{"id":109261,"date":"2026-09-23T19:25:42","date_gmt":"2026-09-23T11:25:42","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/109261.html"},"modified":"2026-09-23T19:25:42","modified_gmt":"2026-09-23T11:25:42","slug":"gpu%e6%98%be%e5%8d%a1%e6%9c%8d%e5%8a%a1%e5%99%a8%e7%9a%84%e8%83%bd%e6%95%88%e4%b8%8e%e7%ae%97%e5%8a%9b%e6%af%94%e4%bc%98%e5%8c%96%e5%ae%9e%e8%b7%b5%ef%bc%9a%e5%9c%a8-ubuntu-%e4%b8%8a%e5%ae%9e%e7%8e%b0","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/109261.html","title":{"rendered":"GPU\u663e\u5361\u670d\u52a1\u5668\u7684\u80fd\u6548\u4e0e\u7b97\u529b\u6bd4\u4f18\u5316\u5b9e\u8df5\uff1a\u5728 Ubuntu \u4e0a\u5b9e\u73b0 AI \u8d1f\u8f7d\u4e0b\u7684\u529f\u8017\u4e0e\u6027\u80fd\u5e73\u8861"},"content":{"rendered":"

\/* MD \/ \u5bcc\u6587\u672c\u4e2d\u7684 .toc\uff08\u542b\u535a\u5ba2\u56ed\u642c\u5bb6\u7b49\u5d4c\u5957\u7ed3\u6784\uff09\uff1b.toc-box \u5728\u4fa7\u680f\uff0c\u4e0d\u53d7\u5f71\u54cd *\/
\n #content_views .toc,
\n \/* \u7f16\u8f91\u5668\u5e38\u5728\u76ee\u5f55\u524d\u540e\u63d2\u5165\u7a7a p\uff08:empty \u4ecd\u5360 20px\uff09\uff0c\u4e00\u5e76\u53bb\u6389\u907f\u514d\u9876\u7a7a\u9699 *\/
\n #content_views.markdown_views > p:empty:has(+ .toc),
\n #content_views.markdown_views > .toc + p:empty,
\n \/* \u5bcc\u6587\u672c\u65e7\u7248\u76ee\u5f55\u6807\u8bb0 *\/
\n #content_views.htmledit_views #main-toc,
\n #content_views.htmledit_views #hr-toc,
\n #content_views.htmledit_views p[id*=\\”-toc\\”] {
\n display: none !important;
\n }
\n \/* \u76ee\u5f55\u53bb\u6389\u540e\uff0c\u7d27\u8ddf\u7684\u9996\u4e2a\u6807\u9898\u4e0d\u518d\u591a\u51fa\u4e00\u5757\u4e0a\u8fb9\u8ddd *\/
\n #content_views.markdown_views > .toc + h1,
\n #content_views.markdown_views > .toc + h2,
\n #content_views.markdown_views > .toc + h3,
\n #content_views.markdown_views > .toc + h4,
\n #content_views.markdown_views > .toc + p:empty + h1,
\n #content_views.markdown_views > .toc + p:empty + h2,
\n #content_views.markdown_views > .toc + p:empty + h3,
\n #content_views.markdown_views > .toc + p:empty + h4 {
\n margin-top: 0 !important;
\n }<\/p>\n

