Standard Roof Rack Fairing Mount Getting In Your Way?! Engineer it better and 3D Print it!

It is no mystery that I love my Subaru. I bought it with the intention of using it and I have continually made modifications with a focus on functionality.

When I bought my roof crossbars in order to mount ski and/or bike racks, I quickly realized I needed to get a fairing in order to reduce drag and wind noise. The fairing functions as designed, and looks great as well. However, when I went to install my bike rack, I noticed that the fairing mount was in the way of mounting at the tower. As a result, I had to mount the rack inboard of the tower by a few inches. This mounting position had a few negative results:

  • The bike was slightly harder to load/unload
  • The additional distance from the tower resulted in additional crossbar flex and bike movement
  • Additional interference between bikes when two racks are installed

These issues could all be solved if the fairing mount was simply inboard a few more inches. If only I had access to the resources to make such a concept a reality…. oh wait, PADT has all the capabilities needed to take this from concept to reality, what a happy coincidence!

First, we used our in-house ZEISS Comet L3D scanner to get a digital version of the standard left fairing mount bracket. The original bracket is coated with Talcum powder to aid in the scanning process.

The output from the scanning software is a faceted model in *.STL format. I imported this faceted CAD into ANSYS SpaceClaim in order to use it as a template to create editable CAD geometry to use as a basis to create my revised design. The standard mounting bracket is an injection molded part and is hollow with the exception of a couple of ribs. I made sure to capture all this geometry to carry forward into my redesigned parts, which would make the move to scaled manufacturing of this design easy.

Continuing in ANSYS SpaceClaim, as it is a direct modeling software instead of traditional feature-based modeling, I was able to split the bracket’s two function ends, the crossbar end and fairing end, and offset them by 4.5 inches, in order to allow the bike rack to mount right at the crossbar tower. I used the geometry from the center section CAD to create my offset structure. A mirrored version allows both the driver and passenger side fairing mount to be moved inboard to enable mounting of two bike racks in optimal positions. The next step is to turn my CAD geometry back into faceted *.STL format for printing, which can be done directly within ANSYS SpaceClaim.

 

After the design has been completed, I spoke with our 3D printing group to discuss what technology and material would be good for these brackets, as the parts will be installed on the car during the Colorado summer and winter. For this application, we decided on our in-house Selective Laser Sintering (SLS) SINTERSTATION 2500 PLUS and glass filled nylon material. As this process uses a powder bed when building the parts, no support is needed for overhanging geometry, so the part can be built fully featured. Find out more about the 3D printing technologies available at PADT here.

Finally, it was time to see the results. The new fairing mount offset brackets installed just like the factory pieces, but allowed the installation of the bike rack right at the tower, reducing the movement that was present when mounted inboard, as well as making it easier to load and unload bikes!!

I am very happy with the end result. The new parts assembled perfectly, just as the factory pieces did, and I have increased the functionality of my vehicle yet again. Stay tuned for some additional work featuring these brackets, and I’m sure the next thing I find that can be engineered better! You can find the files on GrabCAD here.

 

Video Tips – Two-way connection between Solidworks and ANSYS HFSS

This video will show you how you can set up a two-way connection between Solidworks and ANSYS HFSS so you can modify dimensions as you are iterating through designs from within HFSS itself. This prevents the need for creating several different CAD model iterations within Solidworks and allows a more seamless workflow.  Note that this process also works for the other ANSYS Electromagnetic tools such as ANSYS Maxwell.

Introducing our new Newsletter: The PADT Pulse

We are very pleased to announce our new newsletter, the PADT Pulse.  For a while now customers have been asking for a monthly update on what is going on without having to go through our blog. So we are taking the best of what we did in a given month and sharing it in this newsletter.

Not only does it have a recap of important activities, it summarizes our most popular blog posts, shares some outside news of interest, and keeps you up to date on our upcoming events. We hope you enjoy it.

Here is a link to the online version.

And you can subscribe here.

New: PADT’s Medical Device Capabilities and Portfolio Presentation

We recently updated our slide presentation on PADT’s Medical Device product development capabilities that includes some examples of past work.  Our team applies proven processes and deep industry experience across a wide spectrum of products.  Please take a look to learn more about how we help companies engineer their medical devices.

PADT-Medical-Overview-Portfolio-2018_02_13-1

You can learn more here and if you have any questins, simply email info@padtinc.com or call 480.813.4884.

In Business Magazine: Five simple strategies for promoting customer satisfaction

How do you make sure that your customers have a great experience?  In “Five simple strategies for promoting customer satisfaction” PADT’s manager of ANSYS Technical Support and Training, Ted Harris, outlines the tools he and his team use to keep PADT’s customer satisfaction rates outstanding.

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.

