{"id":545,"date":"2024-08-08T09:16:31","date_gmt":"2024-08-08T09:16:31","guid":{"rendered":"https:\/\/www.ienlab.com\/?p=545"},"modified":"2024-08-08T10:18:44","modified_gmt":"2024-08-08T10:18:44","slug":"crux-gpu-efficient-communication-scheduling-for-deep-learning-training","status":"publish","type":"post","link":"https:\/\/www.ienlab.com\/?p=545","title":{"rendered":"Crux: GPU-Efficient\u00a0Communication Scheduling for Deep\u00a0Learning Training"},"content":{"rendered":"\n

Crux:GPU\u9ad8\u6548\u7684\u6df1\u5ea6\u5b66\u4e60\u8bad\u7ec3\u901a\u4fe1\u8c03\u5ea6<\/p>\n\n\n\n

\u4f5c\u8005\uff1a\u963f\u91cc\u4e91\u56e2\u961f<\/p>\n\n\n\n

Author\uff1aJiamin Cao, Yu Guan, Kun Qian, Jiaqi Gao, Wencong Xiao, Jianbo Dong\uff0cBinzhang Fu, Dennis Cai, Ennan Zhai<\/p>\n\n\n\n

\u8bba\u6587\u6458\u8981\u539f\u6587\uff1a<\/strong>Deep learning training (DLT), e.g., large language model (LLM)training,has become one of the most important services in multi-tenant cloud computing. By deeply studying in-production DLTjobs, we observed that communication contention among differ-ent DLT jobs seriously influences the overall GPU computationutilization, resulting in the low efficiency of the training cluster.In this paper, we presentCrux, a communication scheduler thataims to maximize GPU computation utilizationby mitigating the communication contention among DLT jobs. Maximizing GPU com-putation utilization for DLT, nevertheless, is NP-Complete; thus,we formulate and prove a novel theorem to approach this goal by GPUintensity-aware communication scheduling. Then, we propose an approach that prioritizes the DLT flows with high GPU com-putation intensity, reducing potential communication contention.Our 96-GPU testbed experiments show that Crux improves 8.3% to 14.8% GPU computation utilization. The large-scale production trace-based simulation further shows that Crux increases GPU computation utilization by up to 23% compared with alternatives including Sincronia, TACCL, and CASSINI.<\/p>\n\n\n\n

