{"id":13425,"date":"2024-07-10T15:58:40","date_gmt":"2024-07-10T07:58:40","guid":{"rendered":"https:\/\/www.simcentric.com\/uncategorized-sc\/what-is-a-gpu-dedicated-server-and-what-are-its-uses\/"},"modified":"2024-07-10T16:01:49","modified_gmt":"2024-07-10T08:01:49","slug":"what-is-a-gpu-dedicated-server-and-what-are-its-uses","status":"publish","type":"post","link":"https:\/\/www.simcentric.com\/sc\/hong-kong-dedicated-server-sc\/what-is-a-gpu-dedicated-server-and-what-are-its-uses\/","title":{"rendered":"\u4ec0\u4e48\u662fGPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668? \u5b83\u6709\u54ea\u4e9b\u7528\u9014?"},"content":{"rendered":"\n

\n \u5728\u4e0d\u65ad\u53d1\u5c55\u7684\u6280\u672f\u9886\u57df\uff0cGPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668<\/a>\u4f5c\u4e3a\u4e00\u79cd\u5f3a\u5927\u7684\u5de5\u5177\uff0c\u6b63\u5728\u5404\u4e2a\u8ba1\u7b97\u9886\u57df\u5f15\u53d1\u53d8\u9769\u3002\u90a3\u4e48\uff0c\u4ec0\u4e48\u662fGPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668\uff1f\u4e3a\u4ec0\u4e48\u5b83\u5728\u4ece\u4eba\u5de5\u667a\u80fd\u5230\u6e38\u620f\u7684\u5404\u4e2a\u884c\u4e1a\u4e2d\u53d8\u5f97\u81f3\u5173\u91cd\u8981\uff1f\u672c\u6587\u6df1\u5165\u63a2\u8ba8\u4e86GPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668\u7684\u7ec6\u8282\uff0c\u7a81\u51fa\u5176\u4f18\u52bf\u3001\u5e94\u7528\u4ee5\u53ca\u4e3a\u4ec0\u4e48\u9009\u62e9\u9999\u6e2f\u670d\u52a1\u5668<\/a>\u53ef\u80fd\u662f\u6ee1\u8db3\u4e1a\u52a1\u9700\u6c42\u7684\u6700\u4f73\u51b3\u7b56\u3002\n <\/p>\n

\u4ec0\u4e48\u662fGPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668\uff1f<\/strong><\/h2>\n

\n GPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668\u662f\u4e00\u79cd\u5305\u542b\u4e00\u4e2a\u6216\u591a\u4e2a\u56fe\u5f62\u5904\u7406\u5355\u5143\uff08GPU\uff09\u4ee5\u53ca\u4f20\u7edf\u4e2d\u592e\u5904\u7406\u5355\u5143\uff08CPU\uff09\u7684\u670d\u52a1\u5668\u3002\u4e0e\u4e3b\u8981\u7528\u4e8e\u901a\u7528\u5904\u7406\u7684CPU\u4e0d\u540c\uff0cGPU\u4e13\u95e8\u7528\u4e8e\u5904\u7406\u590d\u6742\u7684\u6570\u5b66\u8ba1\u7b97\uff0c\u7279\u522b\u662f\u90a3\u4e9b\u53ef\u4ee5\u5e76\u884c\u5316\u7684\u8ba1\u7b97\u3002\u8fd9\u4f7f\u5f97\u5b83\u4eec\u5728\u9700\u8981\u5927\u91cf\u8ba1\u7b97\u80fd\u529b\u7684\u4efb\u52a1\u4e2d\u975e\u5e38\u9ad8\u6548\u3002\n <\/p>\n

\n GPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668\u4e0e\u4f20\u7edf\u670d\u52a1\u5668\u7684\u4e3b\u8981\u533a\u522b\u5728\u4e8e\u5176\u67b6\u6784\u3002\u4f20\u7edf\u670d\u52a1\u5668\u5b8c\u5168\u4f9d\u8d56CPU\u8fdb\u884c\u5904\u7406\uff0c\u800cGPU\u670d\u52a1\u5668\u5219\u5229\u7528GPU\u5904\u7406\u4f17\u591a\u5e76\u53d1\u64cd\u4f5c\u7684\u80fd\u529b\uff0c\u4ece\u800c\u663e\u8457\u63d0\u5347\u7279\u5b9a\u4efb\u52a1\u7684\u6027\u80fd\u3002\u8fd9\u79cd\u5e76\u884c\u5904\u7406\u80fd\u529b\u5bf9\u4e8e\u6570\u636e\u5bc6\u96c6\u578b\u5e94\u7528\u7279\u522b\u6709\u5229\uff0c\u4e0b\u9762\u6211\u4eec\u5c06\u8be6\u7ec6\u63a2\u8ba8\u8fd9\u4e9b\u5e94\u7528\u3002\n <\/p>\n

GPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668\u7684\u4f18\u52bf<\/strong><\/h2>\n

1. \u6027\u80fd\u4f18\u52bf<\/h2>\n

