{"id":70015,"date":"2026-02-01T12:41:04","date_gmt":"2026-02-01T04:41:04","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/70015.html"},"modified":"2026-02-01T12:41:04","modified_gmt":"2026-02-01T04:41:04","slug":"%e5%a6%82%e4%bd%95%e5%9c%a8gpu%e7%ae%97%e5%8a%9b%e6%9c%8d%e5%8a%a1%e5%99%a8%e4%b8%8a%e9%85%8d%e7%bd%ae%e4%b8%8e%e4%bc%98%e5%8c%96%e6%b7%b1%e5%ba%a6%e5%ad%a6%e4%b9%a0%e6%a1%86%e6%9e%b6%ef%bc%8c","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/70015.html","title":{"rendered":"\u5982\u4f55\u5728GPU\u7b97\u529b\u670d\u52a1\u5668\u4e0a\u914d\u7f6e\u4e0e\u4f18\u5316\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\uff0c\u63d0\u5347\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff08CNN\uff09\u8bad\u7ec3\u4e2d\u7684\u8ba1\u7b97\u541e\u5410\u91cf\uff1f"},"content":{"rendered":"

\u5728\u6df1\u5ea6\u5b66\u4e60\u8fdb\u5165\u5de5\u4e1a\u5e94\u7528\u4e4b\u540e，\u5377\u79ef\u795e\u7ecf\u7f51\u7edc（Convolutional Neural Network, CNN）\u51ed\u501f\u5353\u8d8a\u7684\u56fe\u50cf\u7279\u5f81\u63d0\u53d6\u80fd\u529b\u6210\u4e3a\u89c6\u89c9\u8bc6\u522b\u3001\u76ee\u6807\u68c0\u6d4b\u3001\u8bed\u4e49\u5206\u5272\u7b49\u4efb\u52a1\u7684\u6838\u5fc3\u3002\u968f\u7740\u6a21\u578b\u89c4\u6a21\u548c\u8bad\u7ec3\u6570\u636e\u96c6\u4e0d\u65ad\u589e\u957f，\u8bad\u7ec3\u65f6\u7684\u8ba1\u7b97\u541e\u5410\u91cf（Throughput）\u6210\u4e3a\u5f71\u54cd\u5b9e\u9a8c\u5468\u671f\u548c\u7814\u53d1\u6548\u7387\u7684\u5173\u952e\u6307\u6807\u3002\u9ad8\u6548\u5229\u7528GPU\u7b97\u529b\u670d\u52a1\u5668\u3001\u901a\u8fc7\u8f6f\u786c\u4ef6\u534f\u540c\u7684\u914d\u7f6e\u4e0e\u4f18\u5316，\u80fd\u591f\u663e\u8457\u52a0\u5feb\u8bad\u7ec3\u901f\u5ea6，\u63d0\u9ad8\u6bcf\u79d2\u5904\u7406\u6837\u672c\u6570（samples\/sec），\u52a0\u5feb\u6a21\u578b\u6536\u655b\u3002A5\u6570\u636e\u5c06\u7ed3\u5408\u5177\u4f53\u786c\u4ef6\u914d\u7f6e\u3001\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u53c2\u6570\u3001\u5e95\u5c42\u5e93（CUDA\/cuDNN）\u3001\u6570\u636e\u8f93\u5165\u7ba1\u7ebf\u4f18\u5316\u3001\u6df7\u5408\u7cbe\u5ea6\u8bad\u7ec3\u4e0e\u591a\u5361\u5206\u5e03\u5f0f\u8bad\u7ec3\u7b49\u65b9\u6cd5，\u6df1\u5165\u5256\u6790\u63d0\u5347CNN\u8bad\u7ec3\u541e\u5410\u91cf\u7684\u5b9e\u8df5\u7b56\u7565，\u5e76\u7ed9\u51fa\u6027\u80fd\u8bc4\u6d4b\u5b9e\u4f8b\u3001\u53c2\u6570\u8868\u4e0e\u4ee3\u7801\u793a\u4f8b\u3002<\/p>\n

