{"id":26119,"date":"2013-11-18T19:31:00","date_gmt":"2013-11-18T19:31:00","guid":{"rendered":"https:\/\/www.padtinc.com\/2013\/11\/18\/this-may-be-the-fastest-ansys-mechanical-workstation-we-have-built-so-far\/"},"modified":"2022-03-24T22:54:14","modified_gmt":"2022-03-24T22:54:14","slug":"this-may-be-the-fastest-ansys-mechanical-workstation-we-have-built-so-far","status":"publish","type":"post","link":"https:\/\/www.padtinc.com\/2013\/11\/18\/this-may-be-the-fastest-ansys-mechanical-workstation-we-have-built-so-far\/","title":{"rendered":"This May Be the Fastest ANSYS Mechanical Workstation we Have Built So Far"},"content":{"rendered":"

The Build Up<\/h2>\n

Its 6:30am and a dark shadow looms in Eric\u2019s doorway. I wait until Eric finishes his Monday morning company updates. \u201cEric check this out, the CUBE HVPC w16i-k20x we built for our latest customer ANSYS Mechanical scaled to 16 cores on our test run.\u201d The left eyebrow of Eric\u2019s slightly rises up. I know I have him now I have his full and complete attention.<\/p>\n

Why is this huge news?<\/h2>\n<\/p>\n

This is why; Eric knows and probably many of you reading this also know that solving differential equations, distributed, parallel along with using graphic processing unit makes our hearts skip a beat. The finite element method used for solving these equations is CPU intensive and I\/O intensive. This is headline news type stuff to us geek types. We love scratching our way along the compute processing power grids to utilize every bit of performance out of our hardware! <\/p>\n

Oh and yes a lower time to solve is better! No GPU\u2019s were harmed in this tests. Only one NVIDIA TESLA k20X GPU was used during the test.<\/p>\n

Take a Deep Breath and Start from the Beginning:<\/h2>\n<\/p>\n

I have been gathering and hording years\u2019 worth of ANSYS mechanical benchmark data. Why? Not sure really after all I am wanna-be ANSYS Analysts. However, it wasn\u2019t until a couple weeks ago that I woke up to the why again. MY CUBE HVPC team sold a dual socket INTEL Ivy bridge based workstation to a customer out of Washington state. Once we got the order, our Supermicro reseller\u2018s phone has been bouncing of the desk. After some back and forth, this is how the parts arrive directly from Supermicro, California. Yes, designed in the U.S.A.  And they show up in one big box: <\/p>\n

\"clip_image002[4]\"<\/p>\n

Normal is as Normal Does<\/h2>\n

As per normal is as normal does, I ran the series of ANSYS benchmarks. You know the type of benchmarks that perform coupled-physics simulations and solving really huge matrix numbers. So I ran ANSYS v14sp-5, ANSYS FLUENT benchmarks and some benchmarks for this customer, the types of runs they want to use the new machine for. So I was talking these benchmark results over with Eric. He thought that now is a perfect time to release the flood of benchmark data. Well some\/a smidge of the benchmark data. I do admit the data does get overwhelming so I have tried to trim down the charts and graphs to the bare minimum. So what makes this workstation recipe for the fastest ANSYS Mechanical workstation so special? What is truly exciting enough to tip me over in my overstuffed black leather chair?<\/p>\n

The Fastest Ever? Yup we have been Changed Forever<\/h2>\n<\/p>\n

Not only is it the fastest ANSYS Mechanical workstation running on CUBE HVPC hardware.  It uses two INTEL CPU\u2019s at 22 nanometers. Additionally, this is the first time that we have had an INTEL dual socket based workstation continue to gain faster times on and up to its maximum core count when solving in ANSYS Mechanical APDL. <\/p>\n

Previously the fastest time was on the CUBE HVPC w16i-GPU workstation listed below. And it peaked at 14 cores.  <\/p>\n

Unfortunately we only had time before we shipped the system off to gather two runs: 14 and 16 cores on the new machine. But you can see how fast that was in this table.  It was close to the previous system at 14 cores, but blew past it at 16 whereas the older system actually got clogged up and slowed down: <\/p>\n\n\n\n\n\n\n\n\n
 <\/td>\nRun Time (Sec)<\/strong><\/td>\n<\/tr>\n
Cores Used<\/strong><\/td>\nConfig B<\/strong><\/td>\nConfig C<\/strong><\/td>\nConfig D<\/strong><\/td>\n<\/tr>\n
14<\/td>\n129.1<\/td>\n95.1<\/td>\n91.7<\/td>\n<\/tr>\n
16<\/td>\n130.5<\/td>\n99<\/td>\n83.5<\/td>\n<\/tr>\n<\/tbody>\n<\/colgroup>\n<\/table>\n

And here are the results as a bar graph for all the runs with this benchmark: <\/p>\n

\"CUBE-Benchmark-ANSYS-2013_11_01\" <\/p>\n

  We can\u2019t wait to build one of these with more than one motherboard, maybe a 32 core system with infinband connecting the two. That should allow some very fast run times on some very, very large problems. <\/p>\n

<\/u> <\/p>\n

ANSYS V14sp-5 ANSYS R14 Benchmark Details<\/u><\/p>\n