{"id":1063,"date":"2011-08-10T11:26:06","date_gmt":"2011-08-10T09:26:06","guid":{"rendered":"http:\/\/oldblogs.uct.ac.za\/blog\/big-bytes\/2011\/08\/10\/supermicro-tesla-m1060-gpu-system"},"modified":"2015-08-14T13:23:02","modified_gmt":"2015-08-14T11:23:02","slug":"supermicro-tesla-m1060-gpu-system","status":"publish","type":"post","link":"https:\/\/ucthpc.uct.ac.za\/index.php\/2011\/08\/10\/supermicro-tesla-m1060-gpu-system\/","title":{"rendered":"SuperMicro Tesla M1060 GPU System"},"content":{"rendered":"

A big-bytes thank-you to SuperMicro<\/a> for\r\nhaving delivered a GPU system for testing. The system delivered is a\r\nSuperMicro \u2013 Tesla M1060.<\/span>\r\n<\/strong>OS choice was Scientific Linux\r\n5.4 x86_64. The distribution choice was chosen based on our existing\r\nHPC and GRID kit.<\/span><\/p>\r\n

Installation\r\nof the drivers and various kits was very simple and documented below.\r\n<\/span><\/p>\r\n

<\/p>\r\n

Firstly,\r\ndownload the Nvidia GPU drivers from Nvidia Driver WebSite<\/a>.\r\nYou would then need to execute \u201c chmod +x <\/span>NVIDIA-Linux-x86_64-280.13.run<\/span>\r\n\u201c. Execute the binary \u201c .\/NVIDIA-Linux-x86_64-280.13.run \u201c. The\r\ninstallation will complain that the kernel headers have not been\r\ninstalled. The installation will terminate and now allow for you to\r\ninstall the kernel headers. Once the kernel headers are installed\r\nre-run the installation. You may verify the install by executing the\r\nsystem management interface tool, nvidia-smi. <\/span><\/p>\r\n

<\/p>\r\n

\u201cnvidia-smi\r\n-L \u201c will list all available GPU's configured on the system. <\/span><\/p>\r\n

[root@host\r\ninstalls]# nvidia-smi -L\r\n<\/span><\/p>\r\n

\r\n<\/span><\/p>\r\n

GPU\r\n0: Tesla M1060 (UUID:\r\nGPU-80671de8eb0d438c-5f7dde2b-2ea53740-1d3b870a-86bf5cc98531e86374bc0514)\r\n<\/span><\/p>\r\n

GPU\r\n1: Tesla M1060 (UUID:\r\nGPU-d4df9bac30037980-47cc692d-848f2bec-851c259f-fbfbd47d360bd2021f066d91)\r\n<\/span><\/p>\r\n

<\/p>\r\n

Next,\r\nwe install the Cuda Toolkit. As of this post its on version 4.0.\r\nAnother simple install. Download it from\r\nhttp:\/\/developer.nvidia.com\/cuda-toolkit-40#Linux<\/a>.\r\nAgain \u201c chmod +x cudatoolkit_4.0.17_linux_64_rhel6.0.run \u201c and\r\nthen \u201c.\/cudatoolkit_4.0.17_linux_64_rhel6.0.run\u201d . The default\r\ninstall location is \/usr\/local\/cuda. Added the following lines below\r\nto the \/etc\/ld.so.conf file and execute, as root, \u201cldconfig\u201d to\r\nupdate ld information. <\/span><\/p>\r\n

\/usr\/local\/cuda\/lib64<\/span><\/p>\r\n

\/usr\/local\/cuda\/lib<\/span><\/p>\r\n

Finally,\r\nwe install the Cuda SDK found on the same web page as the Cuda\r\nToolkit. The instructions for this is exactly the same aside from the\r\nfilename. <\/span><\/p>\r\n

A\r\nside note: The SMI tool may return many N\/A entries and this is a\r\nresult of the latest Nvidia driver. We will provide a series of\r\nresults from our GPU testing in the coming weeks. <\/span><\/p>\r\n