\n \u7531\u4e8e\u9ad8\u5e76\u884c\u5904\u7406\u80fd\u529b\uff0cGPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668\u5728\u6027\u80fd\u4e0a\u8868\u73b0\u51fa\u8272\u3002\u5b83\u4eec\u80fd\u591f\u540c\u65f6\u5904\u7406\u6570\u5343\u4e2a\u7ebf\u7a0b\uff0c\u9002\u7528\u4e8e\u9700\u8981\u5927\u91cf\u8ba1\u7b97\u7684\u4efb\u52a1\uff0c\u5982\u8bad\u7ec3\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u6216\u6e32\u67d3\u9ad8\u5206\u8fa8\u7387\u56fe\u5f62\u3002\n <\/p>\n

# \u4f7f\u7528CUDA\u5728Python\u4e2d\u8fdb\u884c\u5e76\u884c\u5904\u7406\u4efb\u52a1\u7684\u793a\u4f8b\r\nimport numpy as np\r\nfrom numba import cuda\r\n\r\n@cuda.jit\r\ndef add_kernel(a, b, c):\r\n    idx = cuda.grid(1)\r\n    if idx < a.size:\r\n        c[idx] = a[idx] + b[idx]\r\n\r\nN = 1000000\r\na = np.ones(N, dtype=np.float32)\r\nb = np.ones(N, dtype=np.float32)\r\nc = np.zeros(N, dtype=np.float32)\r\n\r\nthreads_per_block = 256\r\nblocks_per_grid = (a.size + (threads_per_block - 1)) \/\/ threads_per_block\r\n\r\nadd_kernel[blocks_per_grid, threads_per_block](a, b, c)\r\n\r\nprint(c[:10])  # \u8f93\u51fa\u5e94\u4e3a\u4e00\u4e2a\u75312\u7ec4\u6210\u7684\u6570\u7ec4\r\n    <\/code><\/pre>\n
2. \u7075\u6d3b\u6027<\/h2>\n
\n        GPU\u670d\u52a1\u5668\u975e\u5e38\u7075\u6d3b\uff0c\u53ef\u4ee5\u6839\u636e\u5404\u79cd\u5e94\u7528\u8fdb\u884c\u5b9a\u5236\u3002\u65e0\u8bba\u662f\u8fd0\u884c\u590d\u6742\u7684\u6a21\u62df\u3001\u5904\u7406\u5927\u6570\u636e\u96c6\uff0c\u8fd8\u662f\u5f00\u53d1\u590d\u6742\u7684\u56fe\u5f62\uff0cGPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668\u90fd\u80fd\u63d0\u4f9b\u9002\u5e94\u8fd9\u4e9b\u591a\u6837\u5316\u9700\u6c42\u7684\u7075\u6d3b\u6027\u3002\n    <\/p>\n
3. \u9002\u5e94\u6027<\/h2>\n
\n        \u968f\u7740\u5bf9\u5927\u6570\u636e\u5206\u6790\u548cAI\u9a71\u52a8\u5e94\u7528\u9700\u6c42\u7684\u589e\u52a0\uff0cGPU\u670d\u52a1\u5668\u7684\u9002\u5e94\u6027\u4f7f\u5176\u53d8\u5f97\u4e0d\u53ef\u6216\u7f3a\u3002\u5b83\u4eec\u80fd\u591f\u5904\u7406\u8bad\u7ec3\u673a\u5668\u5b66\u4e60\u6a21\u578b\u548c\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u6240\u9700\u7684\u5de8\u5927\u8ba1\u7b97\u8d1f\u8377\uff0c\u8fd9\u5728\u5f53\u4eca\u6570\u636e\u9a71\u52a8\u7684\u884c\u4e1a\u4e2d\u975e\u5e38\u5e38\u89c1\u3002\n    <\/p>\n
GPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668\u7684\u4e3b\u8981\u5e94\u7528<\/strong><\/h2>\n
1. \u4eba\u5de5\u667a\u80fd\u548c\u673a\u5668\u5b66\u4e60<\/h2>\n
\n        GPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668\u6700\u663e\u8457\u7684\u5e94\u7528\u4e4b\u4e00\u662f\u5728\u4eba\u5de5\u667a\u80fd\uff08AI\uff09\u548c\u673a\u5668\u5b66\u4e60\u9886\u57df\u3002GPU\u7684\u5e76\u884c\u5904\u7406\u80fd\u529b\u663e\u8457\u52a0\u901f\u4e86\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u7684\u8bad\u7ec3\uff0c\u4f7fAI\u7b97\u6cd5\u7684\u5f00\u53d1\u66f4\u52a0\u5feb\u901f\u548c\u9ad8\u6548\u3002\n    <\/p>\n
