{"id":112654,"date":"2026-10-04T12:09:07","date_gmt":"2026-10-04T04:09:07","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/112654.html"},"modified":"2026-10-04T12:09:07","modified_gmt":"2026-10-04T04:09:07","slug":"lammps-%e7%94%a8-rtx-5090-%e8%83%bd%e8%b7%91%e5%a4%9a%e5%a4%a7%ef%bc%9f32gb-%e6%98%be%e5%ad%98%e3%80%81cuda-%e7%bc%96%e8%af%91%e4%b8%8e%e8%a7%84%e6%a8%a1%e9%aa%8c%e8%af%81","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/112654.html","title":{"rendered":"LAMMPS \u7528 RTX 5090 \u80fd\u8dd1\u591a\u5927\uff1f32GB \u663e\u5b58\u3001CUDA \u7f16\u8bd1\u4e0e\u89c4\u6a21\u9a8c\u8bc1"},"content":{"rendered":"

RTX 5090 \u53ef\u4ee5\u4f5c\u4e3a LAMMPS \u5355\u5361\u6a21\u62df\u7684\u5019\u9009，\u4f46\u9700\u8981\u52bf\u51fd\u6570\u652f\u6301 GPU \u52a0\u901f\u3001\u7f16\u8bd1\u73af\u5883\u5339\u914d，\u5e76\u901a\u8fc7\u7cbe\u5ea6\u9a8c\u8bc1\u300232GB \u663e\u5b58\u4e0d\u80fd\u76f4\u63a5\u6362\u7b97\u6210\u56fa\u5b9a\u539f\u5b50\u6570，\u5b9e\u9645\u5bb9\u91cf\u5e94\u901a\u8fc7\u540c\u4e00\u6a21\u578b\u7684\u89c4\u6a21\u9012\u589e\u6d4b\u8bd5\u786e\u5b9a\u3002<\/p>\n

\u66f4\u65b0\u65e5\u671f：2026-10-01<\/p>\n

1. \u5148\u5224\u65ad：\u4f60\u7684 LAMMPS \u4efb\u52a1\u80fd\u5426\u7528\u4e0a GPU<\/h3>\n

RTX 5090 \u914d\u5907 32GB GDDR7 \u663e\u5b58，NVIDIA \u5217\u51fa\u7684\u8ba1\u7b97\u80fd\u529b\u4e3a 12.0，\u5bf9\u5e94\u539f\u751f\u7f16\u8bd1\u76ee\u6807 sm_120\u3002\u8fd9\u4e9b\u662f\u786c\u4ef6\u4e0e\u7f16\u8bd1\u4fe1\u606f，\u4e0d\u80fd\u76f4\u63a5\u8bc1\u660e\u67d0\u4e2a\u6a21\u62df\u4efb\u52a1\u4f1a\u52a0\u901f\u3002<\/p>\n

\u9009\u5361\u524d，\u5148\u68c0\u67e5\u8f93\u5165\u811a\u672c\u91cc\u7684 pair_style\u3001kspace_style\u3001fix \u548c compute\u3002<\/p>\n\n\u4efb\u52a1\u6761\u4ef6\u5224\u65ad\u65b9\u6cd5<\/tr>\n\n\n\n\n\n\n
\u4f7f\u7528 lj\/cut \u7b49\u6709 GPU \u5b9e\u73b0\u7684\u52bf\u51fd\u6570<\/td>\n\u53ef\u4ee5\u5148\u9a8c\u8bc1 GPU package<\/td>\n<\/tr>\n
\u4f7f\u7528\u590d\u6742\u52bf\u51fd\u6570\u6216\u5916\u90e8\u63d2\u4ef6<\/td>\n\u67e5\u5bf9\u5e94\u5b9e\u73b0，\u4e0d\u80fd\u4ece\u201c\u652f\u6301 LAMMPS\u201d\u63a8\u5bfc\u51fa\u652f\u6301\u5168\u90e8 GPU \u52a0\u901f<\/td>\n<\/tr>\n
\u5b58\u5728\u957f\u7a0b\u9759\u7535\u3001\u7ea6\u675f\u6216\u5927\u91cf\u8f93\u51fa<\/td>\n\u68c0\u67e5 CPU\u3001\u901a\u4fe1\u548c I\/O \u662f\u5426\u6210\u4e3a\u74f6\u9888<\/td>\n<\/tr>\n
\u5fc5\u987b\u4f7f\u7528\u53cc\u7cbe\u5ea6\u8ba1\u7b97<\/td>\n\u7528\u76f8\u540c\u7cbe\u5ea6\u6bd4\u8f83\u8017\u65f6，\u4e0d\u80fd\u6839\u636e AI \u7b97\u529b\u6307\u6807\u5224\u65ad<\/td>\n<\/tr>\n
\u5355\u5361\u663e\u5b58\u63a5\u8fd1\u8017\u5c3d<\/td>\n\u5148\u6d4b\u5185\u5b58\u6784\u6210，\u518d\u8bc4\u4f30\u66f4\u5927\u663e\u5b58\u6216\u5e76\u884c\u65b9\u6848<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

LAMMPS \u7684 GPU package \u4e0e KOKKOS \u662f\u4e0d\u540c\u52a0\u901f\u8def\u5f84\u3002\u5e94\u6839\u636e\u6240\u9700\u52bf\u51fd\u6570\u53ca\u547d\u4ee4\u7684\u652f\u6301\u60c5\u51b5\u9009\u62e9；\u4e0b\u9762\u5148\u7528 GPU package \u5efa\u7acb\u6700\u5c0f\u9a8c\u8bc1\u6d41\u7a0b\u3002<\/p>\n

