{"id":18913,"date":"2024-11-12T11:31:50","date_gmt":"2024-11-12T03:31:50","guid":{"rendered":"https:\/\/www.simcentric.com\/uncategorized-sc\/nvidia-gpu-sparse-computing-for-hong-kong-data-centers\/"},"modified":"2024-11-12T14:05:17","modified_gmt":"2024-11-12T06:05:17","slug":"nvidia-gpu-sparse-computing-for-hong-kong-data-centers","status":"publish","type":"post","link":"https:\/\/www.simcentric.com\/sc\/hong-kong-dedicated-server-sc\/nvidia-gpu-sparse-computing-for-hong-kong-data-centers\/","title":{"rendered":"\u4e86\u89e3\u9999\u6e2f\u6570\u636e\u4e2d\u5fc3\u7684NVIDIA GPU\u7a00\u758f\u7b97\u529b"},"content":{"rendered":"

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\/\/ \u539f\u59cb\u5bc6\u96c6\u77e9\u9635\r\n[1 0 0 2]\r\n[0 3 0 0]\r\n[0 0 4 0]\r\n[5 0 0 6]\r\n\r\n\/\/ \u538b\u7f29\u7a00\u758f\u884c(CSR)\u683c\u5f0f\r\nvalues = [1, 2, 3, 4, 5, 6]\r\ncol_indices = [0, 3, 1, 2, 0, 3]\r\nrow_ptr = [0, 2, 3, 4, 6]\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
Ampere\u67b6\u6784\u7684\u7a00\u758f\u5b9e\u73b0<\/strong><\/h2>\n
Ampere\u67b6\u6784\u5f15\u5165\u4e86\u7ed3\u6784\u5316\u7a00\u758f\u6a21\u5f0f\uff0c\u5728\u77e9\u9635\u4e58\u6cd5\u8fd0\u7b97\u4e2d\u5b9e\u73b0\u9ad8\u8fbe2\u500d\u7684\u52a0\u901f\u3002\u8fd9\u5bf9\u8fd0\u884c\u5927\u578b\u8bed\u8a00\u6a21\u578b\u7684\u9999\u6e2f\u670d\u52a1\u5668\u6258\u7ba1\u8bbe\u65bd\u7279\u522b\u91cd\u8981\u3002\u8be5\u67b6\u6784\u7684\u7a00\u758f\u5f20\u91cf\u6838\u5fc3\u80fd\u6709\u6548\u5904\u7406\u7ed3\u6784\u5316\u548c\u975e\u7ed3\u6784\u5316\u7a00\u758f\u6027\uff0c\u4f7f\u5176\u9002\u7528\u4e8e\u5404\u79cdAI\u5de5\u4f5c\u8d1f\u8f7d\u3002<\/p>\n
\u7a00\u758f\u7b97\u529b\u7684\u5173\u952e\u6027\u80fd\u6307\u6807\uff1a<\/p>\n
\n\u2022 FP16\u5f20\u91cf\u6838\u5fc3\u8fd0\u7b97\uff1a312 TFLOPS
\n\u2022 INT8\u5f20\u91cf\u6838\u5fc3\u8fd0\u7b97\uff1a624 TOPS
\n\u2022 \u7ed3\u6784\u5316\u7a00\u758f\u6027\u63d0\u5347\uff1a\u6700\u9ad82\u500d
\n\u2022 \u5185\u5b58\u5e26\u5bbd\u8282\u7701\uff1a\u6700\u9ad850%
\n\u2022 \u80fd\u6548\u63d0\u5347\uff1a30-40%\n<\/p>\n
[\/vc_column_text][\/vc_column][\/vc_row][vc_row el_class=”blog-detail-section”][vc_column][vc_column_text]<\/p>\n
\u9999\u6e2f\u6570\u636e\u4e2d\u5fc3\u7684\u4f18\u5316\u7b56\u7565<\/strong><\/h2>\n
\u9999\u6e2f\u670d\u52a1\u5668\u79df\u7528\u4f9b\u5e94\u5546\u53ef\u4ee5\u901a\u8fc7\u51e0\u79cd\u590d\u6742\u7684\u65b9\u6cd5\u5229\u7528\u7a00\u758f\u7b97\u529b\uff1a<\/p>\n