# \u4f7f\u7528TensorFlow\u8bad\u7ec3\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u7684\u793a\u4f8b\r\nimport tensorflow as tf\r\nfrom tensorflow.keras.models import Sequential\r\nfrom tensorflow.keras.layers import Dense\r\n\r\n# \u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u6a21\u578b\r\nmodel = Sequential([\r\n    Dense(64, activation='relu', input_shape=(784,)),\r\n    Dense(64, activation='relu'),\r\n    Dense(10, activation='softmax')\r\n])\r\n\r\n# \u7f16\u8bd1\u6a21\u578b\r\nmodel.compile(optimizer='adam',\r\n              loss='sparse_categorical_crossentropy',\r\n              metrics=['accuracy'])\r\n\r\n# \u5728GPU\u4e0a\u8bad\u7ec3\u6a21\u578b\r\ngpu_devices = tf.config.experimental.list_physical_devices('GPU')\r\nif gpu_devices:\r\n    tf.config.experimental.set_memory_growth(gpu_devices[0], True)\r\n\r\n(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()\r\nx_train = x_train.reshape(-1, 784).astype('float32') \/ 255\r\nx_test = x_test.reshape(-1, 784).astype('float32') \/ 255\r\n\r\nmodel.fit(x_train, y_train, epochs=5, batch_size=32, validation_data=(x_test, y_test))\r\n    <\/code><\/pre>\n
2. \u56fe\u50cf\u548c\u89c6\u9891\u5904\u7406<\/h2>\n
\n        \u5728\u591a\u5a92\u4f53\u9886\u57df\uff0cGPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668\u5728\u56fe\u50cf\u548c\u89c6\u9891\u5904\u7406\u4efb\u52a1\u4e2d\u975e\u5e38\u6709\u4ef7\u503c\u3002\u5b83\u4eec\u5e7f\u6cdb\u7528\u4e8e\u89c6\u9891\u7f16\u7801\u3001\u6e32\u67d3\u548c\u5b9e\u65f6\u56fe\u50cf\u5206\u6790\uff0c\u51ed\u501f\u5176\u5904\u7406\u5e76\u884c\u64cd\u4f5c\u7684\u80fd\u529b\u786e\u4fdd\u9ad8\u6548\u548c\u5feb\u901f\u3002\n    <\/p>\n
3. \u79d1\u5b66\u8ba1\u7b97<\/h2>\n
\n        \u79d1\u5b66\u8ba1\u7b97\u901a\u5e38\u6d89\u53ca\u9700\u8981\u5927\u91cf\u8ba1\u7b97\u8d44\u6e90\u7684\u6a21\u62df\u548c\u5efa\u6a21\u3002GPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668\u63d0\u4f9b\u4e86\u6267\u884c\u8fd9\u4e9b\u590d\u6742\u8ba1\u7b97\u6240\u9700\u7684\u5f3a\u5927\u80fd\u529b\uff0c\u4f7f\u5176\u6210\u4e3a\u7269\u7406\u3001\u5316\u5b66\u548c\u751f\u7269\u4fe1\u606f\u5b66\u7b49\u9886\u57df\u7684\u5fc5\u5907\u5de5\u5177\u3002\n    <\/p>\n
4. \u533a\u5757\u94fe\u548c\u52a0\u5bc6\u8d27\u5e01\u6316\u77ff<\/h2>\n
\n        \u533a\u5757\u94fe\u884c\u4e1a\u4e5f\u63a5\u53d7\u4e86GPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668\uff0c\u56e0\u4e3a\u5b83\u4eec\u5728\u5904\u7406\u52a0\u5bc6\u8ba1\u7b97\u65b9\u9762\u7684\u6548\u7387\u3002\u5bf9\u4e8e\u52a0\u5bc6\u8d27\u5e01\u6316\u77ff\uff0cGPU\u76f8\u5bf9\u4e8eCPU\u63d0\u4f9b\u4e86\u663e\u8457\u7684\u6027\u80fd\u4f18\u52bf\uff0c\u63d0\u9ad8\u4e86\u6316\u77ff\u64cd\u4f5c\u7684\u76c8\u5229\u6027\u3002\n    <\/p>\n
5. \u6e38\u620f\u5f00\u53d1\u548c\u56fe\u5f62\u6e32\u67d3<\/h2>\n
\n        \u5728\u6e38\u620f\u884c\u4e1a\uff0cGPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668\u5bf9\u4e8e\u6e38\u620f\u5f00\u53d1\u548c\u56fe\u5f62\u6e32\u67d3\u81f3\u5173\u91cd\u8981\u3002\u5b83\u4eec\u80fd\u591f\u521b\u5efa\u8be6\u7ec6\u7684\u9ad8\u8d28\u91cf\u56fe\u5f62\u5e76\u652f\u6301\u5b9e\u65f6\u6e32\u67d3\uff0c\u8fd9\u5bf9\u4e8e\u6c89\u6d78\u5f0f\u6e38\u620f\u4f53\u9a8c\u548c\u865a\u62df\u73b0\u5b9e\uff08VR\uff09\u5e94\u7528\u81f3\u5173\u91cd\u8981\u3002\n    <\/p>\n
\u4e3a\u4ec0\u4e48\u9009\u62e9\u9999\u6e2fGPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668\uff1f<\/strong><\/h2>\n
