PADT Events – February 2017

Although February is a short month, we have lots of activities scheduled to talk about new releases from both ANSYS and Stratasys as well as a STEM and Medtech event. Take a look for details below or visit the bottom of our home page to see the latest.


Arizona Science Bowl

02/04/17
ASU West Campus
Glendale, AZ

PADT will be attending this great event for middle and high schools. Dr. Bhate will be speaking to the middle school students
Learn more

2017 Stratasys New Product Launch Webinar

02/09/17
Online

Stratasys is introduce some new products and you are invited to attend online to learn how once again they will advance 3D Printing to the next level. PADT’s engineers will not just share information about these new systems, they will also explain what we thing is important about each machine and what its new advantages are.
Learn more

ANSYS 18 – Mechanical APDL & HPC Update Webinar

02/14/17
Online

ANSYS is rolling out a new version of their entire software platform, and we are offering seminars to help users understand what is new and cool. This first webinar will be focused on ANSYS Mechanical APDL and what is going on way deep under the hood.
Learn more

AZ Tech Council MedTech
Conference, 2017

02/23/17
Spear Education
Scottsdale, AZ

Medtech has grown a lot in Arizona over the past couple of years, so the Tech Council is putting on an event for everyone involved to get together to network and learn. PADT will have a booth and will be talking about 3D Printing in medical devices. If you are at all involved in medical technology, you should attend.
Learn more

ANSYS 18 – HPC Licensing Update Webinar

02/28/17
Online

ANSYS is rolling out a new version of their entire software platform, and we are offering seminars to help users understand what is new and cool. This second webinar will be focused on ANSYS HPC licensing and how that has changed.
Learn more

Stratasys Release Webinar 2017

We here at PADT are excited to share information on the next big release from Stratasys, the global leader in 3D printing, additive solutions, materials and services.

The name Stratasys has always been synonymous with top of the line machines that meet even the most advanced rapid prototyping needs, and excel at every stage of the design prototyping process.

This new release is no exception.

Keep an eye out for more information on February 6th

Update on ASU 3D Printing Research and Teaching Lab

Two weeks ago we were part of a fantastic open house at the ASU Polytechnic campus for the grand opening of the Additive Manufacturing Research center, a part of the Manufacturing Research and Innovation Hub.  What a great event it was where the Additive Manufacturing community in Arizona gathered in one place to celebrate  this important piece in the local ecosystem.  A piece that puts Arizona in the lead for the practical application of 3D Printing in industry.

I could go on and on, but better writers by far have penned some great stories on the event and on the lab.

ASU’s article is here: New hub’s $2 million in cutting-edge 3-D printing equipment will allow students to stay on forefront of rapidly growing sector

And Hayley Ringle of the Phoenix Business Journal summed it all up, with some great insight into the impact on education and job growth in “See inside the Southwest’s largest 3D printing research facility at ASU

And last but not least, here are some pictures related to PADT that ASU provided:

Phoenix Business Journal: Why accurate prototypes are important to product development success

Cutting corners rarely pays off, and that is especially true in product development when you skimp on physical or virtual prototyping.  In “Why accurate prototypes are important to product development success” I take a look at why accurate prototyping is so important, with some real world lesson learned as examples.

Phoenix Business Journal: ​How do you get value out of Big Data? Simulation!

It’s all the rage. “Big Data!” fixes everything. There is a lot of hype around the value of knowing so much about so many things. The problem is very few people have figured out what to do with that data.  But leading technology companies like GE are using a proven tool to get value from all that great data.  In “How do you get value out of Big Data? Simulation!” I look at how numerical simulation can be used to create digital twins of what your products are doing in the real world, delivering huge benefits today.

Forbes: Ask Not What Your Career Can Do For You, Ask What You Can Do For Your Career

PADT’s Manager of Human Resources, Lara Maack contributed a fantastic post to Forbes’ “Grads of LifeVoice” blog with her observations on how young employees in tech can improve their careers.  In “Ask Not What Your Career Can Do For You, Ask What You Can Do For Your Career” she outlines ten basic steps every young tech worker can take to make the most out of what they have. If you are millennial in the workforce or deal with millennials, it is a useful read.

Not only is it a great article, but PADT’s very own Clinton, Patrick, and Stephen made it in the pictures of PADT’s representative “young employees”

 

Discover the Power of Pervasive Simulation – ANSYS R 18

Introducing the Release of ANSYS 18

Manufacturing is undergoing the most fundamental transformation since the introduction of the assembly line. Trends like the Internet of Things, additive manufacturing and machine learning are merging the physical and digital worlds, resulting in products that defy imagination.

Join the new CEO of ANSYS, Ajei Gopal, and visionary customers

CumminsNebia,OticonMetso, and GE Digital as they demonstrate the power of pervasive simulation, available in the release of ANSYS 18.