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:

ANSYS Startup Roadshow Kickoff – CEI Phoenix

Click Here to Register

Click Here to Register

Can’t make it? Keep an eye out as we will be hosting events in other locations as the roadshow continues on!

In the meantime, click here for more information on the ANSYS Startup Program.

250+ Gather to Celebrate Arizona Engineering and Manufacturing at Nerdtoberfest

nerdtoberfest-logo-nt-1-800w

Customers, friends, partners, and students braved 100 degree temperatures and some unusual traffic to gather at PADT’s Tempe office to celebrate engineering and manufacturing in Arizona at Nerdtoberfest.  Machinists, startup experts, engineers, and professors mingled under the stars and took a tour of the facilities while enjoying pizza and beer.

The day started with a seminar on Metal 3D Printing given by Dr. Dhruv Bhate.  If you missed it, you can watch his talk here:

We followed that with the first ever PADT Perfect Pitch competition, where four teams pitched the same fictitious company as an exercise in seeing if those who teach, can do.  That was such a big part of the day that it has it’s own blog post including a link to a video of all of the pitches.

And after the the laughing and congratulations to the winner of the Unicorn Cup, we started the open house.  A chance to tour PADT and network with other members of the Arizona Tech Community.

If you have ever read a post before about one of our open houses you know we have a consistent problem. Once the party starts we stop taking pictures. The only one I got was of Dhruv showing off our new Laser Concepts Metal 3D Printer.

img_2197

That room was definitely the star of the show and we calculated that Dhruv was talking from 3:00 to 8:30 – five and a half hours non-stop.  He earned his pizza and beer.

The table from Basis Chandler was also popular, where they talked about their 3D Printed prosthetic hand project.  We also had representatives from the SciTech Festival and RevAZ talking to visitors.  The 3D Printing demo room was great and many people stopped to hear about how we are combining 3D Printing and ANSYS Simulation.

We always enjoy these events, they give us a chance to socialize with people we see all the time in work situations.  It is also a great opportunity for us to introduce people that would probably otherwise not meet, and grow the strength of the Arizona engineering and manufacturing ecosystem.

First Perfect Pitch Startup Presentation Competition a Success – CEI Takes Home the Unicorn Cup

perfect-pitch-16-all-2The verdict is in, if the company barq! actually existed they would have raised a lot of seed money yesterday.  Members of the Phoenix area startup community gathered at PADT to try out a new idea: what if the experts who mentor and coach startups tried their hands at pitching a company?  The result was fun, funny, and educational.

title-slides-perfect-pitch-2017Local incubators/accelerators CEI, Seed Spot, and Tallwave joined PADT in pitching a totally made up company, barqk! to a group of judges who are startup experts.  We talked about poop, doggy depression, bessel functions, big data, valuations, and the cat revolt. In the end we ended up with four fantastic examples of how to pitch a company and how to answer questions from investors.  One of the best parts was that every single team finished their pitch in the 10 minutes they were given, and they covered everything that needed to be covered. Yes, it can be done!

And the winner is… The Center for Entrepreneurial Innovation (CEI).  Tom Schumann and Patti DuBois told a story, explained the product, and got across the value to the investors of the product

.  perfect-pitch-16-winners-text

You can watch the recording of the presentations in the video below.  Take some time to watch the pitches and get a feel for barqk!, and how different organizations approach telling the story and more importantly, attracting investors.  The audience noticed that each team had a unique take that represented their strengths.

Our judges were Jim Goulka from Arizona Technology Investors, Christie Kerner from ASU, Carine Dieude of Altima Business Solutions, and Linda Capcara with TechTHiNQ, and they did a fantastic job, especially with keeping a straight face when the contestants responded with some very inventive responses. Their contribution was important.

If you are interested in doing a similar event, here is some background information:

barqk-logo-200-1Rules:

  • Each team gets a copy of the angel group funding application and a logo.
  • Each team gets 10 minutes to pitch
  • The judges have up to 5 minutes to ask questions
  • The other presenters can listen in
  • PowerPoint slides are allowed
  • Some variation from the company application is allowed for humor or to fill gaps, but everyone should stick to the same basic material

Here is their angel funding application, everything you need to know about them is in there: barqk-angel-application-1.pdf

We look forward to doing this again, hopefully as part of a larger startup event. Thank you to all who participated by pitching, judging, or being in the audience.

photo-oct-27-4-24-33-pm
Who will win the Unicorn Cup next?

Nerdtoberfest: Printing a Beer Stein with Beer Filament

Noticed an interesting email in my inbox the other day with the subject line:

“Oktoberfest Time: 3D Print a Beer Stein in Beer Filament”

Marketing gold, you have my attention!

