{"id":30262,"date":"2017-08-01T14:00:33","date_gmt":"2017-08-01T14:00:33","guid":{"rendered":"https:\/\/www.padtinc.com\/2017\/08\/01\/distributed-ansys-18-1-with-the-sp-5-benchmark-using-an-intel-1-6tb-nvme\/"},"modified":"2022-03-31T14:56:24","modified_gmt":"2022-03-31T21:56:24","slug":"distributed-ansys-18-1-with-the-sp-5-benchmark-using-an-intel-1-6tb-nvme","status":"publish","type":"post","link":"https:\/\/www.padtinc.com\/2017\/08\/01\/distributed-ansys-18-1-with-the-sp-5-benchmark-using-an-intel-1-6tb-nvme\/","title":{"rendered":"Distributed ANSYS 18.1 with the SP-5 Benchmark using an INTEL 1.6TB NVMe"},"content":{"rendered":"<p>I recently had a chance to run a series of benchmarks on one of our latest CUBE numerical simulation workstations. I was amazed by the impressive benchmark numbers and wanted to share with you the details for the SP-5 benchmark using ANSYS 18.1. Hopefully this information will help you make the best decision the next time you need to upgrade your numerical simulation C Drive from whatever to now is the time to buy a Non-Volitile Memory Express drive. Total speedup using identical CUBE hardware, except for the INTEL DC P3700 NVMe drive @32 Cores is a 1.19x speedup!<\/p>\n<ul>\n<li>Time Spent Computing Solution ANSYS SP-5 Benchmark\n<ul>\n<li>161.7 seconds vs. 135.6 second<\/li>\n<li>ANSYS 17.1 &amp; ANSYS 18.1 Benchmarks<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>The link below is to a great article that I think will catch you up to speed regarding NVMe, PCIe and SSD Technology.<\/p>\n<p>HDD Magazine hints NVME is coming, I say NVMe is already here&#8230;<\/p>\n<ul>\n<li>HDD Mag Arctle June 2, 2017<\/li>\n<\/ul>\n<h1>CUBE w32iP Specifications (July 2017)<\/h1>\n<ul>\n<li>CUBE Mid-Tower Super Quiet Chassis (900W PS)<\/li>\n<li>CPU: 32 INTEL Cores \u2013 2 x INTEL e5-2697A V4 32c@2.6GHz\/3.6GHz Turbo<\/li>\n<li>OS: INTEL NVMe \u2013 1 x 1.6TB INTEL Enterprise Class SSD<\/li>\n<li>Mid-Term Storage: \u2013 1 x 10TB Enterprise Class SATA 6Gbp\/s, 256M, Helium sealed<\/li>\n<li>RAM: 256GB DDR4-2400MHz LRDIMM RAM<\/li>\n<li>GRAPHICS: NVIDIA QUADRO P6000 (24GB GDDR5X RAM)<\/li>\n<li>MEDIA: DVD-RW\/Audio 7.1 HD<\/li>\n<li>Windows 10 Professional<\/li>\n<\/ul>\n<h2>Just how much faster the INTEL NVME drive performs over previously run ANSYS Benchmarks?<\/h2>\n<h3>Check out the data for yourself:<\/h3>\n<ol>\n<li><a href=\"http:\/\/www.padtinc.com\/blog\/the-focus\/ansys-17-1-fea-benchmarks-using-v17-sp5\" target=\"_blank\" rel=\"noopener\">ANSYS 17.1 &#8211; SP-5 Benchmarks<\/a><\/li>\n<li><a href=\"https:\/\/web.archive.org\/web\/20171210005550\/http:\/\/www.ansys.com:80\/Solutions\/Solutions-by-Role\/it-professionals\/platform-support\/benchmarks-overview\/ansys-mechanical-benchmarks\/ansys-mechanical-benchmarks-15\/bga\" target=\"_blank\" rel=\"noopener\">ANSYS Website<\/a><\/li>\n<li><a href=\"http:\/\/www.hpcadvisorycouncil.com\/\" target=\"_blank\" rel=\"noopener\">HPC Advisory Council<\/a><\/li>\n<\/ol>\n<ul>\n<li>ANSYS Benchmark Test Case Information.