Attend this webinar to learn:

  • How you can use digital exploration to quickly evaluate changes in design, reducing development costs and preventing late-stage design changes
  • How digital prototyping enables you to provide insights into real-world product performance, test “what-if” scenarios and ensure optimal designs
  • How simulation is moving downstream of the product life-cycle through the use of digital twins to increase efficiency and to decrease unplanned downtime

Stay tuned as we will be covering the new additions in ANSYS 18 over the next few months.

PADT Named ANSYS North American Channel Partner of the Year and Becomes an ANSYS Certified Elite Channel Partner

The ANSYS Sales Team at PADT was honored last week when we were recognized four times at the recent kickoff meeting for the ANSYS North American Sales orginization.  The most humbling of those trips up to the stage was when PADT was recognized as the North American Channel Partner of the Year for 2016.  It was humbling because there are so many great partners that we have had the privilege of worked with for almost 20 years now.  Our team worked hard, and our customers were fantastic, so we were able to make strides in adding capability at existing accounts, finding new customers that could benefit from ANSYS simulation tools, and expanding our reach further in Southern California.  It helps that simulation driven product development actually works, and ANSYS tools allow it to work well.

Here we are on stage, accepting the award:

PADT Accepts the Channel Partner of the Year Award. (L-R: ANSYS CEO Ajei Gopal, ANSYS VP Worldwide Sales and Customer Excellence Rick Mahoney, ANSYS Director of WW Channel Ravi Kumar, PADT Co-Owner Ward Rand, PADT Co-Owner Eric Miller, PADT Software Sales Manager Bob Calvin, ANSYS VP Sales for the Americas Ubaldo Rodriguez

We were also recognized two other times; for exceeding our sales goals and for making the cut to the annual President’s Club retreat.   As a reminder, PADT sells the full multiphysics product line from PADT in Southern California, Arizona, New Mexico, Colorado, Utah, and Nevada.  This is a huge geographic area with a very diverse set of industries and customers.

In addition, ANSYS, Inc. announced that PADT was one of several Channel Partners who had obtained Elite Certified Channel Partner status. This will allow PADT to provide our customers with better services and gives our team access to more resources within ANSYS, Inc.

Once we made it back from the forests and hills of Western Pennsylvania we were able to get a picture with the full sale team.  Great job guys:

We could not have had such a great 2016 without the support of everyone at PADT. The sales team, the application engineers, the support engineers, business operations, and everyone else that pitches in.   We look forward to making more customers happy in 2017 and coming back with additional hardware.

Be Alert and Always Inert – The importance of an inert environment with Metal 3D Printing (Video)

Metal 3D Printing is one of the more exciting areas of additive manufacturing, and we are learning a lot about how to safely operate our new system.  Our very own Dhruv Bhate, PhD shared those lessons learned in this new video:

The video stresses the importance of keeping an inert environment to keep part quality and to ensure a safe operating environment. Our Concept Laser Metal 3D Printer uses high powered lasers to melt metal powder one layer at a time to build 3D Parts. This process produces soot that is highly flammable.

Dhruv shows the process we use to break the part from the machine, clean the chamber of soot, and replace the filter that captures the soot.

To learn more about PADT and how we can help you with your 3D Printing, product development, or Numerical Simulation needs, please visit www.padtinc.com

Use this link to see all of our blog posts on Metal 3D Printing

Work for a startup or know someone who does? – Don’t miss this opportunity!

We here at PADT would like to remind you that our webinar covering the significance of simulation for startups is taking place soon!

Join us: Wednesday January 25, 2017

From 12 pm – 1 pm MST

PADT’s Co-owner and Principal, Eric Miller, will be presenting on the various benefits that simulation software can provide for startup companies and entrepreneurs alike. By attending this webinar you will learn:

  • The practical uses of simulation in product design
  • How simulation has driven innovation
  • Why simulation is the most effective tool for startups
  • How simulation can reduce time to market as well as production costs
  • And how you can take advantage of the discounts that the ANSYS Startup Program provides

While many startups tend to avoid using simulation due to cost or a lack of accessibility, this is a key aspect of the modern manufacturing process and should not be ignored. Many people check on Best in Nashik to know about some of the best businesses.

As a partner in the Startup Program, you will gain instant access to ANSYS solutions so you can start building virtual prototypes of your new products. These virtual prototypes can be modified and tested with simulation hundreds of times in the same time it would take to build and test one physical prototype – saving you time and money as you work to perfect your product design. The partnership gives you access to the full portfolio of multiphysics simulation bundles, including the Structural and Fluids bundle and the Electromagnetics bundle.

Take advantage of this opportunity and register today!

Phoenix Business Journal: ​Voice recognition, the new thing in computing

Let’s be honest, the mouse and keyboard are outdated interface methods that serve us well, but voice recognition is pretty dang awesome and efficient. In “​Voice recognition, the new thing in computing” I write an entire post using voice recognition about the pros and cons of voice recognition.  That is almost meta. Please enjoy, it was a fun one to do.