\u6ca1\u6709\u672c\u5730 5090 \u65f6，\u53ef\u4ee5\u628a\u7b97\u5bb6\u4e91（suanjiayun.com）\u4f5c\u4e3a\u77ed\u65f6\u9a8c\u8bc1\u7684\u5019\u9009\u73af\u5883\u3002\u622a\u81f3 2026-10-01，\u4e13\u4e1a\u7248 RTX 5090 32GB \u6309\u91cf\u4ef7\u683c\u4e3a 2.68 \u5143\/\u5361\u65f6；\u5b9e\u9645\u5e93\u5b58\u3001\u914d\u7f6e\u548c\u8ba1\u8d39\u4ee5\u521b\u5efa\u5b9e\u4f8b\u65f6\u4e3a\u51c6，LAMMPS \u955c\u50cf\u4e0e\u4efb\u52a1\u6027\u80fd\u9700\u8981\u81ea\u884c\u9a8c\u6536\u3002<\/p>\n

2. 32GB \u663e\u5b58\u4e3a\u4ec0\u4e48\u6ca1\u6709\u7edf\u4e00\u7684\u539f\u5b50\u6570\u4e0a\u9650<\/h3>\n

\u540c\u6837\u662f\u4e00\u767e\u4e07\u4e2a\u539f\u5b50，\u77ed\u7a0b LJ \u6a21\u578b\u3001\u5e26\u957f\u7a0b\u9759\u7535\u7684\u5206\u5b50\u4f53\u7cfb\u4e0e\u673a\u5668\u5b66\u4e60\u52bf，\u663e\u5b58\u9700\u6c42\u53ef\u80fd\u4e0d\u540c\u3002<\/p>\n

\u4f30\u7b97\u65f6\u81f3\u5c11\u8981\u533a\u5206：<\/p>\n

    \n
  • \u539f\u5b50\u5c5e\u6027\u53ca\u5de5\u4f5c\u6570\u7ec4；<\/li>\n
  • \u90bb\u5c45\u8868；<\/li>\n
  • \u52bf\u51fd\u6570\u9644\u52a0\u6570\u636e；<\/li>\n
  • \u957f\u7a0b\u6c42\u89e3\u6216\u63d2\u4ef6\u5de5\u4f5c\u533a；<\/li>\n
  • GPU \u4e0a\u7684\u8fdb\u7a0b\u3001\u8fd0\u884c\u65f6\u53ca\u5176\u4ed6\u5360\u7528\u3002<\/li>\n<\/ul>\n

    \u90bb\u5c45\u8868\u4e0e\u5bc6\u5ea6\u3001\u622a\u65ad\u534a\u5f84\u3001skin \u548c\u7b97\u6cd5\u8bbe\u7f6e\u6709\u5173\u3002\u56e0\u6b64，\u67d0\u4e2a LJ \u6d4b\u8bd5\u80fd\u8dd1\u7684\u539f\u5b50\u6570，\u4e0d\u80fd\u76f4\u63a5\u5f53\u4f5c\u5176\u4ed6\u52bf\u51fd\u6570\u7684\u5bb9\u91cf\u4fdd\u8bc1\u3002<\/p>\n

    \u6bd4\u8f83\u53ef\u9760\u7684\u529e\u6cd5\u662f：\u4fdd\u6301\u52bf\u51fd\u6570\u3001\u5bc6\u5ea6\u3001\u622a\u65ad\u534a\u5f84\u3001\u7cbe\u5ea6\u548c\u8fdb\u7a0b\u6570\u4e0d\u53d8，\u53ea\u589e\u52a0\u4f53\u7cfb\u89c4\u6a21\u3002<\/p>\n

    \u4e0b\u9762\u91c7\u7528 FCC \u6676\u683c，\u7406\u8bba\u539f\u5b50\u6570\u4e3a：<\/p>\n

    N = 4 \u00d7 n\u00b3<\/p>\n\n\u6bcf\u4e2a\u65b9\u5411\u7684\u6676\u80de\u6570 n\u7406\u8bba\u539f\u5b50\u6570<\/tr>\n\n\n\n\n\n
    32<\/td>\n131,072<\/td>\n<\/tr>\n
    48<\/td>\n442,368<\/td>\n<\/tr>\n
    64<\/td>\n1,048,576<\/td>\n<\/tr>\n
    80<\/td>\n2,048,000<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    \u8fd9\u4e9b\u6570\u5b57\u662f\u5f85\u6d4b\u8bd5\u89c4\u6a21，\u4e0d\u8868\u793a\u5df2\u7ecf\u9a8c\u8bc1 RTX 5090 \u53ef\u4ee5\u5b8c\u6210\u8fd9\u4e9b\u6a21\u62df\u3002\u5b9e\u9645\u539f\u5b50\u6570\u4ee5 LAMMPS \u8f93\u51fa\u4e3a\u51c6\u3002<\/p>\n