\n

\u4e00\u3001\u5178\u578bGPU\u7b97\u529b\u670d\u52a1\u5668www.a5idc.com\u786c\u4ef6\u914d\u7f6e\u4e0e\u53c2\u6570\u9009\u578b<\/h3>\n

\u5728\u8bad\u7ec3CNN\u65f6，GPU\u662f\u4e3b\u529b\u8ba1\u7b97\u5355\u5143，\u5185\u5b58\u4e0e\u6570\u636e\u603b\u7ebf\u6027\u80fd\u76f4\u63a5\u5f71\u54cd\u6574\u4f53\u541e\u5410\u91cf\u3002\u4ee5\u4e0b\u662f\u4e00\u53f0\u7528\u4e8e\u6df1\u5ea6\u5b66\u4e60\u8bad\u7ec3\u7684\u670d\u52a1\u5668\u5178\u578b\u786c\u4ef6\u53c2\u6570\u4e3e\u4f8b：<\/p>\n\n\u7ec4\u4ef6\u578b\u53f7\/\u53c2\u6570\u8bf4\u660e<\/tr>\n\n\n\n\n\n\n\n\n\n\n
CPU<\/td>\n2\u00d7 Intel Xeon Gold 6338 (32C\/CPU)<\/td>\n\u5927\u91cfPCIe\u901a\u9053\u652f\u6301\u591a\u5361<\/td>\n<\/tr>\n
GPU<\/td>\n8\u00d7 NVIDIA A100 80GB PCIe<\/td>\n\u5178\u578b\u6df1\u5ea6\u5b66\u4e60\u8bad\u7ec3\u52a0\u901f\u5361<\/td>\n<\/tr>\n
\u4e3b\u677f<\/td>\nSupermicro GPU\u670d\u52a1\u5668\u4e3b\u677f<\/td>\n\u652f\u6301PCIe Gen4 \u00d716\u63d2\u69fd<\/td>\n<\/tr>\n
\u7cfb\u7edf\u5185\u5b58<\/td>\n512GB DDR4-3200<\/td>\n\u5185\u5b58\u5145\u8db3\u907f\u514d\u6570\u636e\u52a0\u8f7d\u74f6\u9888<\/td>\n<\/tr>\n
\u5b58\u50a8<\/td>\n2\u00d71.92TB NVMe SSD (RAID1)<\/td>\n\u6570\u636e\u96c6\u4e0e\u7f13\u5b58<\/td>\n<\/tr>\n
\u7f51\u7edc<\/td>\n100Gb RDMA(Infiniband\/HDR)<\/td>\n\u591a\u8282\u70b9\u5206\u5e03\u5f0f\u8bad\u7ec3<\/td>\n<\/tr>\n
\u7535\u6e90<\/td>\n3000W Titanium PSU<\/td>\n\u7a33\u5b9a\u4f9b\u7535<\/td>\n<\/tr>\n
\u7cfb\u7edf<\/td>\nUbuntu 22.04 LTS<\/td>\n\u6df1\u5ea6\u5b66\u4e60\u751f\u6001\u6210\u719f\u7248\u672c<\/td>\n<\/tr>\n
\u9a71\u52a8<\/td>\nNVIDIA Driver 535.xx<\/td>\n\u4e0eCUDA\u517c\u5bb9<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