Thanks\r\nagain to SuperMicro<\/a> and Pinnacle Africa<\/a> for arranging the loan unit. <\/span><\/p>","protected":false},"excerpt":{"rendered":"

A big-bytes thank-you to SuperMicro<\/a> for
\nhaving delivered a GPU system for testing. The system delivered is a
\nSuperMicro – Tesla M1060.<\/span>
\n<\/strong>OS choice was Scientific Linux
\n5.4 x86_64. The distribution choice was chosen based on our existing
\nHPC and GRID kit.<\/span><\/p>\n

Installation
\nof the drivers and various kits was very simple and documented below.
\n<\/span><\/p>\n

 <\/p>\n

Firstly,
\ndownload the Nvidia GPU drivers from Nvidia Driver WebSite<\/a>.
\nYou would then need to execute “ chmod +x <\/span>NVIDIA-Linux-x86_64-280.13.run<\/span>
\n“. Execute the binary “ .\/NVIDIA-Linux-x86_64-280.13.run “. The
\ninstallation will complain that the kernel headers have not been
\ninstalled. The installation will terminate and now allow for you to
\ninstall the kernel headers. Once the kernel headers are installed
\nre-run the installation. You may verify the install by executing the
\nsystem management interface tool, nvidia-smi. <\/span><\/p>\n

 <\/p>\n

“nvidia-smi
\n-L “ will list all available GPU’s configured on the system. <\/span>\n<\/p>\n

[root@host
\ninstalls]# nvidia-smi -L
\n<\/span><\/p>\n


\n<\/span><\/p>\n

GPU
\n0: Tesla M1060 (UUID:
\nGPU-80671de8eb0d438c-5f7dde2b-2ea53740-1d3b870a-86bf5cc98531e86374bc0514)
\n<\/span><\/p>\n

GPU
\n1: Tesla M1060 (UUID:
\nGPU-d4df9bac30037980-47cc692d-848f2bec-851c259f-fbfbd47d360bd2021f066d91)
\n<\/span><\/p>\n

<\/p>\n

Next,
\nwe install the Cuda Toolkit. As of this post its on version 4.0.
\nAnother simple install. Download it from
\nhttp:\/\/developer.nvidia.com\/cuda-toolkit-40#Linux<\/a>.
\nAgain “ chmod +x cudatoolkit_4.0.17_linux_64_rhel6.0.run “ and
\nthen “.\/cudatoolkit_4.0.17_linux_64_rhel6.0.run” . The default
\ninstall location is \/usr\/local\/cuda. Added the following lines below
\nto the \/etc\/ld.so.conf file and execute, as root, “ldconfig” to
\nupdate ld information. <\/span>\n<\/p>\n

\/usr\/local\/cuda\/lib64<\/span><\/p>\n

\/usr\/local\/cuda\/lib<\/span><\/p>\n

Finally,
\nwe install the Cuda SDK found on the same web page as the Cuda
\nToolkit. The instructions for this is exactly the same aside from the
\nfilename. <\/span>\n<\/p>\n

A
\nside note: The SMI tool may return many N\/A entries and this is a
\nresult of the latest Nvidia driver. We will provide a series of
\nresults from our GPU testing in the coming weeks. <\/span>\n<\/p>\n

