{"id":2189,"date":"2025-01-13T19:27:52","date_gmt":"2025-01-13T11:27:52","guid":{"rendered":"https:\/\/www.15zhi.net\/blog\/?p=2189"},"modified":"2025-01-13T19:27:52","modified_gmt":"2025-01-13T11:27:52","slug":"202501%e8%ae%ba%e6%96%87%e7%a0%94%e8%af%bb-distributed-data-processing-and-task-scheduling-based-on-gpu-parallel-computing","status":"publish","type":"post","link":"https:\/\/www.15zhi.net\/blog\/202501%e8%ae%ba%e6%96%87%e7%a0%94%e8%af%bb-distributed-data-processing-and-task-scheduling-based-on-gpu-parallel-computing\/","title":{"rendered":"202501\u8bba\u6587\u7814\u8bfb-Distributed data processing and task scheduling based on GPU parallel computing"},"content":{"rendered":"\n

\u4f5c\u8005\uff1aJun Li<\/p>\n\n\n\n

\u53d1\u8868\u671f\u520a\uff1aNeural Computing and Applications<\/p>\n\n\n\n

\u53d1\u8868\u65e5\u671f\uff1a2024\u5e749\u6708<\/p>\n\n\n\n

\u4e00\u3001\u80cc\u666f<\/h2>\n\n\n\n

1. \u6570\u636e\u91cf\u7684\u5feb\u901f\u589e\u957f\uff1a<\/strong>\u968f\u7740\u6570\u636e\u89c4\u6a21\u548c\u590d\u6742\u6027\u6301\u7eed\u589e\u52a0\uff0c\u5206\u5e03\u5f0f\u7cfb\u7edf\u6210\u4e3a\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u7684\u91cd\u8981\u65b9\u5f0f\uff0c\u4f46\u5176\u8ba1\u7b97\u6548\u7387\u4f9d\u8d56\u4e8e\u4efb\u52a1\u5206\u89e3\u548c\u8d44\u6e90\u8c03\u5ea6\u7684\u6709\u6548\u6027\u3002<\/p>\n\n\n\n

2. GPU\u7684\u9ad8\u6548\u8ba1\u7b97\u80fd\u529b\uff1a<\/strong>GPU\uff08\u56fe\u5f62\u5904\u7406\u5355\u5143\uff09\u4ee5\u5176\u9ad8\u5e76\u884c\u6027\u548c\u8ba1\u7b97\u5bc6\u96c6\u4efb\u52a1\u7684\u5904\u7406\u80fd\u529b\uff0c\u5728\u5206\u5e03\u5f0f\u8ba1\u7b97\u4e2d\u5177\u5907\u663e\u8457\u4f18\u52bf\u3002\u4f20\u7edfCPU\u5728\u5904\u7406\u590d\u6742\u8ba1\u7b97\u4efb\u52a1\u65f6\u6548\u7387\u4f4e\uff0c\u800cGPU\u901a\u8fc7\u5927\u91cf\u5e76\u884c\u8ba1\u7b97\u5355\u5143\u80fd\u591f\u663e\u8457\u52a0\u901f\u4efb\u52a1\u6267\u884c\u3002<\/p>\n\n\n\n

3. \u4efb\u52a1\u8c03\u5ea6\u7684\u6311\u6218\uff1a<\/strong>\u5206\u5e03\u5f0f\u8ba1\u7b97\u4e2d\u7684\u4efb\u52a1\u8c03\u5ea6\u662f\u4e00\u4e2aNP\u96be\u95ee\u9898\uff1a\u5982\u4f55\u9ad8\u6548\u5206\u914d\u4efb\u52a1\u4ee5\u6700\u5c0f\u5316\u6267\u884c\u65f6\u95f4\uff0c\u5982\u4f55\u5e73\u8861\u8ba1\u7b97\u8d44\u6e90\u7684\u5229\u7528\u7387\u4ee5\u53ca\u5982\u4f55\u9002\u5e94\u52a8\u6001\u73af\u5883\u4e2d\u7684\u4efb\u52a1\u9700\u6c42\u53d8\u5316\u3002\u4f20\u7edf\u8c03\u5ea6\u7b97\u6cd5\u5728\u8d1f\u8f7d\u5747\u8861\u548c\u80fd\u8017\u4f18\u5316\u65b9\u9762\u8868\u73b0\u6709\u9650\u3002<\/p>\n\n\n\n