GPU\u53c2\u6570\u4e0e\u6027\u80fd\u6307\u6807\u7ec6\u5316<\/h4>\n

\u4ee5 NVIDIA A100 80GB PCIe \u4e3a\u4f8b：<\/p>\n\n\u6307\u6807\u6570\u503c<\/tr>\n\n\n\n\n\n\n\n\n\n
CUDA\u6838\u5fc3\u6570<\/td>\n6912<\/td>\n<\/tr>\n
Tensor Core<\/td>\n3rd Gen Tensor Cores<\/td>\n<\/tr>\n
FP32\u7406\u8bba\u6027\u80fd<\/td>\n19.5 TFLOPS<\/td>\n<\/tr>\n
TF32 Tensor\u6027\u80fd<\/td>\n156 TFLOPS<\/td>\n<\/tr>\n
FP16 Tensor\u6027\u80fd<\/td>\n312 TFLOPS<\/td>\n<\/tr>\n
HBM2\u5185\u5b58<\/td>\n80GB<\/td>\n<\/tr>\n
\u5185\u5b58\u5e26\u5bbd<\/td>\n2039 GB\/s<\/td>\n<\/tr>\n
PCIe\u5e26\u5bbd<\/td>\n32GB\/s (Gen4 \u00d716)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\n

\u4e8c\u3001\u8f6f\u4ef6\u6808\u4e0e\u5173\u952e\u5e95\u5c42\u5e93\u7684\u914d\u7f6e<\/h3>\n

2.1 \u64cd\u4f5c\u7cfb\u7edf\u4e0e\u9a71\u52a8<\/h4>\n
    \n
  • \u64cd\u4f5c\u7cfb\u7edf：Ubuntu 22.04 LTS<\/li>\n
  • NVIDIA \u9a71\u52a8：535.xx \u7cfb\u5217<\/li>\n
  • CUDA Toolkit：CUDA 12.1（\u4e0e\u9a71\u52a8\u517c\u5bb9）<\/li>\n
  • cuDNN：TensorRT 8.x cuDNN 8.9<\/li>\n<\/ul>\n

    \u5b89\u88c5\u4e0e\u9a8c\u8bc1：<\/p>\n

    # \u5b89\u88c5\u9a71\u52a8\u4e0eCUDA<\/span>
    \nsudo<\/span> apt-get<\/span> update
    \nsudo<\/span> apt-get<\/span> install<\/span> -y build-essential dkms
    \n# \u6dfb\u52a0NVIDIA\u6e90\u7136\u540e\u5b89\u88c5<\/span>
    \nsudo<\/span> apt<\/span> install<\/span> nvidia-driver-535
    \n# \u5b89\u88c5CUDA 12.1<\/span>
    \nwget<\/span> https:\/\/developer.download.nvidia.com\/compute\/cuda\/12.1\/local_installers\/cuda-repo-ubuntu2204-12-1-local_12.1.0-1_amd64.deb
    \nsudo<\/span> dpkg -i cuda-repo-*.deb
    \nsudo<\/span> apt-key add<\/span> \/var\/cuda-repo-*\/7fa2af80.pub
    \nsudo<\/span> apt-get<\/span> update
    \nsudo<\/span> apt-get<\/span> install<\/span> -y cuda<\/p>\n

    \u9a8c\u8bc1：<\/p>\n

    nvidia-smi
    \nnvcc –version<\/p>\n

    2.2 \u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u7248\u672c<\/h4>\n

    \u5efa\u8bae\u4f7f\u7528\u5b98\u65b9\u4f18\u5316\u826f\u597d\u7684\u6846\u67b6\u7248\u672c：<\/p>\n\n\u6846\u67b6\u63a8\u8350\u7248\u672c\u4f18\u5316\u91cd\u70b9<\/tr>\n\n\n\n
    PyTorch<\/td>\n2.1.0+cu121<\/td>\n\u4e0eTorch Dataloader\u548cAMP\u534f\u540c<\/td>\n<\/tr>\n
    TensorFlow<\/td>\n2.12+<\/td>\nXLA\u4e0e\u6df7\u5408\u7cbe\u5ea6\u652f\u6301<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    \u5b89\u88c5\u793a\u4f8b（PyTorch）：<\/p>\n

    pip install<\/span> torch<\/span>==<\/span>2.1<\/span>.0+cu121 torchvision<\/span>==<\/span>0.16<\/span>.0+cu121 –extra-index-url https:\/\/download.pytorch.org\/whl\/cu121<\/p>\n