\n        \u5bf9\u4e8e\u5bfb\u6c42GPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668\u7684\u4f01\u4e1a\u548c\u5f00\u53d1\u8005\u6765\u8bf4\uff0c\u9999\u6e2f\u670d\u52a1\u5668\u63d0\u4f9b\u4e86\u591a\u79cd\u4f18\u52bf\u3002\u9999\u6e2f\u7684\u6218\u7565\u5730\u7406\u4f4d\u7f6e\u4e3a\u4e9a\u592a\u5730\u533a\u63d0\u4f9b\u4e86\u4f18\u79c0\u7684\u8fde\u63a5\u6027\u548c\u4f4e\u5ef6\u8fdf\uff0c\u4f7f\u5176\u6210\u4e3a\u56fd\u9645\u8fd0\u8425\u7684\u7406\u60f3\u67a2\u7ebd\u3002\n    <\/p>\n
\n        \u6b64\u5916\uff0c\u9999\u6e2f\u5065\u5168\u7684\u6570\u636e\u9690\u79c1\u6cd5\u5f8b\u548c\u9ad8\u901f\u4e92\u8054\u7f51\u57fa\u7840\u8bbe\u65bd\u786e\u4fdd\u4e86\u53ef\u9760\u548c\u5b89\u5168\u7684\u6570\u636e\u5904\u7406\uff0c\u8fd9\u5bf9\u5546\u4e1a\u548c\u7814\u7a76\u5e94\u7528\u90fd\u81f3\u5173\u91cd\u8981\u3002\u9009\u62e9\u9999\u6e2f\u670d\u52a1\u5668\u53ef\u4ee5\u63d0\u9ad8\u8fd0\u8425\u6548\u7387\uff0c\u5e76\u5728\u5168\u7403\u5e02\u573a\u4e2d\u63d0\u4f9b\u7ade\u4e89\u4f18\u52bf\u3002\n    <\/p>\n
\u5982\u4f55\u9009\u62e9\u5408\u9002\u7684GPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668<\/strong><\/h2>\n
1. \u786e\u5b9a\u60a8\u7684\u4e1a\u52a1\u9700\u6c42<\/h2>\n
\n        \u9009\u62e9GPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668\u7684\u7b2c\u4e00\u6b65\u662f\u5206\u6790\u60a8\u7684\u5177\u4f53\u4e1a\u52a1\u9700\u6c42\u3002\u8003\u8651\u60a8\u9700\u8981\u6267\u884c\u7684\u8ba1\u7b97\u4efb\u52a1\u3001\u5c06\u8981\u5904\u7406\u7684\u6570\u636e\u91cf\u4ee5\u53ca\u5bf9\u60a8\u7684\u64cd\u4f5c\u81f3\u5173\u91cd\u8981\u7684\u6027\u80fd\u6307\u6807\u3002\n    <\/p>\n
2. \u9009\u62e9\u5408\u9002\u7684GPU\u7c7b\u578b<\/h2>\n
\n        \u4e0d\u540c\u7684GPU\u63d0\u4f9b\u4e0d\u540c\u6c34\u5e73\u7684\u6027\u80fd\uff0c\u5e76\u9002\u7528\u4e8e\u4e0d\u540c\u7684\u4efb\u52a1\u3002\u4f8b\u5982\uff0cNVIDIA\u7684Tesla\u548cQuadro\u7cfb\u5217\u5206\u522b\u8bbe\u8ba1\u7528\u4e8e\u9ad8\u6027\u80fd\u8ba1\u7b97\u548c\u4e13\u4e1a\u56fe\u5f62\u3002\u8bc4\u4f30\u4e0d\u540cGPU\u578b\u53f7\u7684\u89c4\u683c\u548c\u529f\u80fd\u53ef\u4ee5\u5e2e\u52a9\u60a8\u505a\u51fa\u660e\u667a\u7684\u51b3\u5b9a\u3002\n    <\/p>\n
3. \u9009\u62e9\u53ef\u9760\u7684\u670d\u52a1\u63d0\u4f9b\u5546<\/h2>\n
\n        \u6700\u540e\uff0c\u9009\u62e9\u4e00\u4e2a\u4fe1\u8a89\u826f\u597d\u7684\u670d\u52a1\u63d0\u4f9b\u5546\u5bf9\u4e8e\u786e\u4fdd\u60a8\u7684GPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668\u7684\u53ef\u9760\u6027\u548c\u8d28\u91cf\u81f3\u5173\u91cd\u8981\u3002\u8003\u8651\u63d0\u4f9b\u5546\u7684\u5f80\u7ee9\u3001\u5ba2\u6237\u652f\u6301\u4ee5\u53ca\u63d0\u4f9b\u7684\u670d\u52a1\u8303\u56f4\uff0c\u4ee5\u786e\u4fdd\u60a8\u7684\u670d\u52a1\u5668\u6ee1\u8db3\u60a8\u7684\u9700\u6c42\u5e76\u63d0\u4f9b\u65e0\u7f1d\u7684\u7528\u6237\u4f53\u9a8c\u3002\n    <\/p>\n
\n       GPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668\u4ee3\u8868\u4e86\u8ba1\u7b97\u6280\u672f\u7684\u91cd\u5927\u8fdb\u6b65\uff0c\u4e3a\u5e7f\u6cdb\u7684\u5e94\u7528\u63d0\u4f9b\u4e86\u65e0\u4e0e\u4f26\u6bd4\u7684\u6027\u80fd\u548c\u7075\u6d3b\u6027\u3002\u65e0\u8bba\u60a8\u4ece\u4e8bAI\u5f00\u53d1\u3001\u89c6\u9891\u5904\u7406\u3001\u79d1\u5b66\u7814\u7a76\u3001\u533a\u5757\u94fe\u8fd8\u662f\u6e38\u620f\u5f00\u53d1\uff0cGPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668\u90fd\u53ef\u4ee5\u63d0\u4f9b\u5b9e\u73b0\u76ee\u6807\u6240\u9700\u7684\u5f3a\u5927\u80fd\u529b\u548c\u7075\u6d3b\u6027\u3002\u9009\u62e9\u9999\u6e2f\u670d\u52a1\u5668\uff0c\u60a8\u53ef\u4ee5\u5229\u7528\u8be5\u5730\u533a\u7684\u6218\u7565\u4f18\u52bf\u6765\u589e\u5f3a\u8fd0\u8425\u6548\u7387\uff0c\u5e76\u5728\u7ade\u4e89\u6fc0\u70c8\u7684\u5168\u7403\u5e02\u573a\u4e2d\u8131\u9896\u800c\u51fa\u3002\n    <\/p>\n","protected":false},"excerpt":{"rendered":"