\u5728\u5927\u89c4\u6a21 AI \u63a8\u7406\u4e0e\u8bad\u7ec3\u573a\u666f\u4e2d，\u5355\u7eaf\u8ffd\u6c42\u7b97\u529b\u6307\u6807（\u5982 TFLOPS\u3001FP16 \u8bad\u7ec3\u901f\u5ea6）\u5f80\u5f80\u4f34\u968f\u7740\u9ad8\u529f\u8017\u4e0e\u80fd\u6548\u4f4e\u4e0b\u7684\u95ee\u9898\u3002\u5bf9\u4e8e\u4e1a\u52a1\u6301\u7eed\u8fd0\u884c\u7684 GPU \u670d\u52a1\u5668\u800c\u8a00，\u80fd\u6548\u6bd4（Compute\u2011Per\u2011Watt）\u5df2\u7ecf\u6210\u4e3a\u8861\u91cf\u786c\u4ef6\u4e0e\u7cfb\u7edf\u8c03\u4f18\u6c34\u5e73\u7684\u91cd\u8981\u6307\u6807\u3002A5\u6570\u636e\u672c\u7bc7\u6587\u7ae0\u805a\u7126\u4e8e\u5728 Ubuntu 22.04 LTS \u670d\u52a1\u5668\u73af\u5883\u4e0b，\u5982\u4f55\u901a\u8fc7\u786c\u4ef6\u914d\u7f6e\u3001\u7cfb\u7edf\u8c03\u5ea6\u3001GPU \u9a71\u52a8\u4e0e\u8fd0\u884c\u65f6\u7b56\u7565，\u7cfb\u7edf\u6027\u5730\u4f18\u5316 AI \u8d1f\u8f7d\u4e0b\u7684\u80fd\u8017\u4e0e\u6027\u80fd\u5e73\u8861\u3002\u6211\u4eec\u4ee5\u4e3b\u6d41 NVIDIA A100 \/ H100 GPU \u4e3a\u57fa\u7840，\u5c55\u5f00\u5177\u4f53\u5b9e\u8df5\u4e0e\u8bc4\u6d4b\u3002<\/p>\n

\u4e00\u3001\u5b9e\u9a8c\u5e73\u53f0\u4e0e\u786c\u4ef6\u914d\u7f6e<\/h3>\n

\u672c\u6b21\u4f18\u5316\u5b9e\u8df5\u9009\u53d6\u884c\u4e1a\u5178\u578bGPU\u670d\u52a1\u5668www.a5idc.com\u914d\u7f6e\u5982\u4e0b：<\/p>\n\n\u786c\u4ef6\u7c7b\u522b\u578b\u53f7 \/ \u89c4\u683c\u8bf4\u660e<\/tr>\n\n\n\n\n\n\n\n\n\n\n
\u4e3b\u673a\u578b\u53f7<\/td>\n\u81ea\u7814\u6df7\u5408\u578b\u673a\u67b6\u670d\u52a1\u5668<\/td>\n2U \u89c4\u683c<\/td>\n<\/tr>\n
CPU<\/td>\nAMD EPYC 7713 (64 \u6838 @ 2.0\u20113.65GHz)<\/td>\n\u9ad8\u5185\u5b58\u5e26\u5bbd\u5e73\u53f0<\/td>\n<\/tr>\n
\u5185\u5b58<\/td>\n1024 GB DDR4 RDIMM<\/td>\nAI \u5927\u6a21\u578b\u5185\u5b58\u9700\u6c42<\/td>\n<\/tr>\n
GPU<\/td>\n4 \u00d7 NVIDIA A100 80GB PCIe<\/td>\n\u4e3b\u529b\u8bad\u7ec3\u63a8\u7406\u5361<\/td>\n<\/tr>\n
GPU （\u5bf9\u6bd4\u7ec4）<\/td>\n4 \u00d7 NVIDIA H100 80GB PCIe<\/td>\n\u4e0b\u4e00\u4ee3\u5bf9\u6bd4\u6d4b\u8bd5<\/td>\n<\/tr>\n
\u4e3b\u5b58\u50a8<\/td>\n4TB NVMe SSD<\/td>\n\u6570\u636e\u96c6\u4e0e\u65e5\u5fd7<\/td>\n<\/tr>\n
\u64cd\u4f5c\u7cfb\u7edf<\/td>\nUbuntu 22.04 LTS<\/td>\nLinux \u751f\u6001\u6807\u51c6<\/td>\n<\/tr>\n
NVIDIA \u9a71\u52a8<\/td>\n535.x \u53ca\u4ee5\u4e0a<\/td>\n\u652f\u6301\u6700\u65b0 NVML \u529f\u80fd<\/td>\n<\/tr>\n
CUDA<\/td>\nCUDA 12.2<\/td>\n\u6700\u65b0\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u652f\u6301<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\n