    \n

    \u4e09\u3001\u6570\u636e\u8f93\u5165\u7ba1\u7ebf\u7684\u4f18\u5316<\/h3>\n

    CNN\u8bad\u7ec3\u7684\u74f6\u9888\u7ecf\u5e38\u51fa\u73b0\u5728\u6570\u636e\u51c6\u5907\u4e0e\u8bfb\u53d6\u9636\u6bb5\u3002\u65e0\u8bbaGPU\u591a\u4e48\u5f3a\u5927，\u5982\u679c\u8bfb\u53d6\u6570\u636e\u8ddf\u4e0d\u4e0a，\u6574\u4e2a\u541e\u5410\u91cf\u5c31\u4f1a\u88ab\u62c9\u4f4e\u3002<\/p>\n

    3.1 \u591a\u8fdb\u7a0b\u6570\u636e\u52a0\u8f7d<\/h4>\n

    \u5728PyTorch\u4e2d\u4f7f\u7528DataLoader\u7684num_workers\u53c2\u6570\u63d0\u5347\u5e76\u884c\u8bfb\u53d6\u80fd\u529b：<\/p>\n

    from<\/span> torch.<\/span>utils.<\/span>data import<\/span> DataLoader<\/p>\n

    train_loader =<\/span> DataLoader(<\/span>
    \n train_dataset,<\/span>
    \n batch_size=<\/span>256<\/span>,<\/span>
    \n shuffle=<\/span>True<\/span>,<\/span>
    \n num_workers=<\/span>12<\/span>,<\/span> # \u6839\u636eCPU\u6838\u5fc3\u8c03\u6574<\/span>
    \n pin_memory=<\/span>True<\/span>,<\/span>
    \n prefetch_factor=<\/span>4<\/span>
    \n)<\/span><\/p>\n

    \u4f18\u5316\u70b9：<\/p>\n

      \n
    • num_workers：\u8bbe\u7f6e\u4e3aCPU\u6838\u5fc3\u6570\u76841\/2 ~ 1\u500d<\/li>\n
    • pin_memory=True：\u52a0\u5febGPU\u6570\u636e\u62f7\u8d1d<\/li>\n
    • prefetch_factor：\u6bcf\u4e2aworker\u63d0\u524d\u52a0\u8f7d\u6570\u636e<\/li>\n<\/ul>\n

      3.2 \u6570\u636e\u683c\u5f0f\u4e0e\u5b58\u50a8\u4f18\u5316<\/h4>\n
        \n
      • \u4f7f\u7528\u9ad8\u6548\u4e8c\u8fdb\u5236\u683c\u5f0f\u5982TFRecords\u3001WebDataset（\u9488\u5bf9\u5927\u89c4\u6a21\u6570\u636e）<\/li>\n
      • \u5c06\u6570\u636e\u7f13\u5b58\u5728RAMDisk\u6216\u8005NVMe SSD\u4e2d\u51cf\u5c11I\/O\u5ef6\u8fdf<\/li>\n<\/ul>\n

        \u793a\u4f8b：\u4f7f\u7528WebDataset：<\/p>\n

        pip install<\/span> webdataset<\/p>\n

        import<\/span> webdataset as<\/span> wds<\/p>\n

        train_dataset =<\/span> wds.<\/span>WebDataset(<\/span>"train-{00000..00099}.tar"<\/span>)<\/span>.<\/span>shuffle(<\/span>1000<\/span>)<\/span>.<\/span>decode(<\/span>"pil"<\/span>)<\/span>.<\/span>to_tuple(<\/span>"jpg"<\/span>,<\/span>"cls"<\/span>)<\/span><\/p>\n

        \n

        \u56db\u3001\u6df7\u5408\u7cbe\u5ea6\u8bad\u7ec3\u4e0eTensor Core\u52a0\u901f<\/h3>\n

        \u6700\u65b0GPU（\u5982A100）\u5e7f\u6cdb\u652f\u6301Tensor Core\u9ad8\u6027\u80fd\u8ba1\u7b97，\u5229\u7528\u6df7\u5408\u7cbe\u5ea6（Mixed Precision）\u53ef\u4ee5\u663e\u8457\u63d0\u5347\u541e\u5410\u91cf\u3002<\/p>\n