\u8bba\u6587\u6458\u8981\u4e2d\u6587\uff1a<\/strong>\u6df1\u5ea6\u5b66\u4e60\u8bad\u7ec3\uff08DLT\uff09\uff0c\u4f8b\u5982\uff1a\u5927\u578b\u8bed\u8a00\u6a21\u578b\uff08LLM\uff09\u8bad\u7ec3\u5df2\u6210\u4e3a\u591a\u79df\u6237\u4e91\u8ba1\u7b97\u4e2d\u6700\u91cd\u8981\u7684\u670d\u52a1\u4e4b\u4e00\u3002\u901a\u8fc7\u5bf9\u751f\u4ea7\u4e2dDLT\u4f5c\u4e1a\u7684\u6df1\u5165\u7814\u7a76\uff0c\u6211\u4eec\u53d1\u73b0DLT\u4f5c\u4e1a\u95f4\u7684\u901a\u4fe1\u7ade\u4e89\u4e25\u91cd\u5f71\u54cd\u4e86GPU\u7684\u6574\u4f53\u8ba1\u7b97\u5229\u7528\u7387\uff0c\u5bfc\u81f4\u8bad\u7ec3\u96c6\u7fa4\u7684\u6548\u7387\u4f4e\u4e0b<\/span>\u3002\u672c\u6587\u63d0\u51fa\u4e86\u4e00\u79cd\u901a\u4fe1\u8c03\u5ea6\u5668Crux\uff0c\u65e8\u5728\u901a\u8fc7\u51cf\u5c11DLT\u4f5c\u4e1a\u4e4b\u95f4\u7684\u901a\u4fe1\u4e89\u7528\u6765\u6700\u5927\u5316GPU\u8ba1\u7b97\u5229\u7528\u7387.\u7136\u800c\uff0cDLT\u7684GPU\u8ba1\u7b97\u5229\u7528\u7387\u6700\u5927\u5316\u662fNP\u5b8c\u5168\u7684;\u56e0\u6b64\uff0c\u6211\u4eec\u516c\u5f0f\u5316\u5e76\u8bc1\u660e\u4e86\u4e00\u4e2a\u65b0\u7684\u5b9a\u7406\uff0c\u4ee5\u901a\u8fc7GPU\u5f3a\u5ea6\u611f\u77e5\u901a\u4fe1\u8c03\u5ea6\u6765\u5b9e\u73b0\u8fd9\u4e00\u76ee\u6807<\/span>\u3002\u5728\u6b64\u57fa\u7840\u4e0a\uff0c\u63d0\u51fa\u4e86\u4e00\u79cd\u5bf9GPU\u8ba1\u7b97\u5f3a\u5ea6\u8f83\u9ad8\u7684DLT\u6d41\u8fdb\u884c\u4f18\u5148\u7ea7\u6392\u5e8f\u7684\u65b9\u6cd5\uff0c\u51cf\u5c11\u4e86\u6f5c\u5728\u7684\u901a\u4fe1\u51b2\u7a81.\u6211\u4eec\u768496-GPU\u6d4b\u8bd5\u5e73\u53f0\u5b9e\u9a8c\u8868\u660e\uff0cCrux\u5c06GPU\u8ba1\u7b97\u5229\u7528\u7387\u63d0\u9ad8\u4e868.3%\u81f314.8%\u3002\u57fa\u4e8e\u5927\u89c4\u6a21\u751f\u4ea7\u8f68\u8ff9\u7684\u6a21\u62df\u8fdb\u4e00\u6b65\u8868\u660e\uff0c\u4e0eSincronia\u3001TACCL\u548cCASSINI\u7b49\u66ff\u4ee3\u65b9\u6848\u76f8\u6bd4\uff0cCrux\u5c06GPU\u8ba1\u7b97\u5229\u7528\u7387\u63d0\u9ad8\u4e8623%\u3002<\/p>\n\n\n\n

\u7814\u7a76\u95ee\u9898\u3001\u5173\u952e\u95ee\u9898\uff1a<\/strong>\u672c\u6587\u7684\u7814\u7a76\u95ee\u9898\u662f \u5982\u4f55\u901a\u8fc7\u4f18\u5316\u591a\u79df\u6237\u6df1\u5ea6\u5b66\u4e60\u8bad\u7ec3\uff08DLT\uff09\u96c6\u7fa4\u4e2d\u7684\u901a\u4fe1\u8c03\u5ea6\uff0c\u63d0\u9ad8 GPU \u8ba1\u7b97\u5229\u7528\u7387\uff0c\u4ece\u800c\u63d0\u5347\u8bad\u7ec3\u6548\u7387\u548c\u96c6\u7fa4\u6536\u76ca<\/span>\u3002<\/p>\n\n\n\n

\u5177\u4f53\u800c\u8a00\uff0c\u7814\u7a76\u95ee\u9898\u5305\u542b\u4ee5\u4e0b\u4e24\u4e2a\u65b9\u9762\uff1a<\/p>\n\n\n\n

1.\u5206\u6790 DLT \u4efb\u52a1\u4e4b\u95f4\u7684\u901a\u4fe1\u7ade\u4e89\u95ee\u9898\uff1a \u7814\u7a76\u751f\u4ea7\u73af\u5883\u4e2d DLT \u4efb\u52a1\u4e4b\u95f4\u7684\u901a\u4fe1\u7ade\u4e89\u73b0\u8c61\uff0c\u5206\u6790\u5176\u4ea7\u751f\u539f\u56e0\u548c\u5f71\u54cd\uff0c\u5e76\u63d0\u51fa\u89e3\u51b3\u65b9\u6848\u3002<\/p>\n\n\n\n