ANSYS Startup Program – Webinar


Phoenix Analysis & Design Technologies Presents:

ANSYS Startup Program: The Significance of Simulation


Wednesday January 25th, from 12 pm – 1 pm MST

We here at PADT would like to remind you about our upcoming webinar covering the importance of simulation software for startups. Not only can the use of such programs help to shorten your company’s time to market, it is also beneficial for reducing manufacturing costs.   


 Click Here to register for this webinar 


While many startups tend to avoid using simulation due to cost or a lack of accessibility, this is a key aspect of the modern manufacturing process and should not be ignored.

As a partner in the Startup Program, you will gain instant access to ANSYS solutions so you can start building virtual prototypes of your new products. These virtual prototypes can be modified and tested with simulation hundreds of times in the same time it would take to build and test one physical prototype – saving you time and money as you work to perfect your product design. The partnership gives you access to the full portfolio of multiphysics simulation bundles, including the Structural and Fluids bundle and the Electromagnetics bundle.

Attend this webinar to discover how you and Phoenix Analysis and Design Technologies can take advantage of the numerous benefits that ANSYS simulation software has to offer, priced at a cost designed for you. 


 Click Here to register for this webinar 


This webinar is taking being held on Wednesday, January 25th from 12 pm – 1 pm MST, and is a can’t miss opportunity. Make sure to register to attend today, we look forward to seeing you there!

Phoenix Business Journal My View: Tech leaders need to take a stand for facts and truth

I’ve had enough.  The destruction of facts and truth in public and business is not acceptable.  The Phoenix Business Journal has allowed me a nice big soap box to rant from as a guest this week on their regular “My View” feature.

Usually I don’t make a direct apeal for anyone to read my musing, let alone share it.  Now I am asking you to read “My View: Tech leaders need to take a stand for facts and truth” and if it resonates with you, please share it with others.  I believe what I said:

“So what can we do? We must dig our heals in and challenge misinformation, or at least demand supporting facts. We cannot back down when those we call to task use bluster and misdirection to avoid answering our challenge. Call them out on their tactics, don’t accept lies, don’t stoop to their level of name calling, stick to the facts, and stay on topic.”

Thank you for your consideration.

ANSYS HPC Distributed Parallel Processing Decoded: CUBE Workstation

ANSYS HPC Distributed Parallel Processing Decoded: CUBE Workstation

Meanwhile, in the real world the land of the missing-middle:  To read and learn more about the missing middle please read this article by Dr. Stephen Wheat. Click Here

This blog post is about distributed parallel processing performance in a missing-middle world of science, tech, engineering & numerical simulation. I will be using two of PADT, Inc.’s very own CUBE workstations along with ANSYS 17.2. To illustrate facts and findings on the ANSYS HPC benchmarks. I will also show you how to decode and extract key bits of data out of your own ANSYS benchmark out files. This information will assist you with locating and describing the performance how’s and why’s on your own numerical simulation workstations and HPC clusters. With the use of this information regarding your numerical simulation hardware. You will be able to trust and verify your decisions. Assist you with understanding in addition to explaining the best upgrade path for your own unique situation. In this example, I am providing to you in this post. I am illustrating a “worst case” scenario.

You already know you need to increase your parallel processing solves times of your models. “No I am not ready with my numerical simulation results. No I am waiting on Matt to finish running the solve of his model.” “Matt said that it will take four months to solve this model using this workstation. Is this true?!”

  1. How do I know what to upgrade and/or you often find yourself asking yourself. What do I really need to buy?
    1. One or three ANSYS HPC Packs?
    2. Purchase more compute power? NVidia TESLA K80’s GPU Accelerators? RAM? A Subaru or Volvo?
  2. I have no budget. Are you sure? Often IT departments set a certain amount of money for component upgrades and parts. Information you learn in these findings may help justify a $250-$5000 upgrade for you.
  3. These two machines as configured will not break the very latest HPC performance speed records. This exercise is a live real world example of what you would see in the HPC missing middle market.
  4.  Benchmarks were formed months after a hardware and software workstation refresh was completed using NO BUDGET, zip, zilch, nada, none.

Backstory regarding the two real-world internal CUBE FEA Workstations.

  1. These two CUBE Workstations were configured on a tight budget. Only the components at a minimum were purchased by PADT, Inc.
  2. These two internal CUBE workstations have been in live production, in use daily for one or two years.
    1. Twenty-four hours a day seven days a week.
  3. These two workstations were both in desperate need of some sort of hardware and operating system refresh.
  4. As part of Microsoft upgrade initiative in 2016.  Windows 10 Professional was upgraded for free! FREE!

Again, join me in this post and read about the journey of two CUBE workstations being reborn and able to produce impressive ANSYS benchmarks to appease the sense of wining in pure geek satisfaction.