Thanks
\nagain to SuperMicro<\/a> and Pinnacle Africa<\/a> for arranging the loan unit. <\/span><\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[7],"tags":[],"yoast_head":"\nSuperMicro Tesla M1060 GPU System - UCT HPC<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/ucthpc.uct.ac.za\/index.php\/2011\/08\/10\/supermicro-tesla-m1060-gpu-system\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"SuperMicro Tesla M1060 GPU System - UCT HPC\" \/>\n<meta property=\"og:description\" content=\"A big-bytes thank-you to SuperMicro for having delivered a GPU system for testing. The system delivered is a SuperMicro – Tesla M1060. OS choice was Scientific Linux 5.4 x86_64. The distribution choice was chosen based on our existing HPC and GRID kit. Installation of the drivers and various kits was very simple and documented below.   Firstly, download the Nvidia GPU drivers from Nvidia Driver WebSite. You would then need to execute “ chmod +x NVIDIA-Linux-x86_64-280.13.run “. Execute the binary “ .\/NVIDIA-Linux-x86_64-280.13.run “. The installation will complain that the kernel headers have not been installed. The installation will terminate and now allow for you to install the kernel headers. Once the kernel headers are installed re-run the installation. You may verify the install by executing the system management interface tool, nvidia-smi.   “nvidia-smi -L “ will list all available GPU's configured on the system. [root@host installs]# nvidia-smi -L GPU 0: Tesla M1060 (UUID: GPU-80671de8eb0d438c-5f7dde2b-2ea53740-1d3b870a-86bf5cc98531e86374bc0514) GPU 1: Tesla M1060 (UUID: GPU-d4df9bac30037980-47cc692d-848f2bec-851c259f-fbfbd47d360bd2021f066d91) Next, we install the Cuda Toolkit. As of this post its on version 4.0. Another simple install. Download it from http:\/\/developer.nvidia.com\/cuda-toolkit-40#Linux. Again “ chmod +x cudatoolkit_4.0.17_linux_64_rhel6.0.run “ and then “.\/cudatoolkit_4.0.17_linux_64_rhel6.0.run” . The default install location is \/usr\/local\/cuda. Added the following lines below to the \/etc\/ld.so.conf file and execute, as root, “ldconfig” to update ld information. \/usr\/local\/cuda\/lib64 \/usr\/local\/cuda\/lib Finally, we install the Cuda SDK found on the same web page as the Cuda Toolkit. The instructions for this is exactly the same aside from the filename. A side note: The SMI tool may return many N\/A entries and this is a result of the latest Nvidia driver. We will provide a series of results from our GPU testing in the coming weeks. Thanks again to SuperMicro and Pinnacle Africa for arranging the loan unit.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/ucthpc.uct.ac.za\/index.php\/2011\/08\/10\/supermicro-tesla-m1060-gpu-system\/\" \/>\n<meta property=\"og:site_name\" content=\"UCT HPC\" \/>\n<meta property=\"article:published_time\" content=\"2011-08-10T09:26:06+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2015-08-14T11:23:02+00:00\" \/>\n<meta name=\"author\" content=\"Timothy Carr\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Timothy Carr\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/ucthpc.uct.ac.za\/index.php\/2011\/08\/10\/supermicro-tesla-m1060-gpu-system\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/ucthpc.uct.ac.za\/index.php\/2011\/08\/10\/supermicro-tesla-m1060-gpu-system\/\"},\"author\":{\"name\":\"Timothy Carr\",\"@id\":\"https:\/\/ucthpc.uct.ac.za\/#\/schema\/person\/41f6cd039836d7741f2b82a7b7cfe8d0\"},\"headline\":\"SuperMicro Tesla M1060 GPU System\",\"datePublished\":\"2011-08-10T09:26:06+00:00\",\"dateModified\":\"2015-08-14T11:23:02+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/ucthpc.uct.ac.za\/index.php\/2011\/08\/10\/supermicro-tesla-m1060-gpu-system\/\"},\"wordCount\":340,\"publisher\":{\"@id\":\"https:\/\/ucthpc.uct.ac.za\/#organization\"},\"articleSection\":[\"GPU\"],\"inLanguage\":\"en-ZA\"},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/ucthpc.uct.ac.za\/index.php\/2011\/08\/10\/supermicro-tesla-m1060-gpu-system\/\",\"url\":\"https:\/\/ucthpc.uct.ac.za\/index.php\/2011\/08\/10\/supermicro-tesla-m1060-gpu-system\/\",\"name\":\"SuperMicro