2.\u8bbe\u8ba1\u9ad8\u6548\u7684\u901a\u4fe1\u8c03\u5ea6\u7b97\u6cd5\uff1a \u57fa\u4e8e GPU \u5f3a\u5ea6\u6982\u5ff5\uff0c\u8bbe\u8ba1\u9ad8\u6548\u7684\u901a\u4fe1\u8c03\u5ea6\u7b97\u6cd5\uff0c\u4f18\u5148\u8c03\u5ea6 GPU \u5f3a\u5ea6\u9ad8\u7684\u4efb\u52a1\uff0c\u4ece\u800c\u6700\u5927\u5316 GPU \u5229\u7528\u7387\u3002<\/p>\n\n\n\n

\u7814\u7a76\u52a8\u673a\uff1a<\/strong><\/strong><\/p>\n\n\n\n

1.\u751f\u4ea7\u73af\u5883\u4e2d DLT \u4efb\u52a1\u901a\u4fe1\u7ade\u4e89\u666e\u904d\u5b58\u5728\uff1a \u968f\u7740\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u7684\u89c4\u6a21\u4e0d\u65ad\u6269\u5927\uff0cDLT \u4efb\u52a1\u5728\u5171\u4eab GPU \u96c6\u7fa4\u4e2d<\/span>\u6267\u884c\u65f6\uff0c\u4e0d\u540c\u4efb\u52a1\u4e4b\u95f4\u4f1a\u4ea7\u751f\u901a\u4fe1\u7ade\u4e89\uff0c\u5bfc\u81f4 GPU \u5229\u7528\u7387\u4f4e\u4e0b\uff0c\u8bad\u7ec3\u6548\u7387\u964d\u4f4e\uff0c\u96c6\u7fa4\u6536\u76ca\u53d7\u635f\u3002<\/p>\n\n\n\n

2.\u73b0\u6709\u901a\u4fe1\u8c03\u5ea6\u65b9\u6cd5\u65e0\u6cd5\u6709\u6548\u89e3\u51b3\u901a\u4fe1\u7ade\u4e89\u95ee\u9898\uff1a \u73b0\u6709\u7684 DLT \u901a\u4fe1\u8c03\u5ea6\u65b9\u6cd5\u4e3b\u8981\u96c6\u4e2d\u5728\u5355\u4efb\u52a1\u5185\u90e8<\/span>\uff0c\u5ffd\u7565\u4e86\u4e0d\u540c\u4efb\u52a1\u4e4b\u95f4\u7684\u901a\u4fe1\u7ade\u4e89\uff0c\u65e0\u6cd5\u6709\u6548\u63d0\u9ad8\u6574\u4f53 GPU \u5229\u7528\u7387\u3002<\/p>\n\n\n\n

3.\u63d0\u9ad8 GPU \u5229\u7528\u7387\u5bf9 DLT \u96c6\u7fa4\u81f3\u5173\u91cd\u8981\uff1a GPU \u5229\u7528\u7387\u662f DLT \u96c6\u7fa4\u6027\u80fd\u7684\u91cd\u8981\u6307\u6807\uff0c\u76f4\u63a5\u5f71\u54cd\u5230\u8bad\u7ec3\u6548\u7387\u548c\u96c6\u7fa4\u6536\u76ca\u3002\u56e0\u6b64\uff0c\u4f18\u5316 GPU \u5229\u7528\u7387\u5bf9\u4e8e DLT \u96c6\u7fa4\u81f3\u5173\u91cd\u8981\u3002<\/p>\n\n\n\n