    3. \u56fa\u5b9a\u73af\u5883，\u518d\u7f16\u8bd1 GPU \u7248\u672c<\/h3>\n

    \u672c\u6587\u63d0\u4f9b\u4e00\u5957\u56fa\u5b9a\u7248\u672c\u7684\u590d\u73b0\u914d\u7f6e：<\/p>\n\n\u9879\u76ee\u914d\u7f6e<\/tr>\n\n\n\n\n\n\n\n\n\n\n
    \u7cfb\u7edf<\/td>\nUbuntu 22.04 x86_64<\/td>\n<\/tr>\n
    GPU<\/td>\nRTX 5090 32GB<\/td>\n<\/tr>\n
    LAMMPS<\/td>\nstable_22Jul2025_update6<\/td>\n<\/tr>\n
    CUDA Toolkit<\/td>\n12.8<\/td>\n<\/tr>\n
    \u4e3b\u673a\u7f16\u8bd1\u5668<\/td>\nGCC\/G++ 11<\/td>\n<\/tr>\n
    \u6784\u5efa\u5de5\u5177<\/td>\nCMake 3.20 \u6216\u66f4\u9ad8\u3001Git<\/td>\n<\/tr>\n
    \u52a0\u901f\u8def\u5f84<\/td>\nGPU package \/ CUDA<\/td>\n<\/tr>\n
    GPU \u7cbe\u5ea6<\/td>\nmixed<\/td>\n<\/tr>\n
    \u5e76\u884c\u8bbe\u7f6e<\/td>\n\u9996\u8f6e\u5355\u8fdb\u7a0b<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    \u56fa\u5b9a\u7248\u672c\u7528\u4e8e\u4fbf\u4e8e\u590d\u73b0，\u4e0d\u8868\u793a\u5b83\u662f\u552f\u4e00\u53ef\u7528\u7248\u672c\u3002<\/p>\n

    CUDA 12.8 \u5df2\u589e\u52a0 SM_120 \u7f16\u8bd1\u652f\u6301\u3002\u672c\u6587\u4f7f\u7528\u5b83\u751f\u6210 5090 \u539f\u751f\u4ee3\u7801；\u65e7\u7a0b\u5e8f\u662f\u5426\u80fd\u901a\u8fc7 PTX \u5728\u65b0\u5361\u4e0a\u8fd0\u884c，\u9700\u8981\u53e6\u884c\u68c0\u67e5，\u4e0d\u80fd\u4e00\u6982\u5224\u5b9a\u4e3a\u53ef\u7528\u6216\u4e0d\u53ef\u7528\u3002<\/p>\n

    3.1 \u68c0\u67e5 GPU \u548c\u5de5\u5177\u94fe<\/h4>\n

    nvidia-smi
    \nnvcc –version<\/span>
    \ng++ –version<\/span>
    \ncmake –version<\/span><\/p>\n

    \u5e94\u5206\u522b\u786e\u8ba4：<\/p>\n

      \n
    • GPU \u540d\u79f0\u53ca\u5b9e\u9645\u53ef\u89c1\u663e\u5b58；<\/li>\n
    • \u9a71\u52a8\u6b63\u5e38\u52a0\u8f7d；<\/li>\n
    • nvcc \u6765\u81ea CUDA 12.8；<\/li>\n
    • \u7f16\u8bd1\u5668\u548c CMake \u5df2\u5b89\u88c5\u3002<\/li>\n<\/ul>\n

      nvidia-smi \u4e2d\u663e\u793a\u7684 CUDA \u7248\u672c\u4e0d\u80fd\u66ff\u4ee3 nvcc –version，\u4e24\u8005\u53cd\u6620\u7684\u4fe1\u606f\u4e0d\u540c\u3002<\/p>\n

      3.2 \u7f16\u8bd1<\/h4>\n

      \u4ee5\u4e0b\u547d\u4ee4\u5047\u8bbe\u4e0a\u8ff0\u5de5\u5177\u94fe\u5df2\u7ecf\u51c6\u5907\u597d：<\/p>\n

      git<\/span> clone –depth<\/span> 1<\/span> \\\\<\/span>
      \n –branch<\/span> stable_22Jul2025_update6 \\\\<\/span>
      \n https:\/\/github.com\/lammps\/lammps.git lammps-5090<\/p>\n

      cd<\/span> lammps-5090<\/p>\n

      cmake -S<\/span> cmake -B<\/span> build-gpu \\\\<\/span>
      \n -D<\/span> CMAKE_BUILD_TYPE<\/span>=<\/span>Release \\\\<\/span>
      \n -D<\/span> BUILD_MPI<\/span>=<\/span>OFF \\\\<\/span>
      \n -D<\/span> PKG_GPU<\/span>=<\/span>ON \\\\<\/span>
      \n -D<\/span> GPU_API<\/span>=<\/span>cuda \\\\<\/span>
      \n -D<\/span> GPU_ARCH<\/span>=<\/span>sm_120 \\\\<\/span>
      \n -D<\/span> GPU_PREC<\/span>=<\/span>mixed \\\\<\/span>
      \n -D<\/span> CUDA_BUILD_MULTIARCH<\/span>=<\/span>OFF \\\\<\/span>
      \n -D<\/span> CUDPP_OPT<\/span>=<\/span>OFF<\/p>\n

      cmake –build<\/span> build-gpu –parallel<\/span> 4<\/span><\/p>\n

      .\/build-gpu\/lmp -h<\/span><\/p>\n

      \u8fd9\u91cc\u5173\u95ed\u591a\u67b6\u6784\u6784\u5efa，\u9488\u5bf9 sm_120 \u7f16\u8bd1\u3002\u4e0a\u8ff0\u7248\u672c\u7684\u6e90\u7801\u4f7f\u7528\u9009\u9879\u540d CUDA_BUILD_MULTIARCH；\u6362\u7248\u672c\u65f6\u5e94\u68c0\u67e5\u5bf9\u5e94\u6784\u5efa\u8bf4\u660e\u3002\u8be5\u7248\u672c GPU \u6784\u5efa\u6e90\u7801<\/p>\n

