GPU \u589e\u5f37\u4f3a\u670d\u5668\u79df\u7528\u7684\u5be6\u969b\u61c9\u7528<\/strong><\/h2>\nGPU \u52a0\u901f\u4f3a\u670d\u5668\u79df\u7528\u7684\u591a\u529f\u80fd\u6027\u5ef6\u4f38\u5230\u5404\u500b\u9818\u57df\uff1a<\/p>\n
1. \u6a5f\u5668\u5b78\u7fd2\u548c\u4eba\u5de5\u667a\u6167<\/h3>\n
GPU \u5df2\u6210\u70ba AI \u548c\u6a5f\u5668\u5b78\u7fd2\u64cd\u4f5c\u7684\u652f\u67f1\u3002\u5b83\u5011\u7684\u4e26\u884c\u8655\u7406\u80fd\u529b\u4f7f\u5176\u975e\u5e38\u9069\u5408\u8a13\u7df4\u795e\u7d93\u7db2\u8def\u548c\u8655\u7406\u5927\u91cf\u8cc7\u6599\u96c6\u3002\u4ee5\u4e0b\u662f\u4e00\u500b\u4f7f\u7528 TensorFlow \u9032\u884c GPU \u52a0\u901f\u7684\u7c21\u55ae Python \u7a0b\u5f0f\u78bc\u7bc4\u4f8b\uff1a<\/p>\n
\r\nimport tensorflow as tf\r\n\r\n# \u6aa2\u67e5 GPU \u53ef\u7528\u6027\r\nprint(\"\u53ef\u7528 GPU \u6578\u91cf: \", len(tf.config.experimental.list_physical_devices('GPU')))\r\n\r\n# \u5b9a\u7fa9\u4e00\u500b\u7c21\u55ae\u7684\u6a21\u578b\r\nmodel = tf.keras.Sequential([\r\n tf.keras.layers.Dense(64, activation='relu', input_shape=(10,)),\r\n tf.keras.layers.Dense(64, activation='relu'),\r\n tf.keras.layers.Dense(1)\r\n])\r\n\r\n# \u7de8\u8b6f\u4e26\u8a13\u7df4\u6a21\u578b\r\nmodel.compile(optimizer='adam', loss='mse')\r\nmodel.fit(x_train, y_train, epochs=100, batch_size=32)\r\n<\/code><\/pre>\n2. \u5927\u6578\u64da\u5206\u6790<\/h3>\n
GPU \u52a0\u901f\u4f3a\u670d\u5668\u64c5\u9577\u8655\u7406\u548c\u5206\u6790\u5927\u578b\u8cc7\u6599\u96c6\uff0c\u5be6\u73fe\u5373\u6642\u6d1e\u5bdf\u548c\u8907\u96dc\u8cc7\u6599\u8996\u89ba\u5316\u3002\u50cf RAPIDS \u9019\u6a23\u7684\u6846\u67b6\u5229\u7528 GPU \u52a0\u901f\u8cc7\u6599\u79d1\u5b78\u5de5\u4f5c\u6d41\u7a0b\uff1a<\/p>\n
\r\nimport cudf\r\nimport cupy as cp\r\n\r\n# \u5c07\u8cc7\u6599\u8f09\u5165\u5230 GPU \u8a18\u61b6\u9ad4\r\ndf = cudf.read_csv('large_dataset.csv')\r\n\r\n# \u5728 GPU \u4e0a\u57f7\u884c\u64cd\u4f5c\r\nresult = df.groupby('category').agg({'value': ['mean', 'std']})\r\n\r\n# \u8a08\u7b97\u76f8\u95dc\u77e9\u9663\r\ncorrelation = df.corr()\r\n\r\nprint(result)\r\nprint(correlation)\r\n<\/code><\/pre>\n3. \u8996\u8a0a\u8f49\u78bc\u548c\u6e32\u67d3<\/h3>\n
\u5c0d\u65bc\u5167\u5bb9\u5206\u767c\u7db2\u8def\u548c\u4e32\u6d41\u5a92\u9ad4\u670d\u52d9\uff0cGPU \u52a0\u901f\u4f3a\u670d\u5668\u986f\u8457\u6e1b\u5c11\u4e86\u8996\u8a0a\u8655\u7406\u6642\u9593\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528 FFmpeg \u548c NVIDIA \u7684 NVENC \u7de8\u78bc\u5668\u7684\u7bc4\u4f8b\uff1a<\/p>\n
\r\nffmpeg -hwaccel cuvid -c:v h264_cuvid -i input.mp4 -c:v hevc_nvenc -preset slow -crf 23 -c:a copy output.mp4\r\n<\/code><\/pre>\n4. \u79d1\u5b78\u6a21\u64ec<\/h3>\n
\u8907\u96dc\u7684\u79d1\u5b78\u6a21\u578b\u548c\u6a21\u64ec\u6975\u5927\u5730\u53d7\u76ca\u65bc GPU \u52a0\u901f\u3002\u50cf PyCUDA \u9019\u6a23\u7684\u51fd\u5f0f\u5eab\u4f7f\u79d1\u5b78\u5bb6\u80fd\u5920\u5728 Python \u4e2d\u5229\u7528 GPU \u7684\u5f37\u5927\u529f\u80fd\uff1a<\/p>\n
\r\nimport pycuda.autoinit\r\nimport pycuda.driver as drv\r\nimport numpy as np\r\nfrom pycuda.compiler import SourceModule\r\n\r\nmod = SourceModule(\"\"\"\r\n__global__ void multiply_them(float *dest, float *a, float *b)\r\n{\r\n const int i = threadIdx.x;\r\n dest[i] = a[i] * b[i];\r\n}\r\n\"\"\")\r\n\r\nmultiply_them = mod.get_function(\"multiply_them\")\r\n\r\na = np.random.randn(400).astype(np.float32)\r\nb = np.random.randn(400).astype(np.float32)\r\n\r\ndest = np.zeros_like(a)\r\nmultiply_them(\r\n drv.Out(dest), drv.In(a), drv.In(b),\r\n block=(400,1,1), grid=(1,1))\r\n\r\nprint(dest-a*b)\r\n<\/code><\/pre>\n[\/vc_column_text][\/vc_column][\/vc_row][vc_row el_class=”blog-detail-section”][vc_column][vc_column_text]<\/p>\n