\u4e8c\u3001\u521b\u65b0\u70b9<\/h2>\n\n\n\n

1.\u57fa\u4e8eGPU\u7684\u5206\u5e03\u5f0f\u4efb\u52a1\u8c03\u5ea6\u6a21\u578b\uff1a<\/strong>\u5728\u4efb\u52a1\u8c03\u5ea6\u4e2d\u5f15\u5165\u4e86\u52a8\u6001\u8c03\u6574\u7b56\u7565\uff0c\u9002\u5e94\u4e0d\u540c\u7684\u8d44\u6e90\u8d1f\u8f7d\u548c\u8ba1\u7b97\u9700\u6c42\u3002\u63d0\u51fa\u4e86\u4e00\u4e2a\u52a8\u6001\u4efb\u52a1\u8c03\u5ea6\u6a21\u578b\uff08DPP-GPU\uff09\uff0c\u80fd\u591f\u5145\u5206\u5229\u7528GPU\u7684\u9ad8\u5e76\u884c\u8ba1\u7b97\u80fd\u529b\uff0c\u5e76\u7ed3\u5408CPU\u8d44\u6e90\u8fdb\u884c\u4efb\u52a1\u5206\u914d\u3002<\/p>\n\n\n\n

2.\u52a8\u6001\u73af\u5883\u4e2d\u7684\u4efb\u52a1\u4f18\u5316\uff1a<\/strong>\u7ed3\u5408\u52a8\u6001\u7f51\u7edc\u73af\u5883\u548c\u8d44\u6e90\u72b6\u6001\uff0c\u8bbe\u8ba1\u4e86\u4e00\u79cd\u81ea\u9002\u5e94\u8c03\u5ea6\u7b97\u6cd5\uff1a\u80fd\u591f\u5e94\u5bf9\u5e26\u5bbd\u6ce2\u52a8\u548c\u8282\u70b9\u8d1f\u8f7d\u53d8\u5316\uff0c\u6839\u636e\u4efb\u52a1\u4f18\u5148\u7ea7\u3001\u8ba1\u7b97\u9700\u6c42\u548c\u8282\u70b9\u72b6\u6001\u52a8\u6001\u5206\u914d\u4efb\u52a1\u3002<\/p>\n\n\n\n

3.GPU\u5e76\u884c\u8ba1\u7b97\u4e0e\u901a\u4fe1\u4f18\u5316\uff1a<\/strong>\u5229\u7528GPU\u52a0\u901f\u8ba1\u7b97\u5bc6\u96c6\u578b\u4efb\u52a1\uff0c\u901a\u8fc7\u4efb\u52a1\u5206\u5757\u51cf\u5c11\u8ba1\u7b97\u65f6\u95f4\u3002\u5728GPU\u4e0eCPU\u4e4b\u95f4\u4f18\u5316\u4e86\u6570\u636e\u4f20\u8f93\u8def\u5f84\uff0c\u964d\u4f4e\u901a\u4fe1\u5f00\u9500\u3002<\/p>\n\n\n\n

\u4e09\u3001\u57fa\u4e8eGPU\u5e76\u884c\u8ba1\u7b97\u7684\u6570\u636e\u5206\u5e03\u5f0f\u5e76\u884c\u5904\u7406\u4efb\u52a1\u8c03\u5ea6<\/h2>\n\n\n\n
\"\"<\/figure>\n\n\n\n

\u4e0a\u56fe\u663e\u793a\u4e86\u4f7f\u7528\u5e76\u884cGPU\u8ba1\u7b97\u548c\u901a\u4fe1\u7684\u5206\u5e03\u5f0f\u6570\u636e\u5e76\u884c\u5904\u7406\u3002\u4f5c\u4e3a\u8f93\u5165\uff0c\u8282\u70b9\u63a5\u6536\u8fde\u7eed\u6d41\u7684\u6781\u7aef\u6570\u636e\u5757\uff0c\u5e76\u901a\u8fc7\u8282\u70b9\u534f\u8c03\u5668\u4ece\u8282\u70b9A\u5e76\u884c\u94fe\u63a5\u5230\u8282\u70b9B\u3002<\/p>\n\n\n\n