        4.1 PyTorch AMP（\u81ea\u52a8\u6df7\u5408\u7cbe\u5ea6）<\/h4>\n

        scaler =<\/span> torch.<\/span>cuda.<\/span>amp.<\/span>GradScaler(<\/span>)<\/span><\/p>\n

        for<\/span> data,<\/span> target in<\/span> train_loader:<\/span>
        \n optimizer.<\/span>zero_grad(<\/span>)<\/span>
        \n with<\/span> torch.<\/span>cuda.<\/span>amp.<\/span>autocast(<\/span>)<\/span>:<\/span>
        \n output =<\/span> model(<\/span>data)<\/span>
        \n loss =<\/span> criterion(<\/span>output,<\/span> target)<\/span>
        \n scaler.<\/span>scale(<\/span>loss)<\/span>.<\/span>backward(<\/span>)<\/span>
        \n scaler.<\/span>step(<\/span>optimizer)<\/span>
        \n scaler.<\/span>update(<\/span>)<\/span><\/p>\n

        \u542f\u7528\u540eTensor Core\u53ef\u81ea\u52a8\u5904\u7406FP16\/FP32\u6df7\u5408\u8ba1\u7b97，\u51cf\u5c11\u663e\u5b58\u5360\u7528\u4e14\u63d0\u5347\u541e\u5410\u7387\u3002<\/p>\n

        4.2 \u6027\u80fd\u5bf9\u6bd4（\u5b9e\u9a8c\u6570\u636e）<\/h4>\n\n\u6a21\u5f0fPrecisionThroughput (samples\/sec)GPU Util (%)<\/tr>\n\n\n\n
        Baseline<\/td>\nFP32<\/td>\n980<\/td>\n75<\/td>\n<\/tr>\n
        AMP (Tensor)<\/td>\nFP16\/TF32<\/td>\n2150<\/td>\n94<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        \u6d4b\u8bd5\u6a21\u578b：ResNet-50，Batch Size=256，\u5355\u5361A100<\/p>\n

        \n

        \u4e94\u3001\u591a\u5361\u5e76\u884c\u4e0e\u5206\u5e03\u5f0f\u8bad\u7ec3<\/h3>\n

        \u5355\u5361\u8bad\u7ec3\u5df2\u65e0\u6cd5\u6ee1\u8db3\u5927\u89c4\u6a21CNN\u8bad\u7ec3\u9700\u6c42，\u901a\u8fc7\u591a\u5361\u5e76\u884c\u663e\u8457\u63d0\u5347\u6574\u4f53\u541e\u5410\u91cf\u3002<\/p>\n

        5.1 PyTorch Distributed Data Parallel (DDP)<\/h4>\n

        python -m torch.distributed.launch –nproc_per_node=<\/span>8<\/span> train.py<\/p>\n

        train.py \u6838\u5fc3\u914d\u7f6e：<\/p>\n

        import<\/span> torch.<\/span>distributed as<\/span> dist
        \nfrom<\/span> torch.<\/span>nn.<\/span>parallel import<\/span> DistributedDataParallel as<\/span> DDP<\/p>\n

        dist.<\/span>init_process_group(<\/span>backend=<\/span>"nccl"<\/span>)<\/span>
        \nmodel =<\/span> model.<\/span>to(<\/span>device)<\/span>
        \nmodel =<\/span> DDP(<\/span>model,<\/span> device_ids=<\/span>[<\/span>local_rank]<\/span>,<\/span> output_device=<\/span>local_rank)<\/span><\/p>\n

        5.2 NCCL\u540e\u7aef\u4e0e\u591a\u8fdb\u7a0b\u8bbe\u8ba1<\/h4>\n
          \n
        • backend="nccl" \u4f18\u5316\u591aGPU\u901a\u4fe1<\/li>\n
        • \u4f7f\u7528 torch.utils.data.distributed.DistributedSampler \u786e\u4fdd\u5404\u5361\u6837\u672c\u4e0d\u91cd\u590d<\/li>\n<\/ul>\n
          \n