\n1. \u5177\u6709\u81ea\u9002\u5e94\u9608\u503c\u7684\u7cbe\u7ec6\u6a21\u578b\u526a\u679d
\n2. \u52a8\u6001\u7a00\u758f\u6ce8\u610f\u529b\u673a\u5236
\n3. \u6df7\u5408\u7a00\u758f\u6a21\u5f0f\u4ee5\u5b9e\u73b0\u6700\u4f73\u6027\u80fd
\n4. \u81ea\u52a8\u7a00\u758f\u6a21\u5f0f\u53d1\u73b0
\n5. \u8d1f\u8f7d\u5e73\u8861\u7684\u7a00\u758f\u7b97\u529b\u8c03\u5ea6\n<\/p>\n
\u7ed3\u6784\u5316\u7a00\u758f\u6027\u7684\u5b9e\u73b0\u793a\u4f8b\uff1a<\/p>\n
import torch\r\nimport numpy as np\r\n\r\nclass StructuredSparsityOptimizer:\r\n    def __init__(self, sparsity_ratio=0.5, block_size=4):\r\n        self.sparsity_ratio = sparsity_ratio\r\n        self.block_size = block_size\r\n    \r\n    def apply_structured_sparsity(self, tensor):\r\n        # \u521b\u5efa\u5757\u7ed3\u6784\r\n        shape = tensor.shape\r\n        blocked = tensor.view(-1, self.block_size)\r\n        \r\n        # \u8ba1\u7b97\u5757\u7ea7\u91cd\u8981\u6027\r\n        block_importance = torch.norm(blocked, dim=1)\r\n        \r\n        # \u521b\u5efa\u7a00\u758f\u63a9\u7801\r\n        n_blocks = len(block_importance)\r\n        k = int(n_blocks * (1 - self.sparsity_ratio))\r\n        \r\n        # \u83b7\u53d6\u524dk\u4e2a\u91cd\u8981\u5757\r\n        _, indices = torch.topk(block_importance, k)\r\n        mask = torch.zeros(n_blocks, device=tensor.device)\r\n        mask[indices] = 1\r\n        \r\n        # \u5c06\u63a9\u7801\u5e94\u7528\u4e8e\u539f\u59cb\u5f20\u91cf\r\n        blocked_mask = mask.unsqueeze(1).expand(-1, self.block_size)\r\n        return (tensor * blocked_mask.view(shape)).contiguous()\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
\u751f\u4ea7\u73af\u5883\u4e2d\u7684\u6027\u80fd\u57fa\u51c6<\/strong><\/h2>\n
\u5728\u9999\u6e2f\u6570\u636e\u4e2d\u5fc3\u7684\u5e7f\u6cdb\u6d4b\u8bd5\u663e\u793a\u51fa\u663e\u8457\u7684\u6027\u80fd\u63d0\u5347\uff1a<\/p>\n
\n\u2022 \u6240\u6709\u5de5\u4f5c\u8d1f\u8f7d\u7684\u5185\u5b58\u5e26\u5bbd\u4f7f\u7528\u51cf\u5c1140%
\n\u2022 Transformer\u6a21\u578b\u5e73\u5747\u52a0\u901f1.7\u500d
\n\u2022 \u529f\u8017\u964d\u4f4e30%
\n\u2022 \u603b\u62e5\u6709\u6210\u672c\u964d\u4f4e25%
\n\u2022 \u6a21\u578b\u63a8\u7406\u5ef6\u8fdf\u6539\u558445%
\n\u2022 \u6279\u5904\u7406\u541e\u5410\u91cf\u63d0\u53472.1\u500d\n<\/p>\n