    \n
  1. \u8282\u70b9\u534f\u4f5c\u4e0e\u5171\u4eab\u5185\u5b58\u7684\u8bbe\u8ba1\u63d0\u9ad8\u4e86\u5206\u5e03\u5f0f\u7cfb\u7edf\u4e2d\u6570\u636e\u4e00\u81f4\u6027\u548c\u5904\u7406\u6548\u7387\u3002<\/li>\n\n\n\n
  2. \u901a\u8fc7\u5e76\u884c\u8ba1\u7b97\u4e0e\u4efb\u52a1\u8c03\u5ea6\uff0c\u6570\u636e\u5757\u88ab\u9ad8\u6548\u5904\u7406\uff0c\u9002\u7528\u4e8e\u52a8\u6001\u73af\u5883\u4e2d\u7684\u5927\u89c4\u6a21\u6570\u636e\u6d41\u3002<\/li>\n<\/ol>\n\n\n\n
    \"\"<\/figure>\n\n\n\n

    \u4e0a\u56fe\u6e05\u6670\u5730\u5c55\u793a\u4e86\u4efb\u52a1\u8c03\u5ea6\u6280\u672f\u7684\u4e24\u5927\u65b9\u5411\u53ca\u5176\u7ec6\u5206\u65b9\u6cd5\uff1a<\/p>\n\n\n\n

      \n
    1. \u672c\u5730\u8c03\u5ea6<\/strong>\uff1a\u5173\u6ce8\u5355\u8282\u70b9\u4f18\u5316\uff0c\u5f3a\u8c03\u8d44\u6e90\u7684\u516c\u5e73\u5206\u914d\uff08\u6bd4\u4f8b\u8c03\u5ea6\uff09\u548c\u9884\u6d4b\u6027\u5206\u914d\uff08\u9884\u6d4b\u8c03\u5ea6\uff09\u3002<\/li>\n\n\n\n
    2. \u534f\u540c\u8c03\u5ea6<\/strong>\uff1a\u5173\u6ce8\u591a\u8282\u70b9\u534f\u4f5c\uff0c\u5f3a\u8c03\u4efb\u52a1\u7684\u540c\u6b65\uff08Gang\u8c03\u5ea6\uff09\u3001\u9690\u5f0f\u4f18\u5316\uff08\u9690\u5f0f\u534f\u540c\u8c03\u5ea6\uff09\u4ee5\u53ca\u52a8\u6001\u81ea\u9002\u5e94\uff08\u52a8\u6001\u534f\u540c\u8c03\u5ea6\uff09\u3002<\/li>\n<\/ol>\n\n\n\n
      \"\"<\/figure>\n\n\n\n

      \u8fd9\u5f20\u56fe\u901a\u8fc7\u5206\u5c42\u7684\u65b9\u5f0f\u5c55\u793a\u4e86\u7cfb\u7edf\u7684\u67b6\u6784\u8bbe\u8ba1\uff0c\u4ece\u5e95\u5c42\u7684\u64cd\u4f5c\u7cfb\u7edf\uff0c\u5230\u4e2d\u95f4\u7684\u8d44\u6e90\u7ba1\u7406\u6a21\u5757\uff0c\u518d\u5230\u4e0a\u5c42\u7684\u6838\u5fc3\u529f\u80fd\u6a21\u5757\uff0c\u6e05\u6670\u5730\u63cf\u8ff0\u4e86\u5206\u5e03\u5f0f\u6570\u636e\u5904\u7406\u7cfb\u7edf\u7684\u5de5\u4f5c\u6d41\u7a0b\u3002<\/p>\n\n\n\n

      \u56db\u3001\u5b9e\u9a8c\u53ca\u7ed3\u679c<\/h2>\n\n\n\n

      \u5b9e\u9a8c\u4e2d\u4f7f\u7528\u4e86 GPU Benchmarks v7.csv<\/strong> \u6570\u636e\u96c6\uff0c\u8be5\u6570\u636e\u96c6\u7528\u4e8e\u6d4b\u8bd5 GPU \u7684\u6027\u80fd\u57fa\u51c6\uff0c\u5305\u542b\u4ee5\u4e0b\u7279\u6027\uff1a<\/p>\n\n\n\n

      \u6570\u636e\u6765\u6e90<\/strong>\uff1aGPU \u57fa\u51c6\u6d4b\u8bd5\u6570\u636e\u5b9a\u671f\u66f4\u65b0\u4e8e PassMark.com<\/strong>\uff0c\u8986\u76d6\u4e86\u591a\u79cd GPU \u7684\u6027\u80fd\u8bc4\u4f30\u3002<\/p>\n\n\n\n