          \u516d\u3001XLA \/ \u7f16\u8bd1\u5668\u4f18\u5316（TensorFlow \u4e13\u7528）<\/h3>\n

          \u5728TensorFlow\u4e2d\u542f\u7528XLA（Accelerated Linear Algebra）\u53ef\u4ee5\u5bf9\u8ba1\u7b97\u56fe\u8fdb\u884c\u5b50\u56fe\u4f18\u5316：<\/p>\n

          import<\/span> tensorflow as<\/span> tf
          \ntf.<\/span>config.<\/span>optimizer.<\/span>set_jit(<\/span>True<\/span>)<\/span><\/p>\n

          \n

          \u4e03\u3001\u5178\u578bCNN\u8bad\u7ec3\u541e\u5410\u91cf\u4f18\u5316\u5bf9\u6bd4\u8868<\/h3>\n\n\u4f18\u5316\u7b56\u7565\u6838\u5fc3\u4f5c\u7528\u63d0\u5347\u7387（\u76f8\u5bf9Baseline）<\/tr>\n\n\n\n\n\n\n
          \u591a\u8fdb\u7a0b\u6570\u636e\u52a0\u8f7d<\/td>\n\u63d0\u5347\u6570\u636e\u8bfb\u53d6\u6548\u7387<\/td>\n1.3\u00d7<\/td>\n<\/tr>\n
          \u6570\u636e\u683c\u5f0f\u4f18\u5316（WebDataset）<\/td>\n\u51cf\u5c11I\/O\u5ef6\u8fdf<\/td>\n1.15\u00d7<\/td>\n<\/tr>\n
          \u6df7\u5408\u7cbe\u5ea6（AMP）<\/td>\nTensor Core\u9ad8\u6548\u8ba1\u7b97<\/td>\n2.2\u00d7<\/td>\n<\/tr>\n
          \u591a\u5361DDP<\/td>\n\u5e76\u884c\u8bad\u7ec3\u52a0\u901f<\/td>\n7.8\u00d7（8\u5361）<\/td>\n<\/tr>\n
          CUDA\u6838\u51fd\u6570\u8c03\u4f18（cuDNN）<\/td>\n\u9ad8\u6548\u5377\u79ef\u5b9e\u73b0<\/td>\n1.1\u00d7<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
          \n

          \u516b\u3001\u6df1\u5165\u6280\u672f\u7ec6\u8282\u4e0e\u8c03\u4f18\u5efa\u8bae<\/h3>\n

          8.1 GPU\u663e\u5b58\u7ba1\u7406<\/h4>\n
            \n
          • \u8bbe\u7f6e\u5408\u7406Batch Size，\u4e0d\u8d85\u8fc7\u663e\u5b58\u754c\u9650<\/li>\n
          • \u4f7f\u7528\u68af\u5ea6\u7d2f\u79ef（Grad Accumulation）\u6a21\u62df\u66f4\u5927Batch<\/li>\n<\/ul>\n

            accum_steps =<\/span> 4<\/span>
            \noptimizer.<\/span>zero_grad(<\/span>)<\/span>
            \nfor<\/span> i,<\/span> data in<\/span> enumerate<\/span>(<\/span>train_loader)<\/span>:<\/span>
            \n with<\/span> autocast(<\/span>)<\/span>:<\/span>
            \n loss =<\/span> model(<\/span>data)<\/span> \/<\/span> accum_steps
            \n scaler.<\/span>scale(<\/span>loss)<\/span>.<\/span>backward(<\/span>)<\/span>
            \n if<\/span> (<\/span>i+<\/span>1<\/span>)<\/span> %<\/span> accum_steps ==<\/span> 0<\/span>:<\/span>
            \n scaler.<\/span>step(<\/span>optimizer)<\/span>
            \n scaler.<\/span>update(<\/span>)<\/span>
            \n optimizer.<\/span>zero_grad(<\/span>)<\/span><\/p>\n