\u4e8c\u3001\u80fd\u6548\u4e0e\u7b97\u529b\u7684\u8861\u91cf\u6307\u6807<\/h3>\n

\u4e3a\u4e86\u91cf\u5316\u4f18\u5316\u6548\u679c，\u672c\u5b9e\u9a8c\u91c7\u7528\u4ee5\u4e0b\u6307\u6807：<\/p>\n\n\u6307\u6807\u540d\u79f0\u8ba1\u7b97\u65b9\u5f0f \/ \u5de5\u5177\u7528\u9014<\/tr>\n\n\n\n\n\n\n
GPU Utilization %<\/td>\nnvidia\u2011smi<\/td>\nGPU \u4f7f\u7528\u7387\u76d1\u63a7<\/td>\n<\/tr>\n
Power Draw (W)<\/td>\nnvidia\u2011smi \/ NVML<\/td>\n\u5b9e\u65f6\u529f\u8017<\/td>\n<\/tr>\n
Compute Throughput<\/td>\n\u5cf0\u503c GFLOPS \/ \u5b9e\u9645\u8bad\u7ec3\u6b65\u65f6 (s\/step)<\/td>\n\u7b97\u529b\u8861\u91cf<\/td>\n<\/tr>\n
Efficiency (GFLOPS\/W)<\/td>\nCompute Throughput \/ Power Draw<\/td>\n\u80fd\u6548\u6bd4\u5bf9\u6bd4<\/td>\n<\/tr>\n
Latency (ms)<\/td>\n\u81ea\u5b9a\u4e49\u6d4b\u8bd5\u811a\u672c<\/td>\n\u63a8\u7406\u5ef6\u8fdf\u8bc4\u4f30<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\n

\u4e09\u3001\u57fa\u7840\u73af\u5883\u90e8\u7f72\u4e0e\u76d1\u63a7\u914d\u7f6e<\/h3>\n

3.1 \u5b89\u88c5 NVIDIA \u9a71\u52a8\u4e0e CUDA \u5de5\u5177\u5305<\/h4>\n

\u786e\u4fdd\u5b89\u88c5\u6700\u65b0\u7a33\u5b9a\u9a71\u52a8\u4ee5\u83b7\u53d6\u5b8c\u6574 NVML \u80fd\u8017\u63a5\u53e3\u652f\u6301：<\/p>\n

# \u66f4\u65b0\u4ed3\u5e93<\/span>
\nsudo<\/span> apt<\/span> update &&<\/span> sudo<\/span> apt<\/span> upgrade -y<\/p>\n

# \u6dfb\u52a0 NVIDIA apt \u6e90<\/span>
\nsudo<\/span> apt<\/span> install<\/span> -y software-properties-common
\nsudo<\/span> add\u2011apt\u2011repository ppa:graphics\u2011drivers\/ppa
\nsudo<\/span> apt<\/span> update<\/p>\n

# \u5b89\u88c5 NVIDIA \u9a71\u52a8<\/span>
\nsudo<\/span> apt<\/span> install<\/span> -y nvidia\u2011driver\u2011535<\/p>\n

# \u5b89\u88c5 CUDA \u5de5\u5177\u5305（\u793a\u4f8b）<\/span>
\nwget<\/span> https:\/\/developer.download.nvidia.com\/compute\/cuda\/12.2\/local_installers\/cuda\u201112.2.0_linux.run
\nsudo<\/span> sh<\/span> cuda\u201112.2.0_linux.run<\/p>\n

\u9a71\u52a8\u4e0e CUDA \u7248\u672c\u9700\u4e0e\u5b9e\u9645 GPU \u578b\u53f7\u517c\u5bb9\u3002<\/p>\n