\u5728\u4e0d\u65ad\u53d1\u5c55\u7684\u6280\u672f\u9886\u57df\uff0cGPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668\u4f5c\u4e3a\u4e00\u79cd\u5f3a\u5927\u7684\u5de5\u5177\uff0c\u6b63\u5728\u5404\u4e2a\u8ba1\u7b97\u9886\u57df\u5f15\u53d1\u53d8\u9769\u3002\u90a3\u4e48\uff0c\u4ec0\u4e48\u662fGPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668\uff1f\u4e3a\u4ec0\u4e48\u5b83\u5728\u4ece\u4eba\u5de5\u667a\u80fd\u5230\u6e38\u620f\u7684\u5404\u4e2a\u884c\u4e1a\u4e2d\u53d8\u5f97\u81f3\u5173\u91cd\u8981\uff1f\u672c\u6587\u6df1\u5165\u63a2\u8ba8\u4e86GPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668\u7684\u7ec6\u8282\uff0c\u7a81\u51fa\u5176\u4f18\u52bf\u3001\u5e94\u7528\u4ee5\u53ca\u4e3a\u4ec0\u4e48\u9009\u62e9\u9999\u6e2f\u670d\u52a1\u5668\u53ef\u80fd\u662f\u6ee1\u8db3\u4e1a\u52a1\u9700\u6c42\u7684\u6700\u4f73\u51b3\u7b56\u3002 \u4ec0\u4e48\u662fGPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668\uff1f GPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668\u662f\u4e00\u79cd\u5305\u542b\u4e00\u4e2a\u6216\u591a\u4e2a\u56fe\u5f62\u5904\u7406\u5355\u5143\uff08GPU\uff09\u4ee5\u53ca\u4f20\u7edf\u4e2d\u592e\u5904\u7406\u5355\u5143\uff08CPU\uff09\u7684\u670d\u52a1\u5668\u3002\u4e0e\u4e3b\u8981\u7528\u4e8e\u901a\u7528\u5904\u7406\u7684CPU\u4e0d\u540c\uff0cGPU\u4e13\u95e8\u7528\u4e8e\u5904\u7406\u590d\u6742\u7684\u6570\u5b66\u8ba1\u7b97\uff0c\u7279\u522b\u662f\u90a3\u4e9b\u53ef\u4ee5\u5e76\u884c\u5316\u7684\u8ba1\u7b97\u3002\u8fd9\u4f7f\u5f97\u5b83\u4eec\u5728\u9700\u8981\u5927\u91cf\u8ba1\u7b97\u80fd\u529b\u7684\u4efb\u52a1\u4e2d\u975e\u5e38\u9ad8\u6548\u3002 GPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668\u4e0e\u4f20\u7edf\u670d\u52a1\u5668\u7684\u4e3b\u8981\u533a\u522b\u5728\u4e8e\u5176\u67b6\u6784\u3002\u4f20\u7edf\u670d\u52a1\u5668\u5b8c\u5168\u4f9d\u8d56CPU\u8fdb\u884c\u5904\u7406\uff0c\u800cGPU\u670d\u52a1\u5668\u5219\u5229\u7528GPU\u5904\u7406\u4f17\u591a\u5e76\u53d1\u64cd\u4f5c\u7684\u80fd\u529b\uff0c\u4ece\u800c\u663e\u8457\u63d0\u5347\u7279\u5b9a\u4efb\u52a1\u7684\u6027\u80fd\u3002\u8fd9\u79cd\u5e76\u884c\u5904\u7406\u80fd\u529b\u5bf9\u4e8e\u6570\u636e\u5bc6\u96c6\u578b\u5e94\u7528\u7279\u522b\u6709\u5229\uff0c\u4e0b\u9762\u6211\u4eec\u5c06\u8be6\u7ec6\u63a2\u8ba8\u8fd9\u4e9b\u5e94\u7528\u3002 GPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668\u7684\u4f18\u52bf 1. \u6027\u80fd\u4f18\u52bf \u7531\u4e8e\u9ad8\u5e76\u884c\u5904\u7406\u80fd\u529b\uff0cGPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668\u5728\u6027\u80fd\u4e0a\u8868\u73b0\u51fa\u8272\u3002\u5b83\u4eec\u80fd\u591f\u540c\u65f6\u5904\u7406\u6570\u5343\u4e2a\u7ebf\u7a0b\uff0c\u9002\u7528\u4e8e\u9700\u8981\u5927\u91cf\u8ba1\u7b97\u7684\u4efb\u52a1\uff0c\u5982\u8bad\u7ec3\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u6216\u6e32\u67d3\u9ad8\u5206\u8fa8\u7387\u56fe\u5f62\u3002 # \u4f7f\u7528CUDA\u5728Python\u4e2d\u8fdb\u884c\u5e76\u884c\u5904\u7406\u4efb\u52a1\u7684\u793a\u4f8b import numpy as np from numba import cuda @cuda.jit def add_kernel(a, b, c): idx = cuda.grid(1) if idx < a.size: c[idx] = a[idx] + b[idx] N = 1000000 a = np.ones(N, dtype=np.float32) b = np.ones(N, dtype=np.float32) c = np.zeros(N, dtype=np.float32) threads_per_block = 256 blocks_per_grid = [...]\n\n
