{"id":426,"date":"2020-02-20T03:44:13","date_gmt":"2020-02-19T18:44:13","guid":{"rendered":"https:\/\/wp.study3.biz\/?p=426"},"modified":"2020-02-20T03:45:08","modified_gmt":"2020-02-19T18:45:08","slug":"ubuntu18-04-1-titan-vx2-cuda-10-0-samples-nbody-benchmark%e3%82%92gpux2%e5%8f%b0%e5%90%88%e7%ae%97%e3%81%a7%e5%8b%95%e4%bd%9c%e3%81%95%e3%81%9b%e3%81%a6%e3%81%bf%e3%81%9f%e5%80%8d%e7%b2%be%e5%ba%a6704","status":"publish","type":"post","link":"https:\/\/wp.study3.biz\/?p=426","title":{"rendered":"Ubuntu18.04.1 TITAN Vx2 CUDA 10.0 Samples nbody benchmark\u3092GPUx2\u53f0\u5408\u7b97\u3067\u52d5\u4f5c\u3055\u305b\u3066\u307f\u305f\u500d\u7cbe\u5ea67049.009 GFLOP\/s \u5358\u7cbe\u5ea617130.392 GFLOP\/s"},"content":{"rendered":"<p>chibi@1804:~$ cat \/etc\/os-release<br \/>\nNAME=&#8221;Ubuntu&#8221;<br \/>\nVERSION=&#8221;18.04.1 LTS (Bionic Beaver)&#8221;<br \/>\nID=ubuntu<br \/>\nID_LIKE=debian<br \/>\nPRETTY_NAME=&#8221;Ubuntu 18.04.1 LTS&#8221;<br \/>\nVERSION_ID=&#8221;18.04&#8243;<br \/>\nHOME_URL=&#8221;https:\/\/www.ubuntu.com\/&#8221;<br \/>\nSUPPORT_URL=&#8221;https:\/\/help.ubuntu.com\/&#8221;<br \/>\nBUG_REPORT_URL=&#8221;https:\/\/bugs.launchpad.net\/ubuntu\/&#8221;<br \/>\nPRIVACY_POLICY_URL=&#8221;https:\/\/www.ubuntu.com\/legal\/terms-and-policies\/privacy-policy&#8221;<br \/>\nVERSION_CODENAME=bionic<br \/>\nUBUNTU_CODENAME=bionic<br \/>\nchibi@1804:~$ nvcc -V<br \/>\nnvcc: NVIDIA (R) Cuda compiler driver<br \/>\nCopyright (c) 2005-2018 NVIDIA Corporation<br \/>\nBuilt on Sat_Aug_25_21:08:01_CDT_2018<br \/>\nCuda compilation tools, release 10.0, V10.0.130<br \/>\nchibi@1804:~$ sudo hddtemp \/dev\/sda<br \/>\n[sudo] chibi \u306e\u30d1\u30b9\u30ef\u30fc\u30c9:<br \/>\n\/dev\/sda: TS128GSSD370S: 19\u00b0C<br \/>\nchibi@1804:~\/NVIDIA_CUDA-10.0_Samples\/5_Simulations\/nbody$ .\/nbody -benchmark -fp64 -numbodies=2<br \/>\n56000 -numdevices=2<br \/>\nRun &#8220;nbody -benchmark [-numbodies=&lt;numBodies&gt;]&#8221; to measure performance.<br \/>\n-fullscreen (run n-body simulation in fullscreen mode)<br \/>\n-fp64 (use double precision floating point values for simulation)<br \/>\n-hostmem (stores simulation data in host memory)<br \/>\n-benchmark (run benchmark to measure performance)<br \/>\n-numbodies=&lt;N&gt; (number of bodies (&gt;= 1) to run in simulation)<br \/>\n-device=&lt;d&gt; (where d=0,1,2&#8230;. for the CUDA device to use)<br \/>\n-numdevices=&lt;i&gt; (where i=(number of CUDA devices &gt; 0) to use for simulation)<br \/>\n-compare (compares simulation results running once on the default GPU and once on the CPU)<br \/>\n-cpu (run n-body simulation on the CPU)<br \/>\n-tipsy=&lt;file.bin&gt; (load a tipsy model file for simulation)<\/p>\n<p>NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.<\/p>\n<p>number of CUDA devices = 2<br \/>\n&gt; Windowed mode<br \/>\n&gt; Simulation data stored in system memory<br \/>\n&gt; Double precision floating point simulation<br \/>\n&gt; 2 Devices used for simulation<br \/>\nGPU Device 0: &#8220;TITAN V&#8221; with compute capability 7.0<\/p>\n<p>&gt; Compute 7.0 CUDA device: [TITAN V]<br \/>\n&gt; Compute 7.0 CUDA device: [TITAN V]<br \/>\nnumber of bodies = 256000<br \/>\n256000 bodies, total time for 10 iterations: 2789.158 ms<br \/>\n= 234.967 billion interactions per second<br \/>\n<strong>= 7049.009 double-precision GFLOP\/s at 30 flops per interaction<\/strong><br \/>\nchibi@1804:~\/NVIDIA_CUDA-10.0_Samples\/5_Simulations\/nbody$ .\/nbody -benchmark -numbodies=256000 -numdevices=2<br \/>\nRun &#8220;nbody -benchmark [-numbodies=&lt;numBodies&gt;]&#8221; to measure performance.