Uh-oh?! $$$

As I mentioned, one challenge that I set for myself on this mission is that I would not allow myself to purchase any new hardware or software. What? That is correct; my challenge was that I would not allow myself to purchase new components for the refresh.

How would I ever succeed in my challenge? Think and then think again.

Harvesting the components of old workstations recently piling up in the IT Lab over the past year! That was the solution. This idea just may be the idea I needed for succeeding in my NO BUDGET challenge. First, utilize existing compute components from old tired machines that had showed in the IT boneyard. Talk to your IT department, you never know what they find or remember that they had laying around in their own IT boneyard. Next, I would also use any RMA’d parts that I could find that had trickled in over the past year. Indeed, by utilizing these old feeder workstations, I was on my way to succeeding in my no budget challenge. The leftovers? Please do not email me for the discarded not worthy components handouts. There is nothing left, none, those components are long gone a nice benefit from our recent in-house next PADT Tech Recycle event.

*** Public Service Announcement *** Please remember to reuse, recycle and erase old computer parts from the landfills.

CUBE Workstation Specifications

PADT, Inc. – CUBE w12ik Numerical Simulation Workstation

(INTENAL PADT CUBE Workstation “CUBE #10”)
1 x CUBE Mid-Tower Chassis (SQ edition)

2 x 6c @3.4GHz/ea (INTEL XEON e5-2643 V3 CPU)

Dual Socket motherboard

16 x 16GB DDR4-2133 MHz ECC REG DIMM

1 x SMC LSI 3108 Hardware RAID Controller – 12 Gb/s

4 x 600GB SAS2 15k RPM – 6 Gb/s – RAID0

3 x 2TB SAS2 7200 RPM Hard Drives – 6 Gb/s (Mid-Term Storage Array – RAID5)

NVIDIA QUADRO K6000 (NVidia Driver version 375.66)

2 x LED Monitors (1920 x 1080)

Windows 10 Professional 64-bit

ANSYS 17.2

INTEL MPI 5.0.3

PADT, Inc. CUBE w16i-k Numerical Simulation Workstation

(INTENAL PADT CUBE Workstation “CUBE #14″)
1 x CUBE Mid-Tower Chassis

2 x 8c @3.2GHz/ea (INTEL XEON e5-2667 V4 CPU)

Dual Socket motherboard

8 x 32GB DDR4-2400 MHz ECC REG DIMM

1 x SMC LSI 3108 Hardware RAID Controller – 12 Gb/s

4 x 600GB SAS3 15k RPM 2.5” 12 Gb/s – RAID0

2 x 6TB SAS3 7.2k RPM 3.5” 12 Gb/s – RAID1

NVIDIA QUADRO K6000 (NVidia Driver version 375.66)

2 x LED Monitors (1920 x 1080)

Windows 10 Professional 64-bit

ANSYS 17.2

INTEL MPI 5.0.3

The ANSYS sp-5 Ball Grid Array Benchmark

ANSYS Benchmark Test Case Information

  • BGA (V17sp-5)
    • Analysis Type Static Nonlinear Structural
    • Number of Degrees of Freedom 6,000,000
    • Equation Solver Sparse
    • Matrix Symmetric
  • ANSYS 17.2
  • ANSYS HPC Licensing Packs required for this benchmark –> (2) HPC Packs
  • 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!
  • 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 Designed, tested and configured within your budget. We are happy to help and to listen to your specific needs.

Comparing the data from the 12 core CUBE vs. a 16 core CUBE with and without GPU Acceleration enabled.

ANSYS 17.2 Benchmark  SP-5 Ball Grid Array
CUBE w12i-k 2643 v3 CUBE w12i-k 2643 v3 w/GPU Acceleration Total Speedup w/GPU CUBE w16i-k 2667 V4 CUBE w16i-k 2667 V4 w/GPU Acceleration Total Speedup w/GPU
Cores CUBE  w12i w/NVIDIA QUADRO K6000 CUBE  w12i w/NVIDIA QUADRO K6000 CUBE  w16i w/NVIDIA QUADRO K6000 CUBE  w16i w/NVIDIA QUADRO K6000
2 878.9 395.9 2.22 X 888.4 411.2 2.16 X
4 485.0 253.3 1.91 X 499.4 247.8 2.02 X
6 386.3 228.2 1.69 X 386.7 221.5 1.75 X
8 340.4 199.0 1.71 X 334.0 196.6 1.70 X
10 269.1 184.6 1.46 X 266.0 180.1 1.48 X
11 235.7 212.0 1.11 X
12 230.9 171.3 1.35 X 226.1 166.8 1.36 X
14 213.2 173.0 1.23 X
15 200.6 152.8 1.31 X
16 189.3 166.6 1.14 X
GPU NOT ENABLED ENABLED NOT ENABLED ENABLED
11/15/2016 & 1/5/2017
CUBE w12i-k v17sp-5 Benchmark Graph 2017
CUBE w12i-k v17sp-5 Benchmark Graph 2017
CUBE w16i-k v17sp-5 Benchmark Graph 2017
CUBE w16i-k v17sp-5 Benchmark Graph 2017