Read More…<\/a><\/p>\n","protected":false},"author":3,"featured_media":13381,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[62],"tags":[2323,2324,265],"class_list":["post-13425","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-hong-kong-dedicated-server-sc","tag-gpu-dedicated-server-sc","tag-the-use-of-gpu-dedicated-server-sc","tag-265"],"acf":[],"yoast_head":"\n\u4ec0\u4e48\u662fGPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668? \u5b83\u6709\u54ea\u4e9b\u7528\u9014?<\/title>\n<meta name=\"description\" content=\"\u6df1\u5165\u4e86\u89e3GPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668\u662f\u4ec0\u4e48\uff0c\u5b83\u4eec\u7684\u4f18\u52bf\u4ee5\u53ca\u5b83\u4eec\u5728AI\u3001\u89c6\u9891\u5904\u7406\u3001\u79d1\u5b66\u8ba1\u7b97\u3001\u533a\u5757\u94fe\u548c\u6e38\u620f\u4e2d\u7684\u5e94\u7528\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.simcentric.com\/sc\/wp-json\/wp\/v2\/posts\/13425\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"company\" \/>\n<meta property=\"og:title\" content=\"\u4ec0\u4e48\u662fGPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668? \u5b83\u6709\u54ea\u4e9b\u7528\u9014?\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.simcentric.com\/sc\/wp-json\/wp\/v2\/posts\/13425\" \/>\n<meta property=\"og:site_name\" content=\"\u65b0\u5929\u57df\u4e92\u8054\" \/>\n<meta property=\"article:published_time\" content=\"2024-07-10T07:58:40+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-07-10T08:01:49+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.simcentric.com\/wp-content\/uploads\/2024\/07\/1720596550790.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"429\" \/>\n\t<meta property=\"og:image:height\" content=\"429\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"\u4ec0\u4e48\u662fGPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668? \u5b83\u6709\u54ea\u4e9b\u7528\u9014?","description":"\u6df1\u5165\u4e86\u89e3GPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668\u662f\u4ec0\u4e48\uff0c\u5b83\u4eec\u7684\u4f18\u52bf\u4ee5\u53ca\u5b83\u4eec\u5728AI\u3001\u89c6\u9891\u5904\u7406\u3001\u79d1\u5b66\u8ba1\u7b97\u3001\u533a\u5757\u94fe\u548c\u6e38\u620f\u4e2d\u7684\u5e94\u7528\u3002","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.simcentric.com\/sc\/wp-json\/wp\/v2\/posts\/13425","og_locale":"zh_CN","og_type":"company","og_title":"\u4ec0\u4e48\u662fGPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668? \u5b83\u6709\u54ea\u4e9b\u7528\u9014?","og_url":"https:\/\/www.simcentric.com\/sc\/wp-json\/wp\/v2\/posts\/13425","og_site_name":"\u65b0\u5929\u57df\u4e92\u8054","article_published_time":"2024-07-10T07:58:40+00:00","article_modified_time":"2024-07-10T08:01:49+00:00","og_image":[{"width":429,"height":429,"url":"https:\/\/www.simcentric.com\/wp-content\/uploads\/2024\/07\/1720596550790.jpg","type":"image\/jpeg"}],"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/www.simcentric.com\/sc\/hong-kong-dedicated-server-sc\/what-is-a-gpu-dedicated-server-and-what-are-its-uses\/#article","isPartOf":{"@id":"https:\/\/www.simcentric.com\/sc\/hong-kong-dedicated-server-sc\/what-is-a-gpu-dedicated-server-and-what-are-its-uses\/"},"author":{"name":"Felix