\u7814\u7a76\u610f\u4e49\uff1a<\/strong><\/strong><\/p>\n\n\n\n

    \n
  1. \u63d0\u5347 DLT \u8bad\u7ec3\u6548\u7387\uff1a \u901a\u8fc7\u4f18\u5316 DLT \u96c6\u7fa4\u7684\u901a\u4fe1\u8c03\u5ea6<\/span>\uff0c\u51cf\u5c11\u901a\u4fe1\u7ade\u4e89\uff0c\u53ef\u4ee5\u63d0\u9ad8 GPU \u5229\u7528\u7387\uff0c\u4ece\u800c\u7f29\u77ed\u8bad\u7ec3\u65f6\u95f4\uff0c\u63d0\u5347\u8bad\u7ec3\u6548\u7387\u3002<\/li>\n\n\n\n
  2. \u63d0\u9ad8\u96c6\u7fa4\u6536\u76ca\uff1a GPU \u5229\u7528\u7387\u8d8a\u9ad8\uff0c\u96c6\u7fa4\u7684\u541e\u5410\u91cf\u8d8a\u5927\uff0c\u53ef\u4ee5\u5904\u7406\u66f4\u591a\u7684\u8bad\u7ec3\u4efb\u52a1\uff0c\u4ece\u800c\u63d0\u9ad8\u96c6\u7fa4\u7684\u6536\u76ca<\/span>\u3002<\/li>\n\n\n\n
  3. \u63a8\u52a8 DLT \u96c6\u7fa4\u53d1\u5c55\uff1a \u901a\u8fc7\u4f18\u5316 DLT \u96c6\u7fa4\u7684\u901a\u4fe1\u8c03\u5ea6\uff0c\u53ef\u4ee5\u4fc3\u8fdb DLT \u96c6\u7fa4\u7684\u53d1\u5c55\uff0c\u4f7f\u5176\u80fd\u591f\u66f4\u597d\u5730\u6ee1\u8db3\u65e5\u76ca\u589e\u957f\u7684\u8ba1\u7b97\u9700\u6c42\u3002<\/li>\n<\/ol>\n\n\n\n

    \u7814\u7a76\u5185\u5bb9\uff08\u7b97\u6cd5\u3001\u65b9\u6cd5\u3001\u6280\u672f\u3001\u6a21\u578b\uff09<\/strong>\uff1a<\/p>\n\n\n\n

      \n
    1. GPU \u5f3a\u5ea6\u6982\u5ff5\uff1a<\/li>\n<\/ol>\n\n\n\n

      \u5b9a\u4e49 GPU \u5f3a\u5ea6\u6765\u8861\u91cf\u4efb\u52a1\u5bf9 GPU \u5229\u7528\u7387\u7684\u5f71\u54cd\uff0c\u5e76\u4ee5\u6b64\u4f5c\u4e3a\u901a\u4fe1\u8c03\u5ea6\u7684\u4f9d\u636e\u3002<\/p>\n\n\n\n

      GPU \u5f3a\u5ea6\u8d8a\u9ad8\uff0c\u4efb\u52a1\u5bf9 GPU \u5229\u7528\u7387\u7684\u5f71\u54cd\u8d8a\u5927\uff0c\u56e0\u6b64\u5728\u901a\u4fe1\u8c03\u5ea6\u4e2d\u5e94\u4f18\u5148\u8003\u8651\u3002<\/p>\n\n\n\n

      \"\"<\/figure>\n\n\n\n
        \n
      • \u8def\u5f84\u9009\u62e9\u7b97\u6cd5\uff1a<\/li>\n<\/ul>\n\n\n\n

        \u8bbe\u8ba1\u8def\u5f84\u9009\u62e9\u7b97\u6cd5\uff0c\u9009\u62e9\u5bf9\u9ad8 GPU \u5f3a\u5ea6\u4efb\u52a1\u5f71\u54cd\u8f83\u5c0f\u7684\u8def\u5f84\uff0c\u907f\u514d\u901a\u4fe1\u7ade\u4e89\u3002<\/p>\n\n\n\n

        \u7b97\u6cd5\u4f1a\u6839\u636e\u4efb\u52a1\u7684 GPU \u5f3a\u5ea6\u548c\u7f51\u7edc\u62d3\u6251\u7ed3\u6784\u8fdb\u884c\u8def\u5f84\u9009\u62e9\uff0c\u4ee5\u786e\u4fdd\u9ad8 GPU \u5f3a\u5ea6\u4efb\u52a1\u80fd\u591f\u4f18\u5148\u4f7f\u7528\u5e26\u5bbd\u8f83\u5bbd\u7684\u8def\u5f84\u3002<\/p>\n\n\n\n