Initial impressions

  1. I was very pleased with the results of this experiment. Using the Am I bound bound or I/O bound overall parallel performance indicators the data showed healthy workstations that were both I/O bound. I assumed the I/O bound issue would happen. During several of the benchmarks, the data reveals almost complete system bandwidth saturation. Upwards of ~82 GB/s of bandwidth created during the in-core distributed solve!
  2. I was pleasantly surprised to see a 1.7X or greater solve speedup using one ANSYS HPC licensing pack and GPU Acceleration!

The when and where of numerical simulation performance bottleneck’s for numerical simulation. Similar to how the clock is ticking on the wall, over the years I have focused on the question of, “is your numerical simulation compute hardware compute bound or I/O bound”. This quick and fast benchmark result will show general parallel performance of the workstation and help you find the performance sweet spot for your own numerical simulation hardware.

As a reminder, to determine the answer to that question you need to record the results of your CPU Time For Main Thread, Time Spent Computing Solution and Total Elapsed Time. If the results time for my CPU Main is about the same as my Total Elapsed Time result. The compute hardware is in a Compute Bound situation. If the Total Elapsed Time result is larger than the CPU Time For Main Thread than the compute hardware is I/O bound. I did the same analysis with these two CUBE workstations. I am pickier than most when it comes to tuning my compute hardware. So often I will use a percentage around 95 percent. The percentage column below determines if the workstation is Compute Bound or O/O bound. Generally, what I have found in the industry, is that a percentage of greater than 90% indicates the workstation is wither Compute Bound, I/O bound or in worst-case scenario is both.

**** Result sets data garnered from the ANSYS results.out files on these two CUBE workstations using ANSYS Mechanical distributed parallel solves.

Data mine that ANSYS results.out file!

The data is all there, at your fingertips waiting for you to trust and verify.

Compute Bound or I/O bound

Results 1 – Compute Cores Only

w12i-k

“CUBE #10”

Cores CPU Time For Main Thread Time Spent Computing Solution Total Elapsed Time % Compute Bound IO Bound
2 2 914.2 878.9 917.0 99.69 YES NO
4 4 517.2 485.0 523.0 98.89 YES NO
6 6 418.8 386.3 422.0 99.24 YES NO
8 8 374.7 340.4 379.0 98.87 YES NO
10 10 302.5 269.1 307.0 98.53 YES NO
11 11 266.6 235.7 273.0 97.66 YES NO
12 12 259.9 230.9 268.0 96.98 YES NO
w16i-k

“CUBE #14”

Cores CPU Time For Main Thread Time Spent Computing Solution Total Elapsed Time % Compute Bound IO Bound
2 2 925.8 888.4 927.0 99.87 YES NO
4 4 532.1 499.4 535.0 99.46 YES NO
6 6 420.3 386.7 425.0 98.89 YES NO
8 8 366.4 334.0 370.0 99.03 YES NO
10 10 299.7 266.0 303.0 98.91 YES NO
12 12 258.9 226.1 265.0 97.70 YES NO
14 14 244.3 213.2 253.0 96.56 YES NO
15 15 230.3 200.6 239.0 96.36 YES NO
16 16 219.6 189.3 231.0 95.06 YES NO

Results 2 – GPU Acceleration + Cores

w12i-k

“CUBE #10”

Cores  + GPU CPU Time For Main Thread Time Spent Computing Solution Total Elapsed Time % Compute Bound IO Bound
2 2 416.3 395.9 435.0 95.70 YES YES
4 4 271.8 253.3 291.0 93.40 YES YES
6 6 251.2 228.2 267.0 94.08 YES YES
8 8 219.9 199.0 239.0 92.01 YES YES
10 10 203.2 184.6 225.0 90.31 YES YES
11 11 227.6 212.0 252.0 90.32 YES YES
12 12 186.0 171.3 213.0 87.32 NO YES
CUBE 14 Cores + GPU CPU Time For Main Thread Time Spent Computing Solution Total Elapsed Time % Compute Bound IO Bound
2 2 427.2 411.2 453.0 94.30 YES YES
4 4 267.9 247.8 286.0 93.67 YES YES
6 6 245.4 221.5 259.0 94.75 YES YES
8 8 219.6 196.6 237.0 92.66 YES YES
10 10 201.8 180.1 222.0 90.90 YES YES
12 12 191.2 166.8 207.0 92.37 YES YES
14 14 195.2 173.0 217.0 89.95 NO YES
15 15 172.6 152.8 196.0 88.06 NO YES
16 16 177.1 166.6 213.0 83.15 NO YES