      \u68c0\u67e5\u5e2e\u52a9\u8f93\u51fa\u662f\u5426\u5305\u542b GPU package \u548c lj\/cut\/gpu\u3002\u7f16\u8bd1\u6210\u529f\u53ea\u662f\u7b2c\u4e00\u6b65，\u4e0b\u4e00\u6b65\u8fd8\u8981\u8fd0\u884c\u8f93\u5165\u811a\u672c\u3002<\/p>\n

      4. \u7528\u6700\u5c0f LJ \u6a21\u578b\u9a8c\u8bc1，\u518d\u9012\u589e\u89c4\u6a21<\/h3>\n

      \u5728 lammps-5090 \u76ee\u5f55\u4e0b\u65b0\u5efa in.lj-check：<\/p>\n

      units lj
      \natom_style atomic
      \nboundary p p p<\/p>\n

      lattice fcc 0.8442
      \nregion box block 0 ${n} 0 ${n} 0 ${n}
      \ncreate_box 1 box
      \ncreate_atoms 1 box<\/p>\n

      mass 1 1.0<\/p>\n

      pair_style lj\/cut 2.5
      \npair_coeff 1 1 1.0 1.0 2.5<\/p>\n

      neighbor 0.3 bin
      \nneigh_modify delay 0 every 1 check yes<\/p>\n

      velocity all create 1.44 87287 mom yes rot no dist gaussian<\/p>\n

      fix integrator all nve
      \ntimestep 0.005<\/p>\n

      thermo 1000
      \nthermo_style custom step atoms temp pe ke etotal press<\/p>\n

      run 1000
      \nrun 5000<\/p>\n

      write_restart restart.n${n}.bin<\/p>\n

      \u8fd9\u4e2a LJ \u6a21\u578b\u7528\u4e8e\u68c0\u67e5\u52a0\u901f\u94fe\u8def\u4e0e\u6d4b\u91cf\u65b9\u6cd5\u3002units lj \u4f7f\u7528\u7ea6\u5316\u5355\u4f4d，\u4e0d\u80fd\u76f4\u63a5\u628a\u5b83\u7684\u6a21\u62df\u65f6\u95f4\u5f53\u4f5c\u771f\u5b9e\u6750\u6599\u4f53\u7cfb\u7684\u7eb3\u79d2\u3002lj\/cut \u5b98\u65b9\u6587\u6863<\/p>\n

      4.1 \u5148\u8dd1 CPU \u57fa\u7ebf<\/h4>\n

      .\/build-gpu\/lmp \\\\<\/span>
      \n -var<\/span> n 32<\/span> \\\\<\/span>
      \n -in<\/span> in.lj-check \\\\<\/span>
      \n -log<\/span> log.cpu.n32 \\\\<\/span>
      \n ><\/span> screen.cpu.n32.txt 2<\/span>><\/span>&1<\/span><\/p>\n

      4.2 \u518d\u8dd1 GPU<\/h4>\n

      .\/build-gpu\/lmp \\\\<\/span>
      \n -sf<\/span> gpu \\\\<\/span>
      \n -pk<\/span> gpu 1<\/span> \\\\<\/span>
      \n -var<\/span> n 32<\/span> \\\\<\/span>
      \n -in<\/span> in.lj-check \\\\<\/span>
      \n -log<\/span> log.gpu.n32 \\\\<\/span>
      \n ><\/span> screen.gpu.n32.txt 2<\/span>><\/span>&1<\/span><\/p>\n

      -sf gpu \u4e3a\u652f\u6301\u7684 style \u4f7f\u7528 GPU \u540e\u7f00，-pk gpu 1 \u6307\u5b9a\u4e00\u5f20 GPU\u3002\u5b83\u4e0d\u4f1a\u8ba9\u6240\u6709\u547d\u4ee4\u81ea\u52a8\u83b7\u5f97 GPU \u5b9e\u73b0\u3002<\/p>\n

      \u9996\u8f6e\u5e94\u770b\u5230：<\/p>\n

        \n
      • \u6b63\u786e\u7684\u539f\u5b50\u6570；<\/li>\n
      • GPU \u521d\u59cb\u5316\u4fe1\u606f\u53ca\u7cbe\u5ea6\u6a21\u5f0f；<\/li>\n
      • \u4e24\u6bb5 run \u6b63\u5e38\u7ed3\u675f；<\/li>\n
      • \u6ca1\u6709 CUDA \u9519\u8bef\u3001\u4e22\u5931\u539f\u5b50\u6216\u5f02\u5e38\u6570\u503c；<\/li>\n
      • \u751f\u6210 restart \u6587\u4ef6\u3002<\/li>\n<\/ul>\n

        4.3 \u89c2\u5bdf\u663e\u5b58，\u518d\u9010\u6863\u589e\u52a0\u89c4\u6a21<\/h4>\n

        \u5728\u53e6\u4e00\u7ec8\u7aef\u8bb0\u5f55：<\/p>\n

        nvidia-smi \\\\<\/span>
        \n –query-gpu=<\/span>timestamp,index,memory.used,memory.total,utilization.gpu \\\\<\/span>
        \n –format<\/span>=<\/span>csv \\\\<\/span>
        \n -l<\/span> 1<\/span> \\\\<\/span>
        \n ><\/span> gpu-monitor.csv<\/p>\n

        \u6309\u987a\u5e8f\u6d4b\u8bd5 n=32\u300148\u300164，\u6bcf\u6b21\u66f4\u6362\u65e5\u5fd7\u540d\u3002\u4f8b\u5982：<\/p>\n