          \n
        • \u4f18\u5148\u7ea7\u5206\u914d\u7b97\u6cd5\uff1a<\/li>\n<\/ul>\n\n\n\n

          \u8bbe\u8ba1\u4f18\u5148\u7ea7\u5206\u914d\u7b97\u6cd5\uff0c\u8003\u8651 DLT \u4efb\u52a1\u7684\u7279\u5f81\uff08\u5982\u8fed\u4ee3\u548c\u8ba1\u7b97-\u901a\u4fe1\u91cd\u53e0\uff09\uff0c\u5bf9\u4efb\u52a1\u8fdb\u884c\u4f18\u5148\u7ea7\u5206\u914d\uff0c\u4f18\u5148\u8c03\u5ea6 GPU \u5f3a\u5ea6\u9ad8\u7684\u4efb\u52a1\u3002<\/p>\n\n\n\n

          \u7b97\u6cd5\u4f1a\u6839\u636e\u4efb\u52a1\u7684 GPU \u5f3a\u5ea6\u3001\u8fed\u4ee3\u65f6\u95f4\u3001\u8ba1\u7b97-\u901a\u4fe1\u91cd\u53e0\u7a0b\u5ea6\u7b49\u56e0\u7d20\u8fdb\u884c\u4f18\u5148\u7ea7\u5206\u914d\uff0c\u4ee5\u786e\u4fdd\u9ad8 GPU \u5f3a\u5ea6\u4efb\u52a1\u80fd\u591f\u4f18\u5148\u83b7\u5f97\u7f51\u7edc\u8d44\u6e90\u3002<\/p>\n\n\n\n

          \"\"<\/figure>\n\n\n\n
            \n
          • \u4f18\u5148\u7ea7\u538b\u7f29\u7b97\u6cd5\uff1a<\/li>\n<\/ul>\n\n\n\n

            \u8bbe\u8ba1\u4f18\u5148\u7ea7\u538b\u7f29\u7b97\u6cd5\uff0c\u5c06\u4f18\u5148\u7ea7\u5206\u914d\u7ed3\u679c\u538b\u7f29\u5230\u6709\u9650\u7684\u4f18\u5148\u7ea7\u7ea7\u522b\u4e0b\uff0c\u6700\u5c0f\u5316 GPU \u5229\u7528\u7387\u635f\u5931\u3002<\/p>\n\n\n\n

            \u7b97\u6cd5\u4f1a\u6839\u636e\u4efb\u52a1\u7684 GPU \u5f3a\u5ea6\u3001\u7f51\u7edc\u62d3\u6251\u7ed3\u6784\u7b49\u56e0\u7d20\u8fdb\u884c\u4f18\u5148\u7ea7\u538b\u7f29\uff0c\u4ee5\u786e\u4fdd\u9ad8 GPU \u5f3a\u5ea6\u4efb\u52a1\u80fd\u591f\u4f18\u5148\u83b7\u5f97\u7f51\u7edc\u8d44\u6e90\uff0c\u540c\u65f6\u5c3d\u91cf\u51cf\u5c11\u4f4e GPU \u5f3a\u5ea6\u4efb\u52a1\u4e4b\u95f4\u7684\u7ade\u4e89\u3002<\/p>\n\n\n\n