Identifying Memory, I/O, Parallel Solver Balance and Performance

Results 3 – Compute Cores Only

w12i-k

“CUBE #10”

Ratio of nonzeroes in factor (min/max) Ratio of flops for factor (min/max) Time (cpu & wall) for numeric factor Time (cpu & wall) for numeric solve Effective I/O rate (MB/sec) for solve Effective I/O rate (GB/sec) for solve No GPU Maximum RAM used in GB
0.9376 0.8399 662.822706 5.609852 19123.88932 19.1 78
0.8188 0.8138 355.367914 3.082555 35301.9759 35.3 85
0.6087 0.6913 283.870728 2.729568 39165.1946 39.2 84
0.3289 0.4771 254.336758 2.486551 43209.70175 43.2 91
0.5256 0.644 191.218882 1.781095 60818.51624 60.8 94
0.5078 0.6805 162.258872 1.751974 61369.6918 61.4 95
0.3966 0.5287 157.315184 1.633994 65684.23821 65.7 96
w16i-k

“CUBE #14”

Ratio of nonzeroes in factor (min/max) Ratio of flops for factor (min/max) Time (cpu & wall) for numeric factor Time (cpu & wall) for numeric solve Effective I/O rate (MB/sec) for solve Effective I/O rate (GB/sec) for solve No GPU Maximum RAM used in GB
0.9376 0.8399 673.225225 6.241678 17188.03613 17.2 78
0.8188 0.8138 368.869242 3.569551 30485.70397 30.5 85
0.6087 0.6913 286.269409 2.828212 37799.17161 37.8 84
0.3289 0.4771 251.115087 2.701804 39767.17792 39.8 91
0.5256 0.644 191.964388 1.848399 58604.0123 58.6 94
0.3966 0.5287 155.623476 1.70239 63045.28808 63.0 96
0.5772 0.6414 147.392121 1.635223 66328.7728 66.3 101
0.6438 0.5701 139.355605 1.484888 71722.92484 71.7 101
0.5098 0.6655 130.042438 1.357847 78511.36377 78.5 103

Results 4 – GPU Acceleration + Cores

w12i-k

“CUBE #10”

Ratio of nonzeroes in factor (min/max) Ratio of flops for factor (min/max) Time (cpu & wall) for numeric factor Time (cpu & wall) for numeric solve Effective I/O rate (MB/sec) for solve Effective I/O rate (GB/sec) for solve % GPU Accelerated The Solve Maximum RAM used in GB
0.9381 0.8405 178.686155 5.516205 19448.54863 19.4 95.78 78
0.8165 0.8108 124.087864 3.031092 35901.34876 35.9 95.91 85
0.6116 0.6893 122.433584 2.536878 42140.01391 42.1 95.74 84
0.3365 0.475 112.33829 2.351058 45699.89654 45.7 95.81 91
0.5397 0.6359 103.586986 1.801659 60124.33358 60.1 95.95 94
0.5123 0.6672 137.319938 1.635229 65751.09125 65.8 85.17 95
0.4132 0.5345 97.252285 1.562337 68696.85627 68.7 95.75 97
w16i-k

“CUBE #14”

Ratio of nonzeroes in factor (min/max) Ratio of flops for factor (min/max) Time (cpu & wall) for numeric factor Time (cpu & wall) for numeric solve Effective I/O rate (MB/sec) for solve Effective I/O rate (GB/sec) for solve % GPU Accelerated The Solve Maximum RAM used in GB
0.9381 0.8405 200.007118 6.054831 17718.44411 17.7 94.96 78
0.8165 0.8108 122.200896 3.357233 32413.68282 32.4 95.20 85
0.6116 0.6893 122.742966 2.624494 40733.2138 40.7 94.91 84
0.3365 0.475 114.618006 2.544626 42223.539 42.2 94.97 91
0.5397 0.6359 105.4884 1.821352 59474.26914 59.5 95.18 94
0.4132 0.5345 96.750618 1.988799 53966.06502 54.0 94.96 97
0.5825 0.6382 106.573973 1.989103 54528.26599 54.5 88.96 101
0.6604 0.566 91.345275 1.374242 77497.60151 77.5 92.21 101
0.5248 0.6534 107.672641 1.301668 81899.85539 81.9 85.07 103

The ANSYS results.out file – The decoding continues

CUBE w12i-k (“CUBE #10”)