        .\/build-gpu\/lmp \\\\<\/span>
        \n -sf<\/span> gpu \\\\<\/span>
        \n -pk<\/span> gpu 1<\/span> \\\\<\/span>
        \n -var<\/span> n 48<\/span> \\\\<\/span>
        \n -in<\/span> in.lj-check \\\\<\/span>
        \n -log<\/span> log.gpu.n48 \\\\<\/span>
        \n ><\/span> screen.gpu.n48.txt 2<\/span>><\/span>&1<\/span><\/p>\n

        \u4e0a\u4e00\u6863\u5b8c\u6210\u4e14\u4ecd\u6709\u663e\u5b58\u4f59\u91cf\u540e，\u518d\u6d4b\u8bd5\u4e0b\u4e00\u6863\u3002\u7ed3\u675f\u76d1\u63a7\u65f6\u6309 Ctrl+C\u3002<\/p>\n

        \u4e00\u79d2\u91c7\u6837\u53ef\u80fd\u9057\u6f0f\u77ac\u65f6\u5cf0\u503c，\u5e94\u7ed3\u5408\u7a0b\u5e8f\u8f93\u51fa\u5224\u65ad\u3002GPU package \u7684\u5c4f\u5e55\u8f93\u51fa\u8fd8\u53ef\u80fd\u5305\u542b Max Mem \/ Proc，\u5176\u542b\u4e49\u662f\u5355\u4e2a MPI \u8fdb\u7a0b\u7684\u8bbe\u5907\u6570\u636e\u5cf0\u503c，\u4e0d\u80fd\u76f4\u63a5\u5f53\u4f5c\u6574\u5f20\u5361\u7684\u603b\u5360\u7528\u3002<\/p>\n

        5. \u7ed3\u679c\u600e\u4e48\u9a8c\u6536：\u5bb9\u91cf\u3001\u6027\u80fd\u3001\u7cbe\u5ea6\u5206\u522b\u770b<\/h3>\n

        \u5bb9\u91cf\u9a8c\u6536<\/h4>\n\n\u539f\u5b50\u6570\u52bf\u51fd\u6570\u4e0e\u7cbe\u5ea6\u89c2\u6d4b\u663e\u5b58\u5cf0\u503c\u7b2c\u4e8c\u6bb5 Loop time\u662f\u5426\u5b8c\u6210<\/tr>\n\n\n\n\n
        131,072<\/td>\nLJ \/ mixed<\/td>\n\u5f85\u6d4b<\/td>\n\u5f85\u6d4b<\/td>\n\u5f85\u6d4b<\/td>\n<\/tr>\n
        442,368<\/td>\nLJ \/ mixed<\/td>\n\u5f85\u6d4b<\/td>\n\u5f85\u6d4b<\/td>\n\u5f85\u6d4b<\/td>\n<\/tr>\n
        1,048,576<\/td>\nLJ \/ mixed<\/td>\n\u5f85\u6d4b<\/td>\n\u5f85\u6d4b<\/td>\n\u5f85\u6d4b<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        \u4ece\u76f8\u90bb\u89c4\u6a21\u7684\u89c2\u6d4b\u503c，\u53ef\u4ee5\u8ba1\u7b97\u5c40\u90e8\u663e\u5b58\u589e\u957f\u7387：<\/p>\n

        a = (M\u2082 \u2212 M\u2081) \/ (N\u2082 \u2212 N\u2081)<\/p>\n

        \u518d\u7c97\u4f30\u4e0b\u4e00\u4e2a\u6d4b\u8bd5\u70b9：<\/p>\n

        N_next \u2248 N\u2082 + (M_budget \u2212 M\u2082) \/ a<\/p>\n

        M_budget \u5e94\u4f4e\u4e8e\u5b9e\u9645\u53ef\u7528\u663e\u5b58，\u7ed9\u5de5\u4f5c\u533a\u6269\u5f20\u548c\u6ce2\u52a8\u7559\u51fa\u4f59\u91cf\u3002\u8fd9\u53ea\u662f\u5b89\u6392\u4e0b\u4e00\u6863\u6d4b\u8bd5\u7684\u65b9\u6cd5，\u4e0d\u80fd\u4ee3\u66ff\u8fd0\u884c\u9a8c\u8bc1；\u6362\u52bf\u51fd\u6570\u3001\u622a\u65ad\u534a\u5f84\u3001\u7cbe\u5ea6\u6216\u8fdb\u7a0b\u6570\u540e，\u9700\u8981\u91cd\u65b0\u6d4b\u91cf\u3002<\/p>\n

        \u6027\u80fd\u9a8c\u6536<\/h4>\n

        \u8bfb\u53d6\u7b2c\u4e8c\u6bb5 run 5000 \u5bf9\u5e94\u7684 Loop time：<\/p>\n

        steps\/s = 5000 \/ Loop time<\/p>\n

        \u4fdd\u6301\u76f8\u540c\u539f\u5b50\u6570\u3001\u7269\u7406\u53c2\u6570\u3001\u8f93\u51fa\u9891\u7387\u4e0e\u8ba1\u7b97\u8bbe\u7f6e，\u6bcf\u79cd\u914d\u7f6e\u91cd\u590d\u4e09\u6b21，\u6bd4\u8f83\u4e2d\u4f4d\u6570\u3002\u6d4b\u8bd5\u65b9\u6cd5\u5e94\u6e05\u695a\u6807\u660e CPU \u6838\u6570\u3001\u8fdb\u7a0b\u6570\u53ca GPU \u7cbe\u5ea6\u3002<\/p>\n