3.2 GPU \u6027\u80fd\u4e0e\u529f\u8017\u76d1\u63a7<\/h4>\n

\u4f7f\u7528 nvidia\u2011smi \u4e0e Python NVML \u7ed1\u5b9a\u5b9e\u65f6\u91c7\u96c6：<\/p>\n

import<\/span> time
\nimport<\/span> pynvml<\/p>\n

pynvml.<\/span>nvmlInit(<\/span>)<\/span>
\nhandle =<\/span> pynvml.<\/span>nvmlDeviceGetHandleByIndex(<\/span>0<\/span>)<\/span><\/p>\n

while<\/span> True<\/span>:<\/span>
\n power =<\/span> pynvml.<\/span>nvmlDeviceGetPowerUsage(<\/span>handle)<\/span> \/<\/span> 1000<\/span>
\n util =<\/span> pynvml.<\/span>nvmlDeviceGetUtilizationRates(<\/span>handle)<\/span>.<\/span>gpu
\n print<\/span>(<\/span>f"GPU0 Power: <\/span>{<\/span>power:<\/span>.1f<\/span>}<\/span><\/span> W, Util: <\/span>{<\/span>util}<\/span><\/span> %"<\/span><\/span>)<\/span>
\n time.<\/span>sleep(<\/span>1<\/span>)<\/span><\/p>\n

\n

\u56db\u3001\u80fd\u6548\u4f18\u5316\u7b56\u7565\u5b9e\u8df5<\/h3>\n

4.1 GPU Power Limit（\u529f\u8017\u4e0a\u9650）\u8c03\u4f18<\/h4>\n

\u5e73\u8861\u80fd\u6548\u4e0e\u6027\u80fd\u7684\u6838\u5fc3\u65b9\u6cd5\u4e4b\u4e00，\u662f\u5c06 GPU \u7684\u529f\u8017\u4e0a\u9650\u8bbe\u7f6e\u5728\u4e00\u4e2a\u201c\u62d0\u70b9\u201d\u503c：<\/p>\n

# \u67e5\u770b\u5f53\u524d\u529f\u8017\u4e0a\u9650<\/span>
\nnvidia\u2011smi \u2011i 0<\/span> \u2011q |<\/span> grep<\/span> "Power Limit"<\/span><\/p>\n

# \u8bbe\u7f6e\u65b0\u7684\u529f\u8017\u4e0a\u9650（\u4f8b\u5982 250 W）<\/span>
\nsudo<\/span> nvidia\u2011smi \u2011i 0<\/span> \u2011pl 250<\/span><\/p>\n

\u5bf9\u4e8e A100 \u9ed8\u8ba4\u529f\u8017\u4e0a\u9650\u7ea6\u4e3a 400W，\u8bbe\u7f6e\u4e3a 250\u2011300W \u53ef\u660e\u663e\u63d0\u5347\u80fd\u6548\u6bd4（GFLOPS\/W）\u4f46\u5bf9\u6027\u80fd\u5f71\u54cd\u53ef\u63a7\u3002<\/p>\n

4.1.1 A100 \u529f\u8017\u4e0a\u9650\u5b9e\u9a8c\u7ed3\u679c<\/h5>\n\nPower Limit (W)GPU Util (%)Throughput (GFLOPS)Efficiency (GFLOPS\/W)<\/tr>\n\n\n\n\n
400 (\u9ed8\u8ba4)<\/td>\n98<\/td>\n19600<\/td>\n49.0<\/td>\n<\/tr>\n
300<\/td>\n93<\/td>\n18200<\/td>\n60.7<\/td>\n<\/tr>\n
250<\/td>\n85<\/td>\n16000<\/td>\n64.0<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u5206\u6790： \u8bbe\u7f6e 300W \u65f6\u6027\u80fd\u4e0b\u964d\u7ea6 7%，\u4f46\u80fd\u6548\u63d0\u5347 24%\u3002\u8fdb\u4e00\u6b65\u964d\u4f4e\u5230 250W \u6027\u80fd\u4e0b\u964d\u66f4\u660e\u663e，\u4f46 GFLOPS\/W \u4ecd\u6709\u589e\u957f\u3002<\/p>\n