            8.2 \u7f51\u7edc\u5e26\u5bbd\u4e0e\u591a\u8282\u70b9\u5206\u5e03\u5f0f\u8bad\u7ec3<\/h4>\n
              \n
            • \u591a\u8282\u70b9\u4f7f\u7528 RDMA\/Infiniband<\/li>\n
            • NCCL \u73af\u5883\u53d8\u91cf\u8c03\u4f18:<\/li>\n<\/ul>\n

              export<\/span> NCCL_DEBUG<\/span>=<\/span>INFO
              \nexport<\/span> NCCL_IB_STRICT_PEER_ORDER<\/span>=<\/span>1<\/span>
              \nexport<\/span> NCCL_SOCKET_IFNAME<\/span>=<\/span>eth0<\/p>\n

              \n

              \u4e5d\u3001\u603b\u7ed3<\/h3>\n

              \u63d0\u5347CNN\u8bad\u7ec3\u7684\u8ba1\u7b97\u541e\u5410\u91cf\u4e0d\u662f\u5355\u4e00\u4f18\u5316\u70b9\u53ef\u4ee5\u5b8c\u6210\u7684，\u800c\u662f\u8f6f\u786c\u4ef6\u534f\u540c\u8c03\u4f18\u7684\u7cfb\u7edf\u5de5\u7a0b\u3002\u4ece\u5e95\u5c42\u9a71\u52a8\u3001\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u7248\u672c，\u5230\u6570\u636e\u7ba1\u7ebf\u3001\u6df7\u5408\u7cbe\u5ea6\u4e0e\u591a\u5361\u5e76\u884c，\u6bcf\u4e00\u5c42\u90fd\u5b58\u5728\u63d0\u5347\u7a7a\u95f4\u3002\u901a\u8fc7\u5408\u7406\u914d\u7f6eGPU\u670d\u52a1\u5668\u786c\u4ef6\u3001\u4f18\u5316\u6570\u636e\u52a0\u8f7d\u4e0e\u5b58\u50a8\u3001\u542f\u7528Tensor Core\u3001\u7ed3\u5408\u5206\u5e03\u5f0f\u5e76\u884c\u7b56\u7565，\u53ef\u4ee5\u5728\u5b9e\u9645\u8bad\u7ec3\u4e2d\u83b7\u5f97\u663e\u8457\u7684\u6027\u80fd\u63d0\u5347\u3002A5\u6570\u636e\u5217\u4e3e\u4e86\u5178\u578b\u786c\u4ef6\u53c2\u6570\u3001\u5e95\u5c42\u5e93\u5b89\u88c5\u4e0e\u9a8c\u8bc1\u3001\u4ee3\u7801\u793a\u4f8b\u4e0e\u6027\u80fd\u8bc4\u6d4b\u6570\u636e，\u4fbf\u4e8e\u5728\u5b9e\u9645\u90e8\u7f72\u4e2d\u53c2\u8003\u4e0e\u590d\u73b0\u3002<\/p>\n

              \u5982\u9700\u9488\u5bf9\u5177\u4f53\u6a21\u578b\u3001\u6570\u636e\u96c6\u7684\u5b9a\u5236\u5316\u8c03\u4f18\u65b9\u6848，\u53ef\u4ee5\u8fdb\u4e00\u6b65\u5206\u6790\u74f6\u9888\u6307\u6807（\u5982PCIe\u5229\u7528\u7387\u3001GPU\u6d3b\u8dc3\u7387\u3001\u6570\u636e\u52a0\u8f7d\u5ef6\u8fdf\u7b49）\u5e76\u505a\u9488\u5bf9\u6027\u7684\u4f18\u5316\u3002\u5e0c\u671b\u672c\u6587\u80fd\u591f\u4f5c\u4e3a\u4f60\u5728GPU\u7b97\u529b\u670d\u52a1\u5668\u4e0a\u8bad\u7ec3CNN\u65f6\u7684\u5b9e\u6218\u6307\u5357\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

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