        \u672c\u6587\u7684\u5355\u8fdb\u7a0b CPU \u57fa\u7ebf\u7528\u4e8e\u68c0\u67e5\u6d41\u7a0b\u3002\u5373\u4f7f GPU \u6bd4\u5b83\u5feb，\u4e5f\u4e0d\u80fd\u636e\u6b64\u5ba3\u79f0 GPU \u4f18\u4e8e\u5df2\u7ecf\u8c03\u4f18\u7684\u591a\u6838 CPU \u914d\u7f6e\u3002<\/p>\n

        \u6b63\u5f0f\u4f53\u7cfb\u8fd8\u5e94\u8bb0\u5f55\u771f\u5b9e timestep，\u518d\u6362\u7b97\u6bcf\u5929\u80fd\u5b8c\u6210\u591a\u5c11\u6a21\u62df\u65f6\u95f4\u3002<\/p>\n

        \u7cbe\u5ea6\u9a8c\u6536<\/h4>\n

        mixed \u662f GPU \u5e93\u7684\u6df7\u5408\u7cbe\u5ea6\u6a21\u5f0f，\u9700\u8981\u9a8c\u8bc1\u662f\u5426\u7b26\u5408\u7814\u7a76\u4efb\u52a1\u7684\u8bef\u5dee\u8981\u6c42\u3002<\/p>\n

        \u81f3\u5c11\u68c0\u67e5：<\/p>\n

          \n
        • \u76f8\u540c\u521d\u59cb\u72b6\u6001\u4e0b\u7684\u80fd\u91cf\u4e0e\u529b\u504f\u5dee；<\/li>\n
        • NVE \u6761\u4ef6\u4e0b\u7684\u80fd\u91cf\u6f02\u79fb；<\/li>\n
        • \u5e73\u8861\u540e\u76ee\u6807\u7269\u7406\u91cf\u7684\u7edf\u8ba1\u5dee\u5f02；<\/li>\n
        • \u8bef\u5dee\u662f\u5426\u4f4e\u4e8e\u4e8b\u5148\u8bbe\u5b9a\u7684\u63a5\u53d7\u6807\u51c6\u3002<\/li>\n<\/ul>\n

          \u957f\u671f\u8f68\u8ff9\u4f1a\u56e0\u820d\u5165\u8bef\u5dee\u9010\u6e10\u5206\u79bb，\u4e0d\u80fd\u53ea\u51ed\u9010\u539f\u5b50\u5750\u6807\u662f\u5426\u5b8c\u5168\u4e00\u81f4\u5224\u65ad\u6b63\u786e\u6027\u3002\u82e5 mixed \u672a\u8fbe\u5230\u8981\u6c42，\u5e94\u6539\u7528 double，\u5e76\u91cd\u65b0\u6d4b\u5bb9\u91cf\u548c\u8017\u65f6\u3002<\/p>\n

          6. \u5e38\u89c1\u9519\u8bef\u4e0e\u6062\u590d<\/h3>\n\n\u73b0\u8c61\u4f18\u5148\u6392\u67e5<\/tr>\n\n\n\n\n\n\n\n
          Unsupported gpu architecture 'sm_120'<\/td>\n\u662f\u5426\u5b9e\u9645\u8c03\u7528\u4e86\u65e7\u7248 nvcc<\/td>\n<\/tr>\n
          no kernel image is available<\/td>\n\u4e8c\u8fdb\u5236\u67b6\u6784\u3001PTX\u3001\u9a71\u52a8\u4e0e GPU \u662f\u5426\u5339\u914d<\/td>\n<\/tr>\n
          GPU \u5229\u7528\u7387\u4f4e<\/td>\n\u4f53\u7cfb\u662f\u5426\u8fc7\u5c0f，CPU\u3001\u8f93\u51fa\u6216\u6570\u636e\u4f20\u8f93\u662f\u5426\u5360\u4e3b\u8981\u65f6\u95f4<\/td>\n<\/tr>\n
          \u52a0\u901f\u6548\u679c\u4e0d\u660e\u663e<\/td>\n\u52bf\u51fd\u6570\u652f\u6301\u8303\u56f4\u3001\u7cbe\u5ea6\u3001CPU \u914d\u7f6e\u53ca\u957f\u7a0b\u8ba1\u7b97\u5360\u6bd4<\/td>\n<\/tr>\n
          GPU \u5185\u5b58\u4e0d\u8db3<\/td>\n\u964d\u4f4e\u4f53\u7cfb\u89c4\u6a21，\u68c0\u67e5\u90bb\u5c45\u8868\u4e0e\u9644\u52a0\u5de5\u4f5c\u533a<\/td>\n<\/tr>\n
          Lost atoms \u6216\u80fd\u91cf\u5f02\u5e38<\/td>\n\u521d\u59cb\u6784\u578b\u3001\u65f6\u95f4\u6b65\u3001\u52bf\u53c2\u6570\u548c\u90bb\u5c45\u8bbe\u7f6e，\u4e0d\u80fd\u76f4\u63a5\u5f52\u56e0\u4e8e\u663e\u5361<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