      \u57fa\u51c6\u6d4b\u8bd5\u8303\u56f4<\/strong>\uff1a<\/p>\n\n\n\n

        \n
      • \u652f\u6301 OpenCL \u548c Microsoft Direct Compute\uff08DirectX API\uff09\uff0c\u7528\u4e8e\u5728 GPU \u4e0a\u6267\u884c\u901a\u7528\u8ba1\u7b97\u4efb\u52a1\u3002<\/li>\n\n\n\n
      • \u6d4b\u8bd5 GPU \u7684\u5e76\u884c\u8ba1\u7b97\u80fd\u529b\uff0c\u9002\u5408\u8bc4\u4f30\u8ba1\u7b97\u5bc6\u96c6\u578b\u4efb\u52a1\u7684\u6027\u80fd\u3002<\/li>\n\n\n\n
      • \u6d89\u53ca DirectX 10 \u548c DirectX 11 \u7684 GPU \u7248\u672c\u3002<\/li>\n<\/ul>\n\n\n\n

        \u6027\u80fd\u6307\u6807<\/strong>\uff1a<\/p>\n\n\n\n

          \n
        • \u6d4b\u8bd5 GPU \u7684\u4efb\u52a1\u6267\u884c\u901f\u5ea6\u548c\u6570\u636e\u5904\u7406\u80fd\u529b\u3002<\/li>\n\n\n\n
        • \u63d0\u4f9b GPU \u7684 OpenCL \u548c Direct Compute \u8ba1\u7b97\u6027\u80fd\u4f5c\u4e3a\u6027\u80fd\u6bd4\u8f83\u7684\u6307\u6807\u3002<\/li>\n<\/ul>\n\n\n\n

          \u5b9e\u9a8c\u7ed3\u679c\uff1a<\/strong><\/p>\n\n\n\n

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

          \u4e0d\u540c\u4efb\u52a1\u8c03\u5ea6\u7b97\u6cd5\u7684\u4efb\u52a1\u5b9e\u73b0\u7387\uff08Implementation Rate, %\uff09\u5728\u4e0d\u540c\u4efb\u52a1\u6570\u91cf\uff08Number of Tasks\uff09\u60c5\u51b5\u4e0b\u7684\u8868\u73b0\u3002<\/p>\n\n\n\n

          \u8868 1<\/strong> \u8bf4\u660e DPP-GPU \u6a21\u578b\u5177\u6709\u6781\u9ad8\u7684\u4efb\u52a1\u5b9e\u73b0\u7387\uff0c\u7279\u522b\u662f\u5728\u9ad8\u4efb\u52a1\u8d1f\u8f7d\u573a\u666f\u4e2d\u8868\u73b0\u4f18\u8d8a\u3002<\/p>\n\n\n\n

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

          \u4e0d\u540c\u4efb\u52a1\u6570\u91cf\u4e0b\uff0c\u5404\u79cd\u8c03\u5ea6\u7b97\u6cd5\u7684\u4efb\u52a1\u6267\u884c\u65f6\u95f4\uff08Execution Time, \u79d2\uff09\u3002<\/p>\n\n\n\n

          \u8868 2<\/strong> \u8bc1\u660e DPP-GPU \u6a21\u578b\u663e\u8457\u7f29\u77ed\u4e86\u4efb\u52a1\u6267\u884c\u65f6\u95f4\uff0c\u5145\u5206\u5229\u7528\u4e86 GPU \u7684\u5f3a\u5927\u8ba1\u7b97\u80fd\u529b\u3002<\/p>\n\n\n\n

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

          \u4e0d\u540c\u4efb\u52a1\u8c03\u5ea6\u7b97\u6cd5\u5728\u4efb\u52a1\u6570\u91cf\u4ece1\u52308\u7684\u60c5\u51b5\u4e0b\u7684\u8bc4\u4f30\u6bd4\u4f8b\uff08Evaluation Ratio\uff0c%\uff09\u3002<\/p>\n\n\n\n

