{"id":51,"date":"2025-03-31T17:42:26","date_gmt":"2025-03-31T09:42:26","guid":{"rendered":"https:\/\/knoka.vip\/?p=51"},"modified":"2025-04-04T11:08:00","modified_gmt":"2025-04-04T03:08:00","slug":"%e5%bf%ab%e6%9d%a5%e6%b5%8b%e8%af%95%e4%b8%80%e4%b8%8b%e4%bd%a0%e7%9a%84-gpu-%e6%80%a7%e8%83%bd%e5%90%a7%ef%bc%81","status":"publish","type":"post","link":"http:\/\/knoka.vip\/index.php\/2025\/03\/31\/%e5%bf%ab%e6%9d%a5%e6%b5%8b%e8%af%95%e4%b8%80%e4%b8%8b%e4%bd%a0%e7%9a%84-gpu-%e6%80%a7%e8%83%bd%e5%90%a7%ef%bc%81\/","title":{"rendered":"\u5feb\u6765\u6d4b\u8bd5\u4e00\u4e0b\u4f60\u7684 GPU \u6027\u80fd\u5427\uff01"},"content":{"rendered":"\n

\u80cc\u666f\u4ecb\u7ecd<\/h2>\n\n\n\n

\u8fd1\u671f\u8001\u677f\u53d1\u4e2a\u4efb\u52a1\uff0c\u60f3\u8981\u6d4b\u8bd5\u4e00\u4e0b\u534a\u7cbe\u5ea6\u4e0b\u7684GPU\u7a00\u758f\u7b97\u529b\uff0c\u63a2\u7d22\u4e86\u591a\u4e2a\u8f6f\u4ef6\u548c\u811a\u672c\uff0c\u5305\u62ecDeepBench<\/strong>\u3001GPU-Burn\u3001Pytorch\u57fa\u51c6\u6d4b\u8bd5\u7b49\uff0c\u53d1\u73b0\u5747\u4e0d\u592a\u597d\u7528\uff1b<\/p>\n\n\n\n

\u627e\u5bfb\u8fc7\u7a0b\u4e2d\u53d1\u73b0\u4e00\u7bc7\u6587\u7ae0\uff0c\u53d1\u73b0\u60f3\u8981\u7b80\u5355\u5feb\u901f\u53c8\u6bd4\u8f83\u51c6\u7684\u7684\u6d4b\u51fa\u5b9e\u9645\u5cf0\u503c\u7b97\u529b\uff0c\u53ef\u4ee5\u4f7f\u7528CUTLASS profiler\u63d0\u4f9b\u7684GEMM\u7b97\u5b50\u8fdb\u884c\u6d4b\u8bd5\uff0c\u901a\u5e38\u8ba4\u4e3aGEMM\u662f\u8ba1\u7b97\u53d7\u9650\u7684\u7b97\u5b50\uff0c\u4e14\u5f53\u4e0b\u5927\u70ed\u7684Transformer\u6a21\u578b\u8d1f\u8f7d\u57fa\u672c\u4e0a\u90fd\u662fGEMM\uff0c\u6545GEMM\u6d4b\u5f97\u7684\u6700\u4f18\u6027\u80fd\u53ef\u4ee5\u88ab\u5f53\u4f5cGPU\u7684\u5b9e\u9645\u5cf0\u503c\u7b97\u529b\uff0c\u5f53\u7136GEMM\u662f\u901a\u7528\u77e9\u9635\uff0c\u4f46\u67e5\u770b\u5b98\u65b9\u6587\u6863\uff0c\u6700\u65b03.9.0\u7248\u672c\u5185\u6dfb\u52a0\u4e86\u7a00\u758f\u77e9\u9635\u7684\u652f\u6301\uff1b<\/p>\n\n\n\n

\"\"<\/div><\/figure>\n\n\n\n

GitHub – NVIDIA\/cutlass: CUDA Templates for Linear Algebra Subroutines\uff09<\/a> <\/p>\n\n\n\n

\u514b\u9686\u6e90\u7801\u5e76\u4e14\u6309\u7167\u6587\u6863\u91cc\u7684\u65b9\u6cd5\u7f16\u8bd1cutlass_profiler\u7a0b\u5e8f\uff0c\u4f7f\u7528\u65b9\u6cd5\u89c1cutlass_profiler –help\uff1b<\/p>\n\n\n\n

\u73af\u5883\u914d\u7f6e<\/h2>\n\n\n\n

\u9996\u5148\u51c6\u5907\u6211\u4eec\u7684\u5de5\u5177\uff0c\u4f7f\u7528\u7684\u662fNVIDIA\u63d0\u4f9b\u7684cutlass\uff0c\u8fd9\u91cc\u5927\u5bb6\u53ef\u4ee5\u590d\u5236\u5230\u6b64\u4ee3\u7406\u7f51\u7ad9\u4e2d\uff0c\u6dfb\u52a0\u4e0a\u4ee3\u7406\uff0c\u6765\u8fdb\u884c\u5feb\u901f\u7684\u4e0b\u8f7d\uff1aGithub Proxy \u6587\u4ef6\u4ee3\u7406\u52a0\u901f<\/a><\/p>\n\n\n\n