[\/vc_column_text][\/vc_column][\/vc_row][vc_row el_class=”blog-detail-section”][vc_column][vc_column_text]<\/p>\n
\u5b9e\u65bd\u6700\u4f73\u5b9e\u8df5<\/strong><\/h2>\n
\u4e3a\u5728\u9999\u6e2f\u670d\u52a1\u5668\u79df\u7528\u73af\u5883\u4e2d\u83b7\u5f97\u6700\u4f73\u7a00\u758f\u7b97\u529b\u6027\u80fd\uff1a<\/p>\n
\n1. \u4f7f\u7528CUDA 11.0+\u4ee5\u652f\u6301\u7a00\u758f\u5f20\u91cf
\n2. \u5728\u8bad\u7ec3\u671f\u95f4\u5b9e\u65bd\u68af\u5ea6\u526a\u679d
\n3. \u4f7f\u7528\u5206\u6790\u5de5\u5177\u76d1\u63a7\u7a00\u758f\u6a21\u5f0f
\n4. \u4f18\u5316\u5185\u5b58\u8bbf\u95ee\u6a21\u5f0f
\n5. \u91c7\u7528\u52a8\u6001\u7535\u538b\u548c\u9891\u7387\u8c03\u8282
\n6. \u5b9e\u73b0\u9ad8\u6548\u7684\u8d1f\u8f7d\u5747\u8861
\n7. \u4f7f\u7528\u611f\u77e5\u7a00\u758f\u6027\u7684\u8c03\u5ea6\u7b97\u6cd5\n<\/p>\n
[\/vc_column_text][\/vc_column][\/vc_row][vc_row el_class=”blog-detail-section”][vc_column][vc_column_text]<\/p>\n
\u672a\u6765\u53d1\u5c55\u548c\u5f71\u54cd<\/strong><\/h2>\n
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\u9700\u8981\u5173\u6ce8\u7684\u5173\u952e\u8d8b\u52bf\uff1a<\/p>\n
\n\u2022 \u52a8\u6001\u7a00\u758f\u6027\u9002\u5e94
\n\u2022 \u786c\u4ef6-\u8f6f\u4ef6\u534f\u540c\u4f18\u5316
\n\u2022 \u9ad8\u7ea7\u538b\u7f29\u6280\u672f
\n\u2022 \u81ea\u52a8\u7a00\u758f\u6a21\u5f0f\u53d1\u73b0
\n\u2022 \u4e0e\u65b0\u5174AI\u67b6\u6784\u7684\u96c6\u6210\n<\/p>\n
\u5b9e\u65bd\u7684\u6280\u672f\u8003\u8651\u56e0\u7d20\uff1a<\/p>\n
\n\u2022 \u77e9\u9635\u7a00\u758f\u6a21\u5f0f\u4f18\u5316
\n\u2022 \u5185\u5b58\u5c42\u6b21\u5229\u7528
\n\u2022 \u5de5\u4f5c\u8d1f\u8f7d\u8c03\u5ea6\u7b97\u6cd5
\n\u2022 \u529f\u8017\u7ba1\u7406
\n\u2022 \u6563\u70ed\u4f18\u5316\u7b56\u7565\n<\/p>\n
\u7406\u89e3\u548c\u5b9e\u65bdGPU\u7a00\u758f\u7b97\u529b\u80fd\u529b\u5bf9\u4e8e\u65e8\u5728\u4f18\u5316\u5176AI\u57fa\u7840\u8bbe\u65bd\u7684\u9999\u6e2f\u670d\u52a1\u5668\u79df\u7528\u4f9b\u5e94\u5546\u6765\u8bf4\u81f3\u5173\u91cd\u8981\u3002\u8fd9\u9879\u6280\u672f\u5bf9\u6027\u80fd\u548c\u6548\u7387\u7684\u5f71\u54cd\u4f7f\u5176\u6210\u4e3a\u73b0\u4ee3\u6570\u636e\u4e2d\u5fc3\u8bbe\u8ba1\u4e2d\u7684\u91cd\u8981\u8003\u8651\u56e0\u7d20\uff0c\u7279\u522b\u662f\u968f\u7740\u9999\u6e2f\u52a0\u5f3a\u5176\u4f5c\u4e3a\u4e9a\u592a\u5730\u533a\u9886\u5148AI\u548c\u4e91\u8ba1\u7b97\u4e2d\u5fc3\u7684\u5730\u4f4d\u3002<\/p>\n
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