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

            \u4e3b\u8981\u8d21\u732e<\/strong><\/strong><\/p>\n\n\n\n

              \n
            1. \u5bf9\u6211\u4eec\u7684\u591a\u79df\u6237\u751f\u4ea7\u57f9\u8bad\u96c6\u7fa4\u7684\u5206\u6790\u8868\u660e\uff0c36.3%\u7684DLT\u4f5c\u4e1a\u53ef\u80fd\u4f1a\u9047\u5230\u4e0e\u5176\u4ed6\u4f5c\u4e1a\u7684\u901a\u4fe1\u7ade\u4e89\uff0c\u4ece\u800c\u5bfc\u81f4\u5927\u91cfGPU\u6d6a\u8d39\u3002\u6211\u4eec\u8ba4\u4e3a\u4f5c\u4e1a\u95f4\u901a\u4fe1\u8c03\u5ea6\u5bf9\u4e8e\u63d0\u9ad8GPU\u5229\u7528\u7387\u662f\u5fc5\u8981\u7684\u3002\u6211\u4eec\u5728https:\/\/github.com\/alibaba\/alibaba-lingjun-dataset-2023\u4e0a\u516c\u5f00\u4e86\u6211\u4eec\u7684\u6570\u636e\u96c6\u3002<\/li>\n\n\n\n
            2. \u6211\u4eec\u5c06GPU\u5229\u7528\u7387\u6700\u5927\u5316\u8fd9\u4e00NP\u5b8c\u5168\uff08NPC\uff09\u95ee\u9898\u8f6c\u5316\u4e3aGPU\u5f3a\u5ea6\u611f\u77e5\u901a\u4fe1\u8c03\u5ea6\u95ee\u9898\u3002\u6211\u4eec\u8bbe\u8ba1\u4e86\u4e00\u4e2a\u7cfb\u7edfCrux\u6765\u4f18\u5316DLT\u96c6\u7fa4\u4e2d\u7684GPU\u5229\u7528\u7387\u3002Crux\u5f15\u5165\u4e86\uff081\uff09\u4e00\u79cd\u8def\u5f84\u9009\u62e9\u7b97\u6cd5\uff0c\u901a\u8fc7\u4e3a\u5177\u6709\u8f83\u9ad8GPU\u5bc6\u5ea6\u7684\u4f5c\u4e1a\u9009\u62e9\u6700\u4e0d\u62e5\u585e\u7684\u8def\u5f84\u6765\u7f13\u89e3\u901a\u4fe1\u4e89\u7528\uff0c\uff082\uff09\u4f18\u5148\u7ea7\u5206\u914d\u7b97\u6cd5\uff0c\u8003\u8651DLT\u7279\u6027\uff0c\u5982\u591a\u6b21\u8fed\u4ee3\u548c\u901a\u4fe1-\u8ba1\u7b97\u91cd\u53e0\uff0c\u4ee5\u53ca\uff083\uff09\u4e00\u79cd\u9ad8\u6548\u7684\u4f18\u5148\u7ea7\u538b\u7f29\u7b97\u6cd5\uff0c\u4ee5\u9002\u5e94\u5b9e\u9645NIC\u548c\u4ea4\u6362\u673a\u4e0a\u6709\u9650\u7684\u4f18\u5148\u7ea7\u3002<\/li>\n\n\n\n
            3. \u6211\u4eec\u7684\u5b9e\u9a8c\u6d4b\u8bd5\u5e73\u53f0\u753196\u4e2aNvidia A100 GPU\u7ec4\u6210\uff0c\u8868\u660eCrux\u5728\u5b9e\u9645\u6a21\u578b\uff08\u4f8b\u5982\uff0cGPT\u3001BERT\u548cResNet\uff09\u3002\u6211\u4eec\u57fa\u4e8e\u751f\u4ea7\u8ddf\u8e2a\uff082\uff0c000 + GPU\uff0c5\uff0c000+\u4f5c\u4e1a\uff09\u7684\u6a21\u62df\u8868\u660e\uff0c\u4e0e\u6700\u5148\u8fdb\u7684\u89e3\u51b3\u65b9\u6848\uff08Sincronia\uff0cCASSINI\u548cTACCL\uff09\u76f8\u6bd4\uff0cCrux\u5728\u5404\u79cd\u96c6\u7fa4\u7f51\u7edc\u67b6\u6784\u4e0b\u5c06GPU\u5229\u7528\u7387\u63d0\u9ad8\u4e865%\u81f323%\u3002<\/li>\n<\/ol>\n\n\n\n