# https:\/\/github.com\/NVIDIA\/cutlass.git\ngit clone https:\/\/github.akams.cn\/https:\/\/github.com\/NVIDIA\/cutlass.git<\/code><\/pre>\n\n\n\n
\"\"<\/div><\/figure>\n\n\n\n
\u7136\u540e\u8fdb\u5165\u6587\u4ef6\u5939\u5185\uff0c\u521b\u5efabuild\u6587\u4ef6\u5939<\/p>\n\n\n\n
cd cutlass\/\nmkdir build && cd build<\/code><\/pre>\n\n\n\n
\u67e5\u770bnvcc\u8def\u5f84\uff0c\u5e76\u5bfc\u5165\u73af\u5883\u53d8\u91cf\uff0c\u4e4b\u540e\u5c31\u53ef\u4ee5\u8fdb\u884c\u7f16\u8bd1
<\/p>\n\n\n\n
\u5f00\u59cb\u6d4b\u8bd5<\/h2>\n\n\n\n
\u5bc6\u96c6\u77e9\u9635<\/h3>\n\n\n\n
    \n
  • 4090\u6d4b\u8bd5\u65f6\u7684CUTLASS\u7f16\u8bd1\u9009\u9879-DCUTLASS_NVCC_ARCHS=”89″<\/li>\n<\/ul>\n\n\n\n
      \n
    • H100\u6d4b\u8bd5\u65f6\u7684CUTLASS\u7f16\u8bd1\u9009\u9879-DCUTLASS_NVCC_ARCHS=”90a”<\/li>\n<\/ul>\n\n\n\n
        \n
      • A10\u6d4b\u8bd5\u65f6\u7684CUTLASS\u7f16\u8bd1\u9009\u9879-DCUTLASS_NVCC_ARCHS=”86″<\/li>\n<\/ul>\n\n\n\n
          \n
        • A100\u6d4b\u8bd5\u65f6\u7684CUTLASS\u7f16\u8bd1\u9009\u9879-DCUTLASS_NVCC_ARCHS=”80″<\/li>\n<\/ul>\n\n\n\n
          \u63a5\u7740\u914d\u7f6e\u4e00\u4e0bgithub\u5168\u5c40\u4ee3\u7406\uff0c\u7f16\u8bd1\u65f6\u5019\u8981\u7528\u5230<\/p>\n\n\n\n
          git config --global url.\"https:\/\/github.moeyy.xyz\/https:\/\/github.com\/\".insteadOf \"https:\/\/github.com\/\"<\/code><\/pre>\n\n\n\n
          \u5f00\u59cb\u8fdb\u884c\u7f16\u8bd1<\/p>\n\n\n\n
          which nvcc\nexport CUDACXX=\/usr\/local\/cuda\/bin\/nvcc\ncmake .. -DCUTLASS_NVCC_ARCHS=89 -DCMAKE_CUDA_COMPILER=\/usr\/local\/cuda\/bin\/nvcc<\/code><\/pre>\n\n\n\n
          \u6ce8\u610f\uff0c\u8fd9\u91cccmake\u7248\u672c\u8981\u6c423.19.3\u4ee5\u4e0a\uff0c\u56e0\u6b64\u7248\u672c\u4e0d\u591f\u5219\u9700\u8981\u8fdb\u884c\u66f4\u65b0\uff08\u6ce8\u610f\u9700\u8981\u63d0\u524d\u5220\u9664\u539f\u6709\u7248\u672c\uff0c\u8fd9\u91cc\u53ef\u4ee5\u81ea\u884c\u641c\u96c6\u4e0b\u6559\u7a0b\uff09<\/p>\n\n\n\n
          \"\"<\/div><\/figure>\n\n\n\n
          \u5f00\u59cb\u8fdb\u884c\u7f16\u8bd1\uff0c\u6ce8\u610f\u6b64\u90e8\u5206\u9700\u8981\u5f88\u957f\u65f6\u95f4\uff0c\u8010\u5fc3\u7b49\u5f85~<\/p>\n\n\n\n
          make cutlass_profiler -j16<\/code><\/pre>\n\n\n\n
          \"\"<\/div><\/figure>\n\n\n\n
          .\/tools\/profiler\/cutlass_profiler   --operation=Gemm   --m=8192   --n=8192   --k=8192   --A=f16:column   --B=f16:column   --C=f16:column   --beta=0,1,2   --profiling-iterations=1   --providers=cutlass   --output=functional-test.csv<\/code><\/pre>\n\n\n\n
          \u8be5\u547d\u4ee4\u7528\u4e8e\u4f7f\u7528CUTLASS\u5e93\u5206\u6790\u77e9\u9635\u4e58\u6cd5\uff08GEMM\uff09\u7684\u6027\u80fd\uff0c\u9a8c\u8bc1FP16\u5927\u77e9\u9635\u4e58\u6cd5\u5728\u4e0d\u540c beta<\/code> \u503c\u4e0b\u7684\u6b63\u786e\u6027\u6216\u6027\u80fd\uff0c\u9002\u7528\u4e8eGPU\u8ba1\u7b97\u4f18\u5316\u6216\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u8c03\u4f18\u3002\u53c2\u6570\u89e3\u91ca\u5982\u4e0b\uff1a<\/p>\n\n\n\n