Cheung","@id":"https:\/\/simcentric.com\/tc\/#\/schema\/person\/2865b3454f789caf7083a203799d4a6d"},"headline":"\u4ec0\u4e48\u662fGPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668? \u5b83\u6709\u54ea\u4e9b\u7528\u9014?","datePublished":"2024-07-10T07:58:40+00:00","dateModified":"2024-07-10T08:01:49+00:00","mainEntityOfPage":{"@id":"https:\/\/www.simcentric.com\/sc\/hong-kong-dedicated-server-sc\/what-is-a-gpu-dedicated-server-and-what-are-its-uses\/"},"wordCount":46,"publisher":{"@id":"https:\/\/simcentric.com\/tc\/#organization"},"image":{"@id":"https:\/\/www.simcentric.com\/sc\/hong-kong-dedicated-server-sc\/what-is-a-gpu-dedicated-server-and-what-are-its-uses\/#primaryimage"},"thumbnailUrl":"https:\/\/www.simcentric.com\/wp-content\/uploads\/2024\/07\/1720596550790.jpg","keywords":["GPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668","GPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668\u7684\u7528\u9014","\u9999\u6e2f\u670d\u52a1\u5668"],"articleSection":["\u9999\u6e2f\u670d\u52a1\u5668"],"inLanguage":"zh-CHN"},{"@type":"WebPage","@id":"https:\/\/www.simcentric.com\/sc\/hong-kong-dedicated-server-sc\/what-is-a-gpu-dedicated-server-and-what-are-its-uses\/","url":"https:\/\/www.simcentric.com\/sc\/hong-kong-dedicated-server-sc\/what-is-a-gpu-dedicated-server-and-what-are-its-uses\/","name":"\u4ec0\u4e48\u662fGPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668? \u5b83\u6709\u54ea\u4e9b\u7528\u9014?","isPartOf":{"@id":"https:\/\/simcentric.com\/tc\/#website"},"primaryImageOfPage":{"@id":"https:\/\/www.simcentric.com\/sc\/hong-kong-dedicated-server-sc\/what-is-a-gpu-dedicated-server-and-what-are-its-uses\/#primaryimage"},"image":{"@id":"https:\/\/www.simcentric.com\/sc\/hong-kong-dedicated-server-sc\/what-is-a-gpu-dedicated-server-and-what-are-its-uses\/#primaryimage"},"thumbnailUrl":"https:\/\/www.simcentric.com\/wp-content\/uploads\/2024\/07\/1720596550790.jpg","datePublished":"2024-07-10T07:58:40+00:00","dateModified":"2024-07-10T08:01:49+00:00","description":"\u6df1\u5165\u4e86\u89e3GPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668\u662f\u4ec0\u4e48\uff0c\u5b83\u4eec\u7684\u4f18\u52bf\u4ee5\u53ca\u5b83\u4eec\u5728AI\u3001\u89c6\u9891\u5904\u7406\u3001\u79d1\u5b66\u8ba1\u7b97\u3001\u533a\u5757\u94fe\u548c\u6e38\u620f\u4e2d\u7684\u5e94\u7528\u3002","breadcrumb":{"@id":"https:\/\/www.simcentric.com\/sc\/hong-kong-dedicated-server-sc\/what-is-a-gpu-dedicated-server-and-what-are-its-uses\/#breadcrumb"},"inLanguage":"zh-CHN","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.simcentric.com\/sc\/hong-kong-dedicated-server-sc\/what-is-a-gpu-dedicated-server-and-what-are-its-uses\/"]}]},{"@type":"ImageObject","inLanguage":"zh-CHN","@id":"https:\/\/www.simcentric.com\/sc\/hong-kong-dedicated-server-sc\/what-is-a-gpu-dedicated-server-and-what-are-its-uses\/#primaryimage","url