  1. Elapsed Time Spent Computing The Solution
    1. This value determines how efficient or balanced the hardware solution for running in distributed parallel solving.
      1. Fastest Solve Time For CUBE 10
    2. 12 out of 12 Cores w/GPU @ 171.3 seconds Time Spent Computing The Solution
  2. Elapsed Time
    1. This value is the actual time to complete the entire solution process. The clock on the wall time.
    2. Fastest Time For CUBE10
      1. 12 out of 12 w/GPU @ 213.0 seconds
  3. CPU Time For Main Thread
    1. This value indicates the RAW number crunching time of the CPU.
    2. Fastest Time For CUBE10
      1. 12 out of 12 w/GPU @186.0 seconds
  4. GPU Acceleration
    1. The NVidia Quadro K6000 accelerated ~96% of the matrix factorization flops
    2. Actual percentage of GPU accelerated flops = 95.7456
  5. Cores and storage solver performance 12 out of 12 cores and using 1 NVidia Quadro K6000
    1. ratio of nonzeroes in factor (min/max) = 0.4132
    2. ratio of flops for factor (min/max) = 0.5345
      1. These two values above indicate to me that the system is well taxed for compute power/hardware viewpoint.
    3. Effective I/O rate (MB/sec) for solve = 68696.856274 (or 69 GB/sec)
      1. No issues here indicates that the workstation has ample bandwidth available for the solving.

CUBE w16i-k (“CUBE #14”)

  1. Elapsed Time Spent Computing The Solution
    1. This value determines how efficient or balanced the hardware solution for running in distributed parallel solving.
    2. Fastest Time For CUBE w16i-k “CUBE #14”
      1. 15 out of 16 Cores w/GPU @ 152.8 seconds
  2. Elapsed Time
    1. This value is the actual time to complete the entire solution process. The clock on the wall time.
    2. CUBE w16i-k “CUBE #14”
      1. 15 out of 16 Cores w/GPU @ 196.0 seconds
  3. CPU Time For Main Thread
    1. This value indicates the RAW number crunching time of the CPU.
    2. CUBE w16i-k “CUBE #14”
      1. 15 out of 16 Cores w/GPU @ 172.6 seconds
  4. GPU Acceleration Percentage
    1. The NVIDIA QUADRO K6000 accelerated ~92% of the matrix factorization flops
    2. Actual percentage of GPU accelerated flops = 92.2065
  5. Cores and storage 12 out of 12 cores and one Nvidia Quadro K6000
    1. ratio of nonzeroes in factor (min/max) = 0.6604
    2. ratio of flops for factor (min/max) = 0.566
      1. These two values above indicate to me that the system is well taxed for compute power/hardware.
    3. Please note that when reviewing these two data points. A balanced solver performance is when both of these values are as close to 1.0000 as possible.
      1. At this point the compute hardware is no longer as efficient and these values will continue to move farther away from 1.0000.
    4. Effective I/O rate (MB/sec) for solve = 77497.6 MB/sec (or ~78 GB/sec)
      1. No issues here indicates that the workstation has ample bandwidth with fast I/O performance for in-core SPARSE Solver solving.
    1. Maximum amount of RAM used by the ANSYS distributed solve
      1. 103GB’s of RAM needed for in-core solve

Conclusions Summary And Upgrade Path Suggestions

It is important for you to locate your bottleneck on your numerical simulation hardware. By utilizing data provided in the ANSYS results.out files, you will be able to logically determine your worst parallel performance inhibitor and plan accordingly on how to resolve what is slowing the parallel performance of your distributed numerical simulation solve.

I/O Bound and/or Compute Bound Summary

  • I/O Bound
    • Both CUBE w12i-k “CUBE #10” and w16i-k “CUBE #14” are I/O Bound.
      • Almost immediately when GPU Acceleration is enabled.
      • When GPU Acceleration is not enabled, I/O bound is no longer an issue compute solving performance. However solve times are impacted due to available and unused compute power.
  • Compute Bound
    • Both CUBE w12i-k “CUBE #10” and w16i-k “CUBE #14” would benefit from additional Compute Power.
    • CUBE w12i-k “CUBE #10” would get the most bang for the buck by adding in the additional compute power.

Upgrade Path Recommendations

CUBE w12i-k “CUBE #10”

  1. I/O:
    1. Hard Drives
    2. Remove & replace the previous generation hard drives
      1. 3.5″ SAS2.0 6Gb/s 15k RPM Hard Drives
    3. Hard Drives could be upgraded to Enterprise Class SSD or PCIe NVMe
      1. COST =  HIGH
    1. Hard Drives could be upgraded to SAS 3.0 12 Gb/s Drives
      1. COST =  MEDIUM
  2.  RAM:
    1. Remove and replace the previous generation RAM
    2. Currently all available RAM slots of RAM are populated.
      1. Optimum slots per these two CPU’s are four slots of RAM per CPU. Currently eight slots of RAM per CPU are installed.
    3. RAM speeds 2133MHz ECC REG DIMM’
      1. Upgrade RAM to DDR4-2400MHz LRDIMM RAM
      2. COST =  HIGH
  3. GPU Acceleration
    1. Install a dedicated GPU Accelerator card such as an NVidia Tesla K40 or K80
    2. COST =  HIGH
  4.  CPU:
    1. Remove and replace the current previous generation CPU’s:
    2. Currently installed dual  x INTEL XEON e5-2643 V3
    3. Upgrade the CPU’s to the V4 (Broadwell) CPU’s
      1. COST =  HIGH