\n

4.2 GPU \u9891\u7387\u9650\u5236（ SM Clock ）\u8c03\u4f18<\/h4>\n

\u5bf9\u4e8e NVIDIA GPU，\u53ef\u4f7f\u7528 nvidia\u2011smi \u2011ac \u5bf9\u5185\u5b58\u4e0e\u6838\u5fc3\u9891\u7387\u8fdb\u884c\u7cbe\u51c6\u63a7\u5236：<\/p>\n

# \u67e5\u8be2\u53ef\u7528\u9891\u7387\u8303\u56f4<\/span>
\nnvidia\u2011smi -q -d SUPPORTED_CLOCKS<\/p>\n

# \u8bbe\u7f6e SM \/ Memory \u9891\u7387<\/span>
\nsudo<\/span> nvidia\u2011smi -i 0<\/span> -ac 1215,1410<\/span><\/p>\n

\u6b64\u5904\u6570\u503c\u9700\u7ed3\u5408\u9a71\u52a8\u652f\u6301\u7684\u9891\u7387，\u907f\u514d\u8d85\u9891\u5bfc\u81f4\u529f\u8017\u6fc0\u589e\u3002<\/p>\n

4.3 CUDA \u8fd0\u884c\u65f6\u4f18\u5316<\/h4>\n

\u5b9e\u9645\u6a21\u578b\u63a8\u7406\u4e0e\u8bad\u7ec3\u8fc7\u7a0b\u4e2d，\u52a1\u5fc5\u542f\u7528 Tensor Core \u4e0e\u6df7\u5408\u7cbe\u5ea6（FP16 \/ BFLOAT16）：<\/p>\n

\u5728 PyTorch \u4e2d：<\/p>\n

model.<\/span>half(<\/span>)<\/span>
\nwith<\/span> torch.<\/span>cuda.<\/span>amp.<\/span>autocast(<\/span>)<\/span>:<\/span>
\n outputs =<\/span> model(<\/span>inputs)<\/span><\/p>\n

\u501f\u52a9 AMP（\u81ea\u52a8\u6df7\u5408\u7cbe\u5ea6），\u5728\u786c\u4ef6\u652f\u6301\u4e0b\u63d0\u5347\u541e\u5410\u91cf\u540c\u65f6\u964d\u4f4e\u529f\u8017\u3002<\/p>\n

\n

\u4e94\u3001\u7cfb\u7edf\u7ea7\u4f18\u5316<\/h3>\n

5.1 \u5173\u95ed\u52a8\u6001\u9891\u7387\u8c03\u6574<\/h4>\n

\u9ed8\u8ba4\u7684 Linux CPU \u8c03\u5ea6\u53ef\u80fd\u5f71\u54cd GPU\u2011CPU \u534f\u540c\u6548\u7387，\u5efa\u8bae\u8bbe\u5b9a CPU \u4e3a\u6027\u80fd\u65b9\u6848：<\/p>\n

sudo<\/span> apt<\/span> install<\/span> cpufrequtils
\nsudo<\/span> cpufreq-set –governor performance<\/p>\n

5.2 NUMA \u4e0e PCIe \u62d3\u6251\u4f18\u5316<\/h4>\n

\u786e\u4fdd GPU \u4e0e\u5176\u9a71\u52a8\u7684 CPU \u7ed1\u5b9a\u5728\u540c\u4e00 NUMA \u8282\u70b9：<\/p>\n

lspci |<\/span> grep<\/span> NVIDIA
\nnumactl –hardware<\/p>\n

\u4f8b\u5982：<\/p>\n

numactl –cpunodebind=<\/span>0<\/span> –membind=<\/span>0<\/span> python train.py<\/p>\n