          \u5728\u4efb\u52a1\u5b9e\u73b0\u6bd4\u4f8b\u4e0a\uff0cDPP-GPU\u6a21\u578b\u663e\u8457\u4f18\u4e8e\u5176\u4ed6\u65b9\u6cd5\uff0c\u7279\u522b\u662f\u5728\u9ad8\u4efb\u52a1\u8d1f\u8f7d\u573a\u666f\u4e0b\uff0c\u8868\u73b0\u51fa\u5353\u8d8a\u7684\u7a33\u5b9a\u6027\u548c\u9ad8\u6548\u6027\u3002DPP-GPU\u6a21\u578b\u5728\u5206\u5e03\u5f0f\u8ba1\u7b97\u73af\u5883\u4e2d\u7684\u6027\u80fd\u8868\u73b0\u9a8c\u8bc1\u4e86\u5176\u9002\u7528\u4e8e\u591a\u4efb\u52a1\u5e76\u53d1\u548c\u9ad8\u8d1f\u8f7d\u573a\u666f\uff0c\u4e3a\u4efb\u52a1\u8c03\u5ea6\u4f18\u5316\u63d0\u4f9b\u4e86\u65b0\u601d\u8def\u3002<\/p>\n\n\n\n

          \u4e94\u3001\u603b\u7ed3<\/h2>\n\n\n\n

          \u8fd9\u7bc7\u8bba\u6587\u63d0\u51fa\u4e86\u4e00\u79cd\u57fa\u4e8eGPU\u7684\u5206\u5e03\u5f0f\u4efb\u52a1\u8c03\u5ea6\u6a21\u578b\uff08DPP-GPU\uff09\uff0c\u901a\u8fc7\u52a8\u6001\u4efb\u52a1\u5206\u914d\u548c\u8d44\u6e90\u4f18\u5316\u7b56\u7565\uff0c\u5145\u5206\u5229\u7528GPU\u7684\u9ad8\u5e76\u884c\u8ba1\u7b97\u80fd\u529b\uff0c\u5b9e\u73b0\u4e86\u5728\u5206\u5e03\u5f0f\u7cfb\u7edf\u4e2d\u9ad8\u6548\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u7684\u76ee\u6807\u3002<\/p>\n\n\n\n

          \u5b9e\u9a8c\u7ed3\u679c\u8868\u660e\uff0cDPP-GPU\u5728\u4efb\u52a1\u5b8c\u6210\u7387\u3001\u6267\u884c\u65f6\u95f4\u548c\u7a33\u5b9a\u6027\u65b9\u9762\u663e\u8457\u4f18\u4e8e\u4f20\u7edf\u8c03\u5ea6\u7b97\u6cd5\uff0c\u5c24\u5176\u5728\u9ad8\u4efb\u52a1\u8d1f\u8f7d\u548c\u52a8\u6001\u73af\u5883\u4e0b\u5c55\u73b0\u51fa\u5353\u8d8a\u7684\u6027\u80fd\uff0c\u4e3a\u5206\u5e03\u5f0f\u8ba1\u7b97\u4efb\u52a1\u8c03\u5ea6\u63d0\u4f9b\u4e86\u9ad8\u6548\u4e14\u53ef\u6269\u5c55\u7684\u89e3\u51b3\u65b9\u6848\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

          \u4f5c\u8005\uff1aJun Li \u53d1\u8868\u671f\u520a\uff1aNeural Computing and Applications \u53d1\u8868\u65e5\u671f\uff1a2 […]<\/p>\n","protected":false},"author":11,"featured_media":0,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-2189","post","type-post","status-publish","format-standard","hentry","category-events"],"_links":{"self":[{"href":"https:\/\/www.15zhi.net\/blog\/wp-json\/wp\/v2\/posts\/2189","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.15zhi.net\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.15zhi.net\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.15zhi.net\/blog\/wp-json\/wp\/v2\/users\/11"}],"replies":[{"embeddable":true,"href":"https:\/\/www.15zhi.net\/blog\/wp-json\/wp\/v2\/comments?post=2189"}],"version-history":[{"count":6,"href":"https:\/\/www.15zhi.net\/blog\/wp-json\/wp\/v2\/posts\/2189\/revisions"}],"predecessor-version":[{"id":2202,"href":"https:\/\/www.15zhi.net\/blog\/wp-json\/wp\/v2\/posts\/2189\/revisions\/2202"}],"wp:attachment":[{"href":"https:\/\/www.15zhi.net\/blog\/wp-json\/wp\/v2\/media?parent=2189"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.15zhi.net\/blog\/wp-json\/wp\/v2\/categories?post=2189"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.15zhi.net\/blog\/wp-json\/wp\/v2\/tags?post=2189"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}