CUBE w16i-k “CUBE #14”

  1. I/O: Hard Drives SAS3.0 15k RPM Hard Drives 12Gbps 2.5”
    1.  Replace the current 2.5” SAS3 12Gb/s 15k RPM Drives with Enterprise Class SSD’s or PCIe NVMe disk
      1. COST =  HIGH
    2. Replace the 2.5″ SAS3 12 Gb/s hard drives with 3.5″ hard drives.
      1. COST =  HIGH
    3. INTEL 1.6TB P3700 HHHL AIC NVMe
      1. Click Here: https://www-ssl.intel.com/content/www/us/en/solid-state-drives/solid-state-drives-dc-p3700-series.html
  2. Currently a total of four Hard Drives are installed
    1. Increase existing hard drive count from four hard drives to a total ofsix or eight.
    2. Change RAID configuration to RAID 50
      1. COST =  HIGH
  3. RAM:
    1. Using DDR4-2400Mhz ECC REG DIMM’s
      1. Upgrade RAM to DDR4-2400MHz LRDIMM RAM
      2. COST =  HIGH

Considering RAM: When determining how much System RAM you need to perform a six million degree of freedom ANSYS numerical simulation. Add the additional amounts to your Maximum Amount of RAM used number indicated in your ANSYS results.out file.

  • ANSYS reserves  ~5% of your RAM
  • Office products can use an additional l ~10-15% to the above number
  • Operating System please add an additional ~5-10% for the Operating System
  • Other programs? For example, open up your windows task manager and look at how much RAM your anti-virus program is consuming. Add for the amount of RAM consumed by these other RAM vampires.

Terms & Definition Goodies:

  • Compute Bound
    • A condition that occurs when your CPU processing power sites idle while the CPU waits for the next set of instructions to calculate. This occurs most often when hardware bandwidth is unable to feed the CPU more data to calculate.
  • CPU Time For Main Thread
    • CPU time (or process time) is the amount of time for which a central processing unit (CPU) was used for processing instructions of a computer program or operating system, as opposed to, for example, waiting for input/output (I/O) operations or entering low-power (idle) mode.
  • Effective I/O rate (MB/sec) for solve
    • The amount of bandwidth used during the parallel distributed solve moving data from storage to CPU input and output totals.
    • For example the in-core 16 core + GPU solve using the CUBE w16i-k reached an effective I//O rate of 82 GB/s.
    • Theoretical system level bandwidth possible is ~96 GB/s
  • IO Bound
    • The ability for the input-output of the system hardware for reading, writing and flow of data pulsing through the system has become inefficient and/or detrimental to running an efficient parallel analysis.
  • Maximum total memory used
    • The maximum amount of memory used by analysis during your analysis.
  • Percentage (%) GPU Accelerated The Solve
    • The percentage of acceleration added to your distributed solve provided by the Graphics Processing Unit (GPU). The overall impact of the GPU will be diminished due to slow and saturated system bandwidth of your compute hardware.
  • Ratio of nonzeroes in factor (min/max)
    • A performance indicator of efficient and balanced the solver is performing on your compute hardware. In this example the solver performance is most efficient when this value is as close to the value of 1.0.
  • Ratio of flops for factor (min/max)
    • A performance indicator of efficient and balanced the solver is performing on your compute hardware. In this example the solver performance is most efficient when this value is as close to the value of 1.0.
  • Time (cpu & wall) for numeric factor
    • A performance indicator used to determine how the compute hardware bandwidth is affecting your solve times. When time (cpu & wall) for numeric factor & time (cpu & wall) for numeric solve values are somewhat equal it means that your compute hardware I/O bandwidth is having a negative impact on the distributed solver functions.
  • Time (cpu & wall) for numeric solve
    • A performance indicator used to determine how the compute hardware bandwidth is affecting your solve times. When time (cpu & wall) for numeric solve & time (cpu & wall) for numeric factor values are somewhat equal it means that your compute hardware I/O bandwidth is having a negative impact on the distributed solver functions.
  • Total Speedup w/GPU
    • Total performance gain for compute systems task using a Graphics Processing Unit (GPU).
  • Time Spent Computing Solution
    • The actual clock on the wall time that it took to compute the analysis.
  • Total Elapsed Time
    • The actual clock on the wall time that it took to complete the analysis.

References:

Phoenix Business Journal: ​Um, the coffee machine needs more water and 5 rules to improve your user interface design game

Usually getting coffee is just getting coffee, but a recent trip turned into some deep thoughts on user interface design.  “​Um, the coffee machine needs more water and 5 rules to improve your user interface design game” explains my encounter with the office caffeine dispenser as well as five key rules that everyone should follow when developing a user interface for a product.