\u907f\u514d\u8de8\u8282\u70b9\u6570\u636e\u4f20\u8f93\u5bfc\u81f4\u5ef6\u8fdf\u548c\u80fd\u8017\u4e0a\u5347\u3002<\/p>\n

\n

\u516d\u3001\u7efc\u5408\u8bc4\u6d4b\u6570\u636e<\/h3>\n

6.1 \u6d4b\u8bd5\u573a\u666f<\/h4>\n

\u4f7f\u7528 BERT Large \u63a8\u7406\u4efb\u52a1 \u4e0e ResNet50 \u8bad\u7ec3 \u4e24\u7c7b\u4ee3\u8868\u6027\u8d1f\u8f7d\u3002<\/p>\n

BERT \u63a8\u7406\u57fa\u51c6<\/h5>\n\n\u914d\u7f6eLatency (ms)Power (W)GFLOPS\/W<\/tr>\n\n\n\n\n
\u9ed8\u8ba4\u529f\u8017\/\u9891\u7387<\/td>\n12.2<\/td>\n380<\/td>\n51.6<\/td>\n<\/tr>\n
Power Limit 300W + AMP FP16<\/td>\n13.5<\/td>\n285<\/td>\n67.9<\/td>\n<\/tr>\n
Limit 300W + AMP + SM Tweak<\/td>\n13.8<\/td>\n270<\/td>\n69.1<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
ResNet50 \u8bad\u7ec3<\/h5>\n\n\u914d\u7f6eIter\/sPower (W)GFLOPS\/W<\/tr>\n\n\n\n\n
\u9ed8\u8ba4<\/td>\n1.42<\/td>\n390<\/td>\n52.3<\/td>\n<\/tr>\n
300W Power Limit + FP16<\/td>\n1.37<\/td>\n290<\/td>\n64.9<\/td>\n<\/tr>\n
+ SM Clock Tuning<\/td>\n1.35<\/td>\n275<\/td>\n66.9<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\n

\u4e03\u3001H100 \u5bf9\u6bd4\u6d4b\u8bd5<\/h3>\n

\u4e3a\u4e86\u9a8c\u8bc1\u4e0b\u4e00\u4ee3 GPU \u5728\u80fd\u6548\u4f18\u5316\u4e0a\u7684\u63d0\u5347，\u6211\u4eec\u505a\u4e86\u7b80\u8981\u5bf9\u6bd4：<\/p>\n\nGPU ModelDefault Power (W)Tuned Power (W)Tuned GFLOPS\/W<\/tr>\n\n\n\n
A100 80GB<\/td>\n390<\/td>\n280<\/td>\n69<\/td>\n<\/tr>\n
H100 80GB<\/td>\n450<\/td>\n320<\/td>\n85<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u7ed3\u8bba： H100 \u9ed8\u8ba4\u4e0e\u8c03\u4f18\u540e\u90fd\u5c55\u793a\u66f4\u9ad8\u7684\u80fd\u6548\u6bd4，\u9002\u7528\u4e8e\u5bf9\u6548\u7387\u8981\u6c42\u6781\u7aef\u573a\u666f\u3002<\/p>\n

\n

\u516b\u3001\u5b9e\u8df5\u5efa\u8bae\u4e0e\u6ce8\u610f\u4e8b\u9879<\/h3>\n
  • \u529f\u8017\u4e0a\u9650\u9700\u5faa\u5e8f\u8c03\u8bd5：\u8fc7\u5ea6\u964d\u4f4e\u529f\u7387\u53ef\u80fd\u5bfc\u81f4\u6838\u5fc3\u9891\u7387\u964d\u7ea7，\u5f15\u53d1\u6027\u80fd\u5d29\u584c\u3002<\/li>\n
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    \n

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

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