<\/li>\n<li>ANSYS HPC Licensing Packs required for this benchmark\n<ul>\n<li>I used (2) HPC Packs to unlock all 32 cores.<\/li>\n<\/ul>\n<\/li>\n<li>1.19x Total Speedup!<\/li>\n<li>Please contact your local ANSYS Software Sales Representative for more information on purchasing ANSYS HPC Packs. You too may be able to speed up your solve times by unlocking additional compute power!<\/li>\n<li>What is a CUBE? For more information regarding our Numerical Simulation workstations and clusters please contact our CUBE Hardware Sales Representative at SALES@PADTINC.COM\n<ul>\n<li>Designed, tested and configured within your budget. We are happy to help and to\u00a0 listen to your specific needs.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h1>ANSYS SP-5 Benchmark Details<\/h1>\n<table width=\"499\">\n<tbody>\n<tr>\n<td colspan=\"2\" width=\"264\">\n<h4>BGA (V18sp-5)<\/h4>\n<\/td>\n<td width=\"235\"><\/td>\n<\/tr>\n<tr>\n<td>Analysis Type<\/td>\n<td><\/td>\n<td>Static Nonlinear Structural<\/td>\n<\/tr>\n<tr>\n<td>Number of Degrees of Freedom<\/td>\n<td><\/td>\n<td>6,000,000<\/td>\n<\/tr>\n<tr>\n<td>Equation Solver<\/td>\n<td><\/td>\n<td>Sparse<\/td>\n<\/tr>\n<tr>\n<td>Matrix<\/td>\n<td><\/td>\n<td>Symmetric<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<table width=\"588\">\n<tbody>\n<tr>\n<td width=\"43\">\u00a0July 2017<\/td>\n<td width=\"169\">TIME SPENT COMPUTING SOLUTION<\/td>\n<td width=\"187\">TOTAL CPU TIME FOR MAIN THREAD<\/td>\n<td width=\"189\">ELAPSED TIME<\/td>\n<\/tr>\n<tr>\n<td><\/td>\n<td>CUBE w32iP<\/td>\n<td>CUEB w32iP<\/td>\n<td>CUBE w32iP<\/td>\n<\/tr>\n<tr>\n<td width=\"43\"># of Cores<\/td>\n<td width=\"169\">CUBE w32iP<\/td>\n<td width=\"187\">CUBE w32iP<\/td>\n<td width=\"189\">CUBE w32iP<\/td>\n<\/tr>\n<tr>\n<td>2<\/td>\n<td>1034.3<\/td>\n<td>1073.7<\/td>\n<td>1076<\/td>\n<\/tr>\n<tr>\n<td>4<\/td>\n<td>594.7<\/td>\n<td>630.3<\/td>\n<td>633<\/td>\n<\/tr>\n<tr>\n<td>6<\/td>\n<td>431.5<\/td>\n<td>465.7<\/td>\n<td>472<\/td>\n<\/tr>\n<tr>\n<td>8<\/td>\n<td>333.4<\/td>\n<td>367.9<\/td>\n<td>377<\/td>\n<\/tr>\n<tr>\n<td>10<\/td>\n<td>268.7<\/td>\n<td>302.6<\/td>\n<td>316<\/td>\n<\/tr>\n<tr>\n<td>12<\/td>\n<td>243.6<\/td>\n<td>276.5<\/td>\n<td>287<\/td>\n<\/tr>\n<tr>\n<td>14<\/td>\n<td>223<\/td>\n<td>256.2<\/td>\n<td>264<\/td>\n<\/tr>\n<tr>\n<td>16<\/td>\n<td>186.8<\/td>\n<td>219.3<\/td>\n<td>227<\/td>\n<\/tr>\n<tr>\n<td>18<\/td>\n<td>180<\/td>\n<td>212.4<\/td>\n<td>226<\/td>\n<\/tr>\n<tr>\n<td>20<\/td>\n<td>174.4<\/td>\n<td>207.4<\/td>\n<td>220<\/td>\n<\/tr>\n<tr>\n<td>22<\/td>\n<td>164.5<\/td>\n<td>197.4<\/td>\n<td>209<\/td>\n<\/tr>\n<tr>\n<td>24<\/td>\n<td>155.6<\/td>\n<td>188.2<\/td>\n<td>199<\/td>\n<\/tr>\n<tr>\n<td>26<\/td>\n<td>147.1<\/td>\n<td>179.2<\/td>\n<td>193<\/td>\n<\/tr>\n<tr>\n<td>28<\/td>\n<td>146.4<\/td>\n<td>178.2<\/td>\n<td>190<\/td>\n<\/tr>\n<tr>\n<td>30<\/td>\n<td>140.8<\/td>\n<td>168.5<\/td>\n<td>196<\/td>\n<\/tr>\n<tr>\n<td>31<\/td>\n<td>140.4<\/td>\n<td>164<\/td>\n<td>196<\/td>\n<\/tr>\n<tr>\n<td>32<\/td>\n<td>135.6<\/td>\n<td>158.1<\/td>\n<td>182<\/td>\n<\/tr>\n<tr>\n<td><\/td>\n<td>WO\/GPU