          \u3010operation=Gemm\u3011\u6307\u5b9a\u64cd\u4f5c\u4e3a\u901a\u7528\u77e9\u9635\u4e58\u6cd5\uff08GEMM\uff09\uff0c\u5373\u8ba1\u7b97 C = alpha*A*B + beta*C<\/code>\u3002<\/p>\n\n\n\n
          \u3010m=8192\u3001–n=8192\u3001–k=8192\u3011\u5b9a\u4e49\u77e9\u9635\u7ef4\u5ea6\uff0c\u6d4b\u8bd5\u5927\u89c4\u6a21\u77e9\u9635\u8fd0\u7b97\u6027\u80fd\u3002<\/p>\n\n\n\n
            \n
          • A<\/code> \u7684\u5f62\u72b6\u4e3a M\u00d7K = 8192\u00d78192<\/code><\/li>\n\n\n\n
          • B<\/code> \u7684\u5f62\u72b6\u4e3a K\u00d7N = 8192\u00d78192<\/code><\/li>\n\n\n\n
          • C<\/code> \u7684\u5f62\u72b6\u4e3a M\u00d7N = 8192\u00d78192<\/code>
            <\/li>\n<\/ul>\n\n\n\n
            \u3010<\/strong>A=f16:column\u3001–B=f16:column\u3001–C=f16:column\u3011<\/p>\n\n\n\n
              \n
            • f16<\/code>\uff1a\u77e9\u9635\u6570\u636e\u7c7b\u578b\u4e3a\u534a\u7cbe\u5ea6\u6d6e\u70b9\uff08FP16\uff09\uff0c\u8282\u7701\u5185\u5b58\u5e26\u5bbd\u5e76\u52a0\u901f\u8ba1\u7b97\u3002<\/li>\n\n\n\n
            • column<\/code>\uff1a\u5217\u4e3b\u5e8f\u5b58\u50a8\uff08\u4e0ecuBLAS\u9ed8\u8ba4\u4e00\u81f4\uff09\uff0c\u5185\u5b58\u4e2d\u6309\u5217\u4f18\u5148\u6392\u5217\u6570\u636e\u3002<\/li>\n<\/ul>\n\n\n\n
              \u3010<\/strong>beta=0,1,2\u3011\u6d4b\u8bd5\u4e09\u79cd\u4e0d\u540c\u7684 beta<\/code> \u503c\uff1a<\/p>\n\n\n\n
                \n
              • beta=0<\/code>\uff1a\u7ed3\u679c C = alpha*A*B<\/code>\uff08\u5ffd\u7565\u539fC\u7684\u503c\uff09\u3002<\/li>\n\n\n\n
              • beta=1<\/code>\uff1a\u7ed3\u679c C = alpha*A*B + C<\/code>\uff08\u7d2f\u52a0\u5230\u539fC\uff09\u3002<\/li>\n\n\n\n
              • beta=2<\/code>\uff1a\u7ed3\u679c C = alpha*A*B + 2*C<\/code>\uff08\u9a8c\u8bc1\u590d\u6742\u8ba1\u7b97\u8def\u5f84\uff09\u3002<\/li>\n<\/ul>\n\n\n\n
                \u3010profiling-iterations=1\u3011\u6bcf\u4e2a\u914d\u7f6e\u4ec5\u8fd0\u884c1\u6b21\u8fed\u4ee3\uff0c\u5feb\u901f\u9a8c\u8bc1\u529f\u80fd\u6216\u521d\u6b65\u6027\u80fd\uff08\u975e\u7a33\u5b9a\u6027\u6d4b\u8bd5\uff09\u3002<\/p>\n\n\n\n
                \u3010providers=cutlass\u3011\u6307\u5b9a\u4f7f\u7528CUTLASS\u5e93\u5b9e\u73b0\uff0c\u800c\u975e\u5176\u4ed6\u540e\u7aef\uff08\u5982cuBLAS\uff09\uff0cCUTLASS\u662fNVIDIA\u4f18\u5316\u7684\u77e9\u9635\u8ba1\u7b97\u6a21\u677f\u5e93\u3002<\/p>\n\n\n\n
                \u3010output=functional-test.csv\u3011\u5c06\u7ed3\u679c\uff08\u5982\u8017\u65f6\u3001\u541e\u5410\u91cf\uff09\u4fdd\u5b58\u5230CSV\u6587\u4ef6\uff0c\u4fbf\u4e8e\u540e\u7eed\u5206\u6790\u3002<\/p>\n\n\n\n