Tesla M1060 GPU System - 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The system delivered is a SuperMicro – Tesla M1060. OS choice was Scientific Linux 5.4 x86_64. The distribution choice was chosen based on our existing HPC and GRID kit. Installation of the drivers and various kits was very simple and documented below.   Firstly, download the Nvidia GPU drivers from Nvidia Driver WebSite. You would then need to execute “ chmod +x NVIDIA-Linux-x86_64-280.13.run “. Execute the binary “ .\/NVIDIA-Linux-x86_64-280.13.run “. The installation will complain that the kernel headers have not been installed. The installation will terminate and now allow for you to install the kernel headers. Once the kernel headers are installed re-run the installation. You may verify the install by executing the system management interface tool, nvidia-smi.   “nvidia-smi -L “ will list all available GPU's configured on the system. [root@host installs]# nvidia-smi -L GPU 0: Tesla M1060 (UUID: GPU-80671de8eb0d438c-5f7dde2b-2ea53740-1d3b870a-86bf5cc98531e86374bc0514) GPU 1: Tesla M1060 (UUID: GPU-d4df9bac30037980-47cc692d-848f2bec-851c259f-fbfbd47d360bd2021f066d91) Next, we install the Cuda Toolkit. As of this post its on version 4.0. Another simple install. Download it from http:\/\/developer.nvidia.com\/cuda-toolkit-40#Linux. Again “ chmod +x cudatoolkit_4.0.17_linux_64_rhel6.0.run “ and then “.\/cudatoolkit_4.0.17_linux_64_rhel6.0.run” . The default install location is \/usr\/local\/cuda. Added the following lines below to the \/etc\/ld.so.conf file and execute, as root, “ldconfig” to update ld information. \/usr\/local\/cuda\/lib64 \/usr\/local\/cuda\/lib Finally, we install the Cuda SDK found on the same web page as the Cuda Toolkit. The instructions for this is exactly the same aside from the filename. A side note: The SMI tool may return many N\/A entries and this is a result of the latest Nvidia driver. We will provide a series of results from our GPU testing in the coming weeks. Thanks again to SuperMicro and Pinnacle Africa for arranging the loan unit.","og_url":"https:\/\/ucthpc.uct.ac.za\/index.php\/2011\/08\/10\/supermicro-tesla-m1060-gpu-system\/","og_site_name":"UCT HPC","article_published_time":"2011-08-10T09:26:06+00:00","article_modified_time":"2015-08-14T11:23:02+00:00","author":"Timothy Carr","twitter_card":"summary_large_image","twitter_misc":{"Written by":"Timothy Carr","Est. reading time":"2 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/ucthpc.uct.ac.za\/index.php\/2011\/08\/10\/supermicro-tesla-m1060-gpu-system\/#article","isPartOf":{"@id":"https:\/\/ucthpc.uct.ac.za\/index.php\/2011\/08\/10\/supermicro-tesla-m1060-gpu-system\/"},"author":{"name":"Timothy Carr","@id":"https:\/\/ucthpc.uct.ac.za\/#\/schema\/person\/41f6cd039836d7741f2b82a7b7cfe8d0"},"headline":"SuperMicro Tesla M1060 GPU System","datePublished":"2011-08-10T09:26:06+00:00","dateModified":"2015-08-14T11:23:02+00:00","mainEntityOfPage":{"@id":"https:\/\/ucthpc.uct.ac.za\/index.php\/2011\/08\/10\/supermicro-tesla-m1060-gpu-system\/"},"wordCount":340,"publisher":{"@id":"https:\/\/ucthpc.uct.ac.za\/#organization"},"articleSection":["GPU"],"inLanguage":"en-ZA"},{"@type":"WebPage","@id":"https:\/\/ucthpc.uct.ac.za\/index.php\/2011\/08\/10\/supermicro-tesla-m1060-gpu-system\/","url":"https:\/\/ucthpc.uct.ac.za\/index.php\/2011\/08\/10\/supermicro-tesla-m1060-gpu-system\/","name":"SuperMicro Tesla M1060 GPU System - 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