              \u521b\u65b0\u70b9\u3001\u521b\u65b0\u6027<\/strong>\uff1a<\/p>\n\n\n\n

              \u63d0\u51fa\u4e86 GPU \u5f3a\u5ea6\u6982\u5ff5\u5e76\u6784\u5efa\u4e86\u57fa\u4e8e GPU \u5f3a\u5ea6\u7684\u901a\u4fe1\u8c03\u5ea6\u65b9\u6cd5\uff0c\u4ece\u800c\u6709\u6548\u5730\u89e3\u51b3\u4e86 DLT \u96c6\u7fa4\u4e2d\u4efb\u52a1\u4e4b\u95f4\u7684\u901a\u4fe1\u7ade\u4e89\u95ee\u9898\uff0c\u63d0\u9ad8\u4e86 GPU \u5229\u7528\u7387\u548c\u8bad\u7ec3\u6548\u7387\u3002<\/p>\n\n\n\n

              \u6280\u672f\u96be\u70b9<\/strong>\uff1a<\/p>\n\n\n\n

                \n
              1. \u901a\u4fe1\u7ade\u4e89\u7684\u590d\u6742\u6027\uff0c\u4ee5\u53ca\u5982\u4f55\u51c6\u786e\u8bc4\u4f30\u548c\u8c03\u5ea6\u901a\u4fe1\u7ade\u4e89\u3002<\/li>\n\n\n\n
              2. GPU \u5f3a\u5ea6\u7684\u8ba1\u7b97\uff0c\u4ee5\u53ca\u5982\u4f55\u9ad8\u6548\u5730\u8ba1\u7b97 GPU \u5f3a\u5ea6\u3002<\/li>\n\n\n\n
              3. \u8c03\u5ea6\u7b97\u6cd5\u7684\u8bbe\u8ba1\uff0c\u4ee5\u53ca\u5982\u4f55\u8bbe\u8ba1\u9ad8\u6548\u7684\u8c03\u5ea6\u7b97\u6cd5\u3002<\/li>\n\n\n\n
              4. \u7cfb\u7edf\u7684\u53ef\u6269\u5c55\u6027\u548c\u9c81\u68d2\u6027\uff0c\u4ee5\u53ca\u5982\u4f55\u63d0\u9ad8\u7cfb\u7edf\u7684\u53ef\u6269\u5c55\u6027\u548c\u9c81\u68d2\u6027\u3002<\/li>\n<\/ol>\n\n\n\n

                \u8fdb\u4e00\u6b65\u7814\u7a76\u601d\u8def (Future Work)<\/strong>\uff1a<\/p>\n\n\n\n

                Crux \u8bba\u6587\u63d0\u51fa\u7684 GPU \u5f3a\u5ea6\u6982\u5ff5\u548c\u901a\u4fe1\u8c03\u5ea6\u65b9\u6cd5\u4e3a DLT \u96c6\u7fa4\u7684\u6027\u80fd\u4f18\u5316\u63d0\u4f9b\u4e86\u65b0\u7684\u601d\u8def\u3002\u672a\u6765\u7814\u7a76\u53ef\u4ee5\u4ece\u4ee5\u4e0b\u51e0\u4e2a\u65b9\u9762\u8fdb\u884c\u63a2\u7d22\uff1a<\/p>\n\n\n\n

                GPU \u5f3a\u5ea6\u7684\u7ec6\u5316\uff1a \u8003\u8651\u6570\u636e\u4f20\u8f93\u6a21\u5f0f\u3001\u6570\u636e\u7c7b\u578b\u3001\u901a\u4fe1\u534f\u8bae\u7b49\u56e0\u7d20\uff0c\u8bbe\u8ba1\u66f4\u7cbe\u7ec6\u7684 GPU \u5f3a\u5ea6\u8ba1\u7b97\u65b9\u6cd5\uff0c\u4ee5\u66f4\u51c6\u786e\u5730\u53cd\u6620\u4efb\u52a1\u7684\u901a\u4fe1\u9700\u6c42\u3002<\/p>\n\n\n\n