                \"\"<\/div><\/figure>\n\n\n\n
                \u6700\u7ec8\u751f\u6210\u4e00\u4e2aCSV\u6587\u4ef6<\/p>\n\n\n\n
                Problem (Beta)<\/th>Operation<\/th>Runtime (ms)<\/th>GB\/s<\/th>GFLOPs<\/th><\/tr><\/thead>
                1 (\u03b2=0)<\/td>SIMT HGEMM<\/td>15.2975<\/td>24.5138<\/td>71883.9<\/td><\/tr>
                1 (\u03b2=0)<\/td>TensorOp F16 S1688<\/td>7.40659<\/td>50.6306<\/td>148469<\/td><\/tr>
                1 (\u03b2=0)<\/td>TensorOp H1688<\/td>4.76467<\/td>78.7043<\/td>230792<\/td><\/tr>
                1 (\u03b2=0)<\/td>TensorOp F16 S16816<\/td>6.31603<\/td>59.3727<\/td>174104<\/td><\/tr>
                1 (\u03b2=0)<\/td>TensorOp H16816<\/td>3.43142<\/td>109.284<\/td>320463<\/td><\/tr>
                2 (\u03b2=1)<\/td>SIMT HGEMM<\/td>15.5003<\/td>32.2575<\/td>70943.6<\/td><\/tr>
                2 (\u03b2=1)<\/td>TensorOp F16 S1688<\/td>7.48749<\/td>66.7781<\/td>146864<\/td><\/tr>
                2 (\u03b2=1)<\/td>TensorOp H1688<\/td>5.0185<\/td>99.6314<\/td>219119<\/td><\/tr>
                2 (\u03b2=1)<\/td>TensorOp F16 S16816<\/td>6.38157<\/td>78.3507<\/td>172316<\/td><\/tr>
                2 (\u03b2=1)<\/td>TensorOp H16816<\/td>3.30051<\/td>151.492<\/td>333174<\/td><\/tr>
                3 (\u03b2=2)<\/td>SIMT HGEMM<\/td>15.4532<\/td>32.3558<\/td>71159.8<\/td><\/tr>
                3 (\u03b2=2)<\/td>TensorOp F16 S1688<\/td>7.48339<\/td>66.8146<\/td>146945<\/td><\/tr>
                3 (\u03b2=2)<\/td>TensorOp H1688<\/td>5.05574<\/td>98.8974<\/td>217504<\/td><\/tr>
                3 (\u03b2=2)<\/td>TensorOp F16 S16816<\/td>6.38362<\/td>78.3255<\/td>172261<\/td><\/tr>
                3 (\u03b2=2)<\/td>TensorOp H16816<\/td>3.30035<\/td>151.499<\/td>333190<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
                \u7a00\u758f\u77e9\u9635\uff08\u540e\u7eed\u66f4\u65b0\uff09<\/h3>\n\n\n\n
                \u7a00\u758f\u77e9\u9635\u6d4b\u8bd5\u9700\u8981\u91cd\u65b0\u8fdb\u884c\u7f16\u8bd1\uff0c\u5728\u7f16\u8bd1\u65f6\u5019\u542f\u52a8\u7a00\u758f\u77e9\u9635\u529f\u80fd\u652f\u6301\uff0c\u5982\u679c\u672a\u542f\u7528\u7a00\u758f\u652f\u6301\uff0c\u76f8\u5173\u5185\u6838\u4f1a\u88ab\u8df3\u8fc7\uff0c\u5f53\u524d\u5b98\u65b9\u662f\u4e13\u95e8\u9488\u5bf9 Blackwell SM120a \u67b6\u6784\u7684\u5757\u7f29\u653e\u7a00\u758f GEMM\uff08Block-Scaled Sparse GEMM\uff09\u8bbe\u8ba1\u4e86\u5355\u5143\u6d4b\u8bd5<\/strong>\uff0c\u56e0\u6b64\u76ee\u524d\u4ec5\u652f\u630150\u7cfb\u5217\u663e\u5361\uff0c\u8fd8\u65e0\u6cd5\u6d4b\u8bd5\u5176\u4ed6\u5361\u3002<\/p>\n\n\n\n
                \"\"<\/div><\/figure>\n","protected":false},"excerpt":{"rendered":"
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