<br \/>\n-fullscreen (run n-body simulation in fullscreen mode)<br \/>\n-fp64 (use double precision floating point values for simulation)<br \/>\n-hostmem (stores simulation data in host memory)<br \/>\n-benchmark (run benchmark to measure performance)<br \/>\n-numbodies=&lt;N&gt; (number of bodies (&gt;= 1) to run in simulation)<br \/>\n-device=&lt;d&gt; (where d=0,1,2&#8230;. for the CUDA device to use)<br \/>\n-numdevices=&lt;i&gt; (where i=(number of CUDA devices &gt; 0) to use for simulation)<br \/>\n-compare (compares simulation results running once on the default GPU and once on the CPU)<br \/>\n-cpu (run n-body simulation on the CPU)<br \/>\n-tipsy=&lt;file.bin&gt; (load a tipsy model file for simulation)<\/p>\n<p>NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.<\/p>\n<p>number of CUDA devices = 2<br \/>\n&gt; Windowed mode<br \/>\n&gt; Simulation data stored in system memory<br \/>\n&gt; Single precision floating point simulation<br \/>\n&gt; 2 Devices used for simulation<br \/>\nGPU Device 0: &#8220;TITAN V&#8221; with compute capability 7.0<\/p>\n<p>&gt; Compute 7.0 CUDA device: [TITAN V]<br \/>\n&gt; Compute 7.0 CUDA device: [TITAN V]<br \/>\nnumber of bodies = 256000<br \/>\n256000 bodies, total time for 10 iterations: 765.143 ms<br \/>\n= 856.520 billion interactions per second<br \/>\n<strong>= 17130.392 single-precision GFLOP\/s at 20 flops per interaction<\/strong><\/p>\n<p>\u30c7\u30fc\u30bf\u8a73\u7d30 <a href=\"https:\/\/wp.study3.biz\/wp-content\/uploads\/2020\/02\/Ubuntu18.04.1-TITAN-Vx2-CUDA-10.0-Samples-nbody-benchmark-\u500d\u7cbe\u5ea67049.009-GFLOP-s-\u5358\u7cbe\u5ea617130.392-GFLOP-s.txt\">Ubuntu18.04.1 TITAN Vx2 CUDA 10.0 Samples nbody benchmark \u500d\u7cbe\u5ea67049.009 GFLOP s \u5358\u7cbe\u5ea617130.392 GFLOP s<\/a><\/p>\n<p>GPU\u6e29\u5ea6\u63a8\u79fb <a href=\"https:\/\/wp.study3.biz\/wp-content\/uploads\/2020\/02\/Ubuntu18.04.1-TITAN-Vx2-CUDA-10.0-Samples-nbody-benchmark-\u500d\u7cbe\u5ea67049.009-GFLOP-s-\u5358\u7cbe\u5ea617130.392-GFLOP-s-nvidia-smi-sensors.txt\">Ubuntu18.04.1 TITAN Vx2 CUDA 10.0 Samples nbody benchmark \u500d\u7cbe\u5ea67049.009 GFLOP s \u5358\u7cbe\u5ea617130.392 GFLOP s nvidia-smi sensors<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>chibi@1804:~$ cat \/etc\/os-release NAME=&#8221;Ubuntu&#8221; VERSION=&#8221;18.04.1 LTS (Bionic Beaver)&#8221;  &hellip; <a href=\"https:\/\/wp.study3.biz\/?p=426\">\u7d9a\u304d\u3092\u8aad\u3080 <span class=\"meta-nav\">&rarr;<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[18,17],"tags":[],"class_list":["post-426","post","type-post","status-publish","format-standard","hentry","category-nvidia","category-ubuntu"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/wp.study3.biz\/index.php?rest_route=\/wp\/v2\/posts\/426","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wp.study3.biz\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wp.study3.biz\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wp.study3.biz\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/wp.study3.biz\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=426"}],"version-history":[{"count":4,"href":"https:\/\/wp.study3.biz\/index.php?rest_route=\/wp\/v2\/posts\/426\/revisions"}],"predecessor-version":[{"id":442,"href":"https:\/\/wp.study3.biz\/index.php?rest_route=\/wp\/v2\/posts\/426\/revisions\/442"}],"wp:attachment":[{"href":"https:\/\/wp.study3.biz\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=426"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wp.study3.biz\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=426"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wp.study3.biz\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=426"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}