                \u591a\u76ee\u6807\u4f18\u5316\uff1a \u5728\u6700\u5927\u5316 GPU \u5229\u7528\u7387\u7684\u57fa\u7840\u4e0a\uff0c\u8003\u8651\u6700\u5c0f\u5316\u4efb\u52a1\u5b8c\u6210\u65f6\u95f4\u3001\u63d0\u9ad8\u4efb\u52a1\u516c\u5e73\u6027\u3001\u6700\u5927\u5316\u541e\u5410\u91cf\u7b49\u591a\u76ee\u6807\uff0c\u8bbe\u8ba1\u591a\u76ee\u6807\u4f18\u5316\u7b97\u6cd5\uff0c\u627e\u5230\u6700\u4f18\u7684\u8c03\u5ea6\u7b56\u7565\u3002<\/p>\n\n\n\n

                \u81ea\u52a8\u5316\u8c03\u5ea6\uff1a \u5229\u7528\u673a\u5668\u5b66\u4e60\u6216\u5f3a\u5316\u5b66\u4e60\u6280\u672f\uff0c\u5b9e\u73b0\u81ea\u52a8\u5316\u8c03\u5ea6\uff0c\u51cf\u5c11\u4eba\u5de5\u5e72\u9884\uff0c\u63d0\u9ad8\u8c03\u5ea6\u6548\u7387\uff0c\u5e76\u66f4\u597d\u5730\u9002\u5e94\u52a8\u6001\u53d8\u5316\u7684 DLT \u96c6\u7fa4\u73af\u5883\u3002<\/p>\n\n\n\n

                \u4e2a\u4eba\u603b\u7ed3\uff1a<\/strong><\/strong><\/p>\n\n\n\n

                Crux\u5f15\u5165GPU\u5f3a\u5ea6<\/span>\u6982\u5ff5\uff08\u5373\u7279\u5f02\u6027\u6307\u6807\uff09\u8861\u91cf\u4f5c\u4e1a\u5bf9GPU\u5229\u7528\u7387\u7684\u5f71\u54cd\u3002\u5b83\u4eec\u7684\u8c03\u5ea6\u51b3\u7b56\u5e76\u975e\u662f\u4f20\u7edf\u7684\u57fa\u4e8e\u5355\u4e2a\u4f5c\u4e1a\u7684\u6d41\u91cf\u6a21\u5f0f\uff0c\u8003\u8651\u4e86\u4e0d\u540c\u4f5c\u4e1a\u95f4\u7684\u7ade\u4e89\u3002\u4f7f\u7528GPU\u5f3a\u5ea6\uff0cCrux\u4e3a\u4e0d\u540c\u7684\u4f5c\u4e1a\u9009\u62e9\u8def\u5f84\u5e76\u5206\u914d\u4f18\u5148\u7ea7\uff0c\u4ee5\u51cf\u8f7b\u901a\u4fe1\u4e89\u7528\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

                Crux:GPU\u9ad8\u6548\u7684\u6df1\u5ea6\u5b66\u4e60\u8bad\u7ec3\u901a\u4fe1\u8c03\u5ea6 \u4f5c\u8005\uff1a\u963f\u91cc\u4e91\u56e2\u961f Author\uff1aJiamin Cao, Yu Gu […]<\/p>\n","protected":false},"author":3,"featured_media":547,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[39],"tags":[],"class_list":["post-545","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-39"],"_links":{"self":[{"href":"https:\/\/www.ienlab.com\/index.php?rest_route=\/wp\/v2\/posts\/545","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.ienlab.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.ienlab.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.ienlab.com\/index.php?rest_route=\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.ienlab.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=545"}],"version-history":[{"count":3,"href":"https:\/\/www.ienlab.com\/index.php?rest_route=\/wp\/v2\/posts\/545\/revisions"}],"predecessor-version":[{"id":550,"href":"https:\/\/www.ienlab.com\/index.php?rest_route=\/wp\/v2\/posts\/545\/revisions\/550"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.ienlab.com\/index.php?rest_route=\/wp\/v2\/media\/547"}],"wp:attachment":[{"href":"https:\/\/www.ienlab.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=545"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.ienlab.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=545"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.ienlab.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=545"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}