eec\u63cf\u7ed8\u6027\u80fd\u4f18\u5316\u65b9\u6848\u3002<\/p>\n
Halide\u76ee\u524d\u5e76\u6ca1\u6709\u592a\u591a\u7684\u8003\u8651\u7f16\u8bd1\u5668\u81ea\u52a8\u4f18\u5316\u7684\u95ee\u9898\uff0c\u4f46\u8fd9\u662f\u4e00\u4e2a\u6f02\u4eae\u7684\u5f00\u7aef\u3002\u5982\u679c\u5c06\u6765\u5728\u624b\u52a8\u4f18\u5316\u7684\u540c\u65f6\u4ecd\u6709\u5f3a\u5927\u7684\u7f16\u8bd1\u5668\u4f18\u5316\u505a\u540e\u76fe\uff0c\u5c06\u4f1a\u662f\u4e00\u756a\u4ec0\u4e48\u666f\u8c61\uff1f<\/p>\n
\u672c\u7ad9\u5c06\u6301\u7eed\u8ddf\u8e2a\u8fd9\u65b9\u9762\u7684\u8fdb\u5c55\u3002\u5173\u4e8eHalide\u8bed\u8a00\u8fdb\u4e00\u6b65\u7684\u5256\u6790\uff0c\u8bf7\u7b49\u5f85\u4e0b\u7bc7\u3002<\/p>\n
\uff08\u672a\u5b8c\u5f85\u7eed\uff09<\/p>\n","protected":false},"excerpt":{"rendered":"
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\u89e3\u8026\u5de5\u4f5c\u7684\u96be\u5ea6\u4e00\u5b9a\u7a0b\u5ea6\u4e0a\u53d6\u51b3\u4e8e\u8981\u89e3\u8026\u7684\u4e24\u4e2a\u6982\u5ff5\u662f\u5426\u80fd\u591f\u6e05\u6670\u7684\u533a\u5206\u5f00\u6765\u3002\u7b97\u6cd5\u63cf\u8ff0\u548c\u6027\u80fd\u4f18\u5316\u7684\u89e3\u8026\u662f\u4e0d\u5bb9\u6613\u7684\uff0c\u56e0\u4e3a\u4e00\u822c\u8bf4\u6765\u8fd9\u4e24\u4e2a\u6982\u5ff5\u4e0d\u6613\u533a\u5206\u3002\u4f46\u5728\u56fe\u50cf\u5904\u7406\u8fd9\u6837\u7684\u9886\u57df\u91cc\uff0c\u8ba1\u7b97\u5177\u6709\u5178\u578b\u7684\u6a21\u5f0f\uff08\u6570\u636e\u5728pipeline\u4e0a\u6d41\u52a8\uff0c\u88ab\u5404\u4e2a\u8282\u70b9\u4f9d\u6b21\u5904\u7406\uff09\uff0c\u6211\u4eec\u4ecd\u7136\u53ef\u4ee5\u628a\u4e8c\u8005\u5f88\u597d\u5730\u89e3\u8026\u3002 Halide\u5c31\u662f\u8fd9\u6837\u4e00\u95e8\u8bed\u8a00\u3002 Halide\u662f\u7531MIT\u3001Adobe\u548cStanford\u7b49\u673a\u6784\u5408\u4f5c\u5b9e\u73b0\u7684\u56fe\u50cf\u5904\u7406\u8bed\u8a00\uff0c\u5b83\u7684\u6838\u5fc3\u601d\u60f3\u5373\u89e3\u8026\u7b97\u6cd5\u548c\u4f18\u5316\uff0c\u4e8b\u5b9e\u4e5f\u8bc1\u660e\u5b83\u662f\u6210\u529f\u7684\uff0c\u5728\u5404\u79cd\u5b9e\u4f8b\u4e2d\u5b83\u5747\u4ee5\u51e0\u5206\u4e4b\u4e00\u7684\u4ee3\u7801\u91cf\u5b9e\u73b0\u51fa\u540c\u7b49\u6216\u8005\u6570\u500d\u4e8e\u624b\u5de5C++\u4ee3\u7801\u7684\u6548\u80fd\uff0c\u66f4\u4e0d\u7528\u63d0\u4ee3\u7801\u7684\u53ef\u7ef4\u62a4\u6027\u548c\u5f00\u53d1\u6548\u7387\u3002 \u5148\u4e0a\u4f8b\u5b50\uff08\u6765\u81eaHalide\u7684\u6587\u732e”Decoupling Algorithms from Schedules for Easy Optimization of Image Processing Pipelines”\uff09\uff0c\u4e09\u4e2a\u7248\u672c\u7684\u56fe\u50cf\u6a21\u7cca\u7b97\u6cd5\uff0c\u4ee5\u53ca\u4ed6\u4eec\u5404\u81ea\u7684\u6027\u80fd\u3002 9.96 ms\/megapixel (quad core x86) \u4ee3\u78011. \u00a0C++\u5b9e\u73b0\u56fe\u50cf\u6a21\u7cca\uff0c\u7ed3\u6784\u826f\u597d\u4f46\u6548\u7387\u5dee\u3002 11x fasterthan a na\u00efve implementation 0.9 ms\/megapixel (quad core x86) \u4ee3\u78012. \u00a0\u4e0a\u4e00\u6bb5\u4ee3\u7801\u7684\u4f18\u5316\u7248\u672c\uff0c\u6548\u7387\u597d\u4f46\u7ed3\u6784\u6027\u7834\u574f\u3002 0.9 ms\/megapixel \u4ee3\u78013. \u00a0Halide\u4ee3\u7801\uff0c\u6e05\u6670\u7b80\u77ed\uff0c\u4e14\u4e00\u6837\u9ad8\u6548\u3002 \u6211\u4eec\u53ef\u4ee5\u770b\u5230\uff0c\u5728Halide\u6240\u5b9e\u73b0\u7684\u7248\u672c\u4e2d\uff0c\u4ee3\u7801\u5206\u6210\u4e24\u90e8\u5206\uff0c\u4e00\u90e8\u5206\u662f\u63cf\u8ff0\u7b97\u6cd5\u7684algorithm\u90e8\u5206\uff0c\u91c7\u7528\u5178\u578b\u7684\u51fd\u6570\u5f0f\u98ce\u683c\u5b9a\u4e49\u51fa\u8981\u8ba1\u7b97\u4ec0\u4e48\uff1b\u53e6\u4e00\u90e8\u5206\u5219\u662f\u6307\u5b9a\u201d\u5982\u4f55\u8ba1\u7b97\u201c\u7684schedule\u90e8\u5206\u3002Halide\u76ee\u524d\u6ca1\u6709\u81ea\u5df1\u7684\u8bed\u6cd5\u89e3\u6790\u5668\uff0c\u5b83\u7684\u524d\u7aef\u76f4\u63a5\u5d4c\u5165\u5728C++\u91cc\uff0c\u4f5c\u4e3a\u4e00\u4e2a\u5e93\u6765\u4f7f\u7528\u3002\u6211\u4eec\u6784\u9020\u51fa\u4e00\u4e2a\u56fe\u50cf\u5904\u7406\u7b97\u6cd5\u540e\uff0c\u53ef\u4ee5\u628a\u5b83\u7f16\u8bd1\u5230\u8bf8\u5982x86\/SSE, ARM v7\/NEON, CUDA, Native Client, OpenCL\u5404\u79cd\u5e73\u53f0\u4e0a\u3002 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