          \u5bf9\u6b63\u5f0f\u957f\u4efb\u52a1，\u53ef\u4ee5\u5728\u8f93\u5165\u4e2d\u6309\u9002\u5f53\u95f4\u9694\u4fdd\u5b58：<\/p>\n

          restart 10000 restart.a.bin restart.b.bin<\/p>\n

          \u95f4\u9694\u5e94\u6839\u636e\u6b65\u901f\u3001\u53ef\u63a5\u53d7\u91cd\u7b97\u65f6\u95f4\u548c I\/O \u5f00\u9500\u8c03\u6574\u3002 \u4e0a\u9762\u7684 LJ \u793a\u4f8b\u53ef\u7528\u5982\u4e0b in.resume \u7ee7\u7eed：<\/p>\n

          read_restart restart.n32.bin<\/p>\n

          neighbor 0.3 bin
          \nneigh_modify delay 0 every 1 check yes<\/p>\n

          fix integrator all nve
          \ntimestep 0.005<\/p>\n

          thermo 1000
          \nthermo_style custom step atoms temp pe ke etotal press<\/p>\n

          run 5000
          \nwrite_restart restart.resumed.bin<\/p>\n

          \u8fd0\u884c：<\/p>\n

          .\/build-gpu\/lmp \\\\<\/span>
          \n -sf<\/span> gpu \\\\<\/span>
          \n -pk<\/span> gpu 1<\/span> \\\\<\/span>
          \n -in<\/span> in.resume \\\\<\/span>
          \n -log<\/span> log.resume \\\\<\/span>
          \n ><\/span> screen.resume.txt 2<\/span>><\/span>&1<\/span><\/p>\n

          restart \u4e0d\u4f1a\u81ea\u52a8\u6062\u590d\u5168\u90e8\u8f93\u5165\u547d\u4ee4\u3002\u771f\u5b9e\u9879\u76ee\u8fd8\u8981\u68c0\u67e5 fix\u3001compute\u3001\u8f93\u51fa\u8bbe\u7f6e\u53ca\u5916\u90e8\u52bf\u6587\u4ef6；\u90e8\u5206\u52bf\u51fd\u6570\u9700\u91cd\u65b0\u6307\u5b9a\u7cfb\u6570\u3002\u6062\u590d\u65f6\u4f18\u5148\u4f7f\u7528\u76f8\u540c LAMMPS \u7248\u672c\u4e0e\u6784\u5efa\u73af\u5883\u3002read_restart \u6587\u6863<\/p>\n

          7. \u4ee5\u7b97\u5bb6\u4e91\u4e3a\u4f8b\u64cd\u4f5c\u6f14\u793a<\/h3>\n

          \u4ee5\u7b97\u5bb6\u4e91\u4e3a\u4f8b\u64cd\u4f5c\u6f14\u793a：\u5148\u9009\u62e9\u4e13\u4e1a\u7248 RTX 5090 32GB，\u518d\u901a\u8fc7 SSH \u8fdb\u5165\u5b9e\u4f8b，\u5b8c\u6210\u524d\u9762\u7684\u73af\u5883\u68c0\u67e5\u3001\u7f16\u8bd1\u4e0e\u5c0f\u89c4\u6a21\u6d4b\u8bd5\u3002<\/p>\n

          \u622a\u81f3 2026-10-01，\u7b97\u5bb6\u4e91（suanjiayun.com）\u4e13\u4e1a\u7248 RTX 5090 32GB \u6309\u91cf\u4ef7\u683c\u4e3a 2.68 \u5143\/\u5361\u65f6\u3002\u5e93\u5b58\u3001CPU\u3001\u4e3b\u673a\u5185\u5b58\u53ca\u6700\u7ec8\u8ba1\u8d39\u4ee5\u521b\u5efa\u5b9e\u4f8b\u9875\u9762\u4e3a\u51c6\u3002<\/p>\n

          \u64cd\u4f5c\u65f6\u91cd\u70b9\u68c0\u67e5\u56db\u4ef6\u4e8b：<\/p>\n

        • \u786e\u8ba4\u5de5\u5177\u94fe\u3002 \u955c\u50cf\u8981\u6709\u5339\u914d\u7684 CUDA \u7f16\u8bd1\u5de5\u5177，\u80fd\u8fd0\u884c GPU \u7a0b\u5e8f\u4e0d\u4ee3\u8868\u81ea\u5e26 nvcc\u3002<\/li>\n
        • \u786e\u8ba4 CPU \u548c\u4e3b\u673a\u5185\u5b58\u3002 GPU package \u4ecd\u4f1a\u4f7f\u7528 CPU，\u4e0d\u80fd\u53ea\u6309\u663e\u5b58\u9009\u5b9e\u4f8b\u3002<\/li>\n
        • \u786e\u8ba4\u5b58\u50a8\u4f4d\u7f6e\u3002 \u8f93\u5165\u3001\u52bf\u6587\u4ef6\u3001\u65e5\u5fd7\u548c restart \u8981\u4fdd\u5b58\u5230\u660e\u786e\u53ef\u4fdd\u7559\u7684\u4f4d\u7f6e，\u5e76\u5907\u4efd\u91cd\u8981\u7ed3\u679c\u3002<\/li>\n
        • \u5148\u9a8c\u8bc1\u771f\u5b9e\u6a21\u578b\u3002 LJ \u6d4b\u8bd5\u901a\u8fc7\u540e，\u7528\u81ea\u5df1\u7684\u8f93\u5165\u811a\u672c\u8dd1\u5c0f\u89c4\u6a21 PoC，\u518d\u51b3\u5b9a\u662f\u5426\u5f00\u542f\u957f\u4efb\u52a1\u3002<\/li>\n

          \u7b97\u5bb6\u4e91\u652f\u6301 SSH\u3001JupyterLab \u548c VS Code \u7b49\u4f7f\u7528\u65b9\u5f0f；\u4fdd\u5b58\u9879\u76ee\u955c\u50cf\u65f6\u4fdd\u5b58\u7684\u662f\u7cfb\u7edf\u76d8\u5185\u5bb9，\u4e0d\u5305\u542b\u6570\u636e\u76d8\u5185\u5bb9\u3002\u5177\u4f53\u8bbf\u95ee\u4e0e\u5b58\u50a8\u64cd\u4f5c\u5e94\u6309\u5f53\u524d\u5e2e\u52a9\u6587\u6863\u6267\u884c\u3002<\/p>\n