":"https:\/\/www.simcentric.com\/wp-content\/uploads\/2024\/07\/1720596550790.jpg","contentUrl":"https:\/\/www.simcentric.com\/wp-content\/uploads\/2024\/07\/1720596550790.jpg","width":429,"height":429},{"@type":"BreadcrumbList","@id":"https:\/\/www.simcentric.com\/sc\/hong-kong-dedicated-server-sc\/what-is-a-gpu-dedicated-server-and-what-are-its-uses\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.simcentric.com\/sc\/"},{"@type":"ListItem","position":2,"name":"\u4ec0\u4e48\u662fGPU\u72ec\u7acb\u663e\u5361\u670d\u52a1\u5668? \u5b83\u6709\u54ea\u4e9b\u7528\u9014?"}]},{"@type":"WebSite","@id":"https:\/\/simcentric.com\/tc\/#website","url":"https:\/\/simcentric.com\/tc\/","name":"Simcentric Solutions","description":"","publisher":{"@id":"https:\/\/simcentric.com\/tc\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/simcentric.com\/tc\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"zh-CHN"},{"@type":"Organization","@id":"https:\/\/simcentric.com\/tc\/#organization","name":"Simcentric Solutions","url":"https:\/\/simcentric.com\/tc\/","logo":{"@type":"ImageObject","inLanguage":"zh-CHN","@id":"https:\/\/simcentric.com\/tc\/#\/schema\/logo\/image\/","url":"https:\/\/www.simcentric.com\/wp-content\/uploads\/2023\/06\/sim-logo-2023.png","contentUrl":"https:\/\/www.simcentric.com\/wp-content\/uploads\/2023\/06\/sim-logo-2023.png","width":800,"height":222,"caption":"Simcentric Solutions"},"image":{"@id":"https:\/\/simcentric.com\/tc\/#\/schema\/logo\/image\/"}},{"@type":"Person","@id":"https:\/\/simcentric.com\/tc\/#\/schema\/person\/2865b3454f789caf7083a203799d4a6d","name":"Felix Cheung","image":{"@type":"ImageObject","inLanguage":"zh-CHN","@id":"https:\/\/secure.gravatar.com\/avatar\/836e6f2be80c47f0897198ffea03fae331dad9aaafbc988c752691eb595e0e2f?s=96&d=mm&r=g","url":"https:\/\/secure.gravatar.com\/avatar\/836e6f2be80c47f0897198ffea03fae331dad9aaafbc988c752691eb595e0e2f?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/836e6f2be80c47f0897198ffea03fae331dad9aaafbc988c752691eb595e0e2f?s=96&d=mm&r=g","caption":"Felix Cheung"}}]}},"_links":{"self":[{"href":"https:\/\/www.simcentric.com\/sc\/wp-json\/wp\/v2\/posts\/13425","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.simcentric.com\/sc\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.simcentric.com\/sc\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.simcentric.com\/sc\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.simcentric.com\/sc\/wp-json\/wp\/v2\/comments?post=13425"}],"version-history":[{"count":1,"href":"https:\/\/www.simcentric.com\/sc\/wp-json\/wp\/v2\/posts\/13425\/revisions"}],"predecessor-version":[{"id":13428,"href":"https:\/\/www.simcentric.com\/sc\/wp-json\/wp\/v2\/posts\/13425\/revisions\/13428"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.simcentric.com\/sc\/wp-json\/wp\/v2\/media\/13381"}],"wp:attachment":[{"href":"https:\/\/www.simcentric.com\/sc\/wp-json\/wp\/v2\/media?parent=13425"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.simcentric.com\/sc\/wp-json\/wp\/v2\/categories?post=13425"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.simcentric.com\/sc\/wp-json\/wp\/v2\/tags?post=13425"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}