Acceleration<\/td>\n<td>WO\/GPU Acceleration<\/td>\n<td>WO\/GPU Acceleration<\/p>\n<p>July 2017, drjm, PADT, Inc.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>CUBE W32iP SP-5 Benchmark Graph<\/p>\n<p><a href=\"https:\/\/www.padtinc.com\/wp-content\/uploads\/2022\/02\/cubew32ip-July2017-sp5.png\"><picture><source srcset=\"https:\/\/www.padtinc.com\/wp-content\/uploads\/2022\/02\/cubew32ip-July2017-sp5.webp \"  type=\"image\/webp\"><img src=\"https:\/\/www.padtinc.com\/wp-content\/uploads\/2022\/02\/cubew32ip-July2017-sp5.png\" height=\"611\" width=\"993\" class=\"size-full wp-image-9973 sp-no-webp\" alt=\"CUBE w32iP with INTEL DC P3700 1.6TB\" fetchpriority=\"high\" decoding=\"async\"  > <\/picture><\/a><\/p>\n<p><a href=\"https:\/\/www.padtinc.com\/legacy-link\/\" target=\"_blank\" rel=\"noopener\">Click Here<\/a> for more information on the engineering simulation workstations and clusters designed in-house at PADT, Inc.. PADT, Inc. is happy to be a premier re-seller and dealer of <a href=\"https:\/\/www.supermicro.com\/index_home.cfm\" target=\"_blank\" rel=\"noopener\">Supermicro <\/a>hardware.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>I recently had a chance to run a series of benchmarks on one of our latest CUBE numerical simulation workstations. [&hellip;]<\/p>\n","protected":false},"author":5,"featured_media":30263,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2323],"tags":[1727,1728,1729],"post-podcast":[],"_links":{"self":[{"href":"https:\/\/www.padtinc.com\/wp-json\/wp\/v2\/posts\/30262"}],"collection":[{"href":"https:\/\/www.padtinc.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.padtinc.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.padtinc.com\/wp-json\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/www.padtinc.com\/wp-json\/wp\/v2\/comments?post=30262"}],"version-history":[{"count":0,"href":"https:\/\/www.padtinc.com\/wp-json\/wp\/v2\/posts\/30262\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.padtinc.com\/wp-json\/wp\/v2\/media\/30263"}],"wp:attachment":[{"href":"https:\/\/www.padtinc.com\/wp-json\/wp\/v2\/media?parent=30262"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.padtinc.com\/wp-json\/wp\/v2\/categories?post=30262"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.padtinc.com\/wp-json\/wp\/v2\/tags?post=30262"},{"taxonomy":"post-podcast","embeddable":true,"href":"https:\/\/www.padtinc.com\/wp-json\/wp\/v2\/post-podcast?post=30262"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}