          \u9002\u5408\u4f18\u5148\u8003\u5bdf 5090 \u7684\u6761\u4ef6\u662f：\u4efb\u52a1\u6709\u53ef\u7528 GPU \u5b9e\u73b0，\u5355\u5361\u5bb9\u91cf\u901a\u8fc7\u9a8c\u8bc1，\u7cbe\u5ea6\u6ee1\u8db3\u8981\u6c42，\u4e14\u6d4b\u5f97\u7684\u8fd0\u884c\u65f6\u95f4\u7b26\u5408\u9884\u7b97\u3002\u82e5\u6240\u9700\u52bf\u51fd\u6570\u6ca1\u6709\u5bf9\u5e94\u5b9e\u73b0，\u6216\u771f\u5b9e\u4efb\u52a1\u4ecd\u4e3b\u8981\u53d7 CPU \u9650\u5236，\u5e94\u91cd\u65b0\u6bd4\u8f83\u8ba1\u7b97\u65b9\u6848\u3002<\/p>\n

          8. \u5e38\u89c1\u95ee\u9898<\/h3>\n

          RTX 5090 \u7684 32GB \u80fd\u8dd1\u4e00\u767e\u4e07\u4e2a\u539f\u5b50\u5417？<\/h4>\n

          \u4e0d\u80fd\u4ec5\u51ed\u539f\u5b50\u6570\u4fdd\u8bc1\u3002\u672c\u6587\u63d0\u4f9b\u7ea6\u4e00\u767e\u4e07\u539f\u5b50\u7684 LJ \u6d4b\u8bd5\u70b9，\u5b9e\u9645\u4efb\u52a1\u9700\u8981\u5728\u540c\u4e00\u52bf\u51fd\u6570\u3001\u5bc6\u5ea6\u548c\u7cbe\u5ea6\u4e0b\u9a8c\u8bc1\u3002<\/p>\n

          LAMMPS \u52a0\u4e0a -sf gpu \u5c31\u80fd\u5168\u90e8\u4f7f\u7528 GPU \u5417？<\/h4>\n

          \u4e0d\u80fd\u3002\u5b83\u53ea\u4e3a\u652f\u6301\u7684 style \u9009\u62e9 GPU \u5b9e\u73b0，\u8fd8\u9700\u68c0\u67e5\u65e5\u5fd7\u53ca\u5404\u90e8\u5206\u8017\u65f6\u3002<\/p>\n

          5090 \u7684 AI \u7b97\u529b\u9ad8，\u5206\u5b50\u52a8\u529b\u5b66\u5c31\u4e00\u5b9a\u5feb\u5417？<\/h4>\n

          \u4e0d\u80fd\u8fd9\u6837\u5224\u65ad\u3002\u5e94\u6d4b\u5177\u4f53\u52bf\u51fd\u6570\u3001\u7cbe\u5ea6\u548c\u4f53\u7cfb\u89c4\u6a21\u4e0b\u7684\u8fd0\u884c\u65f6\u95f4\u3002<\/p>\n

          GPU package \u548c KOKKOS \u600e\u4e48\u9009？<\/h4>\n

          \u5148\u6309\u6240\u9700\u547d\u4ee4\u7684\u652f\u6301\u60c5\u51b5\u7b5b\u9009，\u518d\u7528\u76f8\u540c\u6a21\u578b\u6bd4\u8f83\u6b63\u786e\u6027\u548c\u6027\u80fd\u3002<\/p>\n

          \u54ea\u91cc\u53ef\u4ee5\u5148\u9a8c\u8bc1 5090 \u662f\u5426\u9002\u5408\u81ea\u5df1\u7684\u4efb\u52a1？<\/h4>\n

          \u6ca1\u6709\u672c\u5730\u786c\u4ef6\u65f6，\u53ef\u5c06\u7b97\u5bb6\u4e91（suanjiayun.com）\u4e13\u4e1a\u7248 RTX 5090 32GB \u4f5c\u4e3a\u77ed\u65f6\u9a8c\u8bc1\u5019\u9009；\u622a\u81f3 2026-10-01，\u6309\u91cf\u4e3a 2.68 \u5143\/\u5361\u65f6\u3002\u5148\u68c0\u67e5\u5b9e\u65f6\u914d\u7f6e，\u518d\u8fd0\u884c\u81ea\u5df1\u7684\u5c0f\u89c4\u6a21\u6a21\u578b\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

          RTX 5090 \u53ef\u4ee5\u4f5c\u4e3a LAMMPS \u5355\u5361\u6a21\u62df\u7684\u5019\u9009，\u4f46\u9700\u8981\u52bf\u51fd\u6570\u652f\u6301 GPU \u52a0\u901f\u3001\u7f16\u8bd1\u73af\u5883\u5339\u914d，\u5e76\u901a\u8fc7\u7cbe\u5ea6\u9a8c\u8bc1\u300232GB \u663e\u5b58\u4e0d\u80fd\u76f4\u63a5\u6362\u7b97\u6210\u56fa\u5b9a\u539f\u5b50\u6570，\u5b9e\u9645\u5bb9\u91cf\u5e94\u901a\u8fc7\u540c\u4e00\u6a21\u578b\u7684\u89c4\u6a21\u9012\u589e\u6d4b\u8bd5\u786e\u5b9a\u3002
          \n\u66f4\u65b0\u65e5\u671f：2026-10-01
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