After reading the reviews from the filament manufacturer, I dove in and got some of the hoppy, malty filament on order from 3D Fuel. I was very excited when it came in and couldn’t wait to print PADT’s own beer stein for our upcoming Nerdtoberfest event. Meanwhile I found a nice starting point with a file from GrabCad and added my own additions and alterations.

cad

I quickly went to load the beer filament into one of our 3D printers, when I noticed that the roll size was not compatible with the spool holder on the printer. It was this disconnect that would have previously stopped this experiment in it’s track, however, the future is NOW!

I popped onto the Thingiverse, and alas, I was not alone in having this issue and a plethora of solution were populated before me. I was about to 3D print and adapter to allow my 3D printer to accept a new roll size that was found to be incompatible just moments before. Disaster averted, I was now cooking with gas, er, beer.

holder

roll-on-holder

The printing process was uneventful and the beer filament printed well. We now have a beer mug printed out of beer filament for PADT’s annual Nerdtoberfest!

4

mug-views

img_7587-copy

giphy

ANSYS Breakthrough Energy Innovation Campaign is live!

As the worldwide demand for energy continues to grow every year, energy systems simulation is becoming an indispensable tool for improving the way energy is produced and consumed. At the same time, concerns about climate change are leading to stricter emissions regulations and calls for sustainable design in all future energy systems. Clearly, breakthroughs in energy innovation are needed to meet these formidable challenges.

Join PADT in exploring the impact of breakthrough energy innovation as well as how ANSYS simulation solutions can be used to help combat the challenges that this area presents.

This campaign covers five main topics:

  1. Advanced Electrification
  2. Machine & Fuel Efficiency
  3. Effective Lightweighting
  4. Thermal Optimization
  5. Aerodynamic Design
Information on each topic will be released over the course of the next few months as the webinars take place.
The campaign will consist of a series of webinars explaining the applications of ANSYS simulations software with regards to each topic, along with additional downloadable content.

Sign Up Now to receive updates regarding the campaign, including additional information on each subject, registration forms to each webinar and more.

More information regarding the campaign in general can be found Here.

artwork-for-bei-campaign

Investigation Signal Integrity: How to find problems before they find you – Webinar

In the Age of IoT, electronics continue to get smaller, faster, more power efficient, and are integrated into everything around us. Increasingly, companies are incorporating simulation early in the product development process, when the cost of design changes are at their lowest, to meet the challenges presented by Signal Integrity. For this to be effective, simulation tools need to be easy-to-use, compatible with existing work flows, and accurate, all while delivering meaningful results quickly.

If you or your company are designing or using electronics that are:
Critical to revenue, performance, or safety
Getting smaller, faster, or more efficient
Communicating with Gbps data rates
Using several or new connectors
Using long cables or backplanes
Then you could be a victim of Signal Integrity failure!

Join us September 7th, 2016 at 1 pm Pacific Time for this free webinar to discover how ANSYS is delivering intuitive Signal Integrity analysis solutions that can easily import ECAD geometry to compute SYZ parameters, inter-trace coupling, or impedance variations. Learn how ANSYS can help identify Signal Integrity problems and optimize potential solutions faster and cheaper than prototyping multiple iterations.

This webinar will introduce:

  • What products ANSYS provides for Signal Integrity problems
  • How these products can integrate into existing design workflows
  • And how easy these products are to use, even for novice operators

Followed by a Q&A session!

Click Here to register for this event and be sure to add it to your calendar to receive reminders.

Can’t make it? We suggest you register regardless, as our webinars are recorded and sent out along with a PDF of the presentation to our contacts within 24 hours of the presentation finishing.

ANSYS AIM Webinar: Increase Simulation Realism with Multiphysics

Some product designs require a single physics solution, while others require multiple physics simulations. Electronics cooling, wind loading on a solar array and the thermal performance of a heat exchanger are just a few examples of applications that require multiphysics simulation. Setting up and running multiphysics simulations used to be a challenging task involving the transfer of data between multiple physics solvers. With AIM, multiphysics simulations are easy to perform. AIM provides a consistent workflow and intuitive simulation environment for fluids, structures and electromagnetics that lowers the barrier to entry for multiphysics simulations.

 

Join us for this webinar to discover how AIM makes it easier than ever to solve your multiphysics design challenges in a single, easy-to-use environment. Don’t settle for single physics approximation when multiphysics simulations yield more accurate results with AIM.

This webinar will be held on September 1st from 1:00 – 2:00 pm PT 
Click Here to register for this webinar
AIM Webinar Title Page3

ANSYS AIM Webinar: Democratize Simulation for Your Design Engineers

Innovative companies are using simulation early in the product development process to improve and optimize product designs. Companies deploying up-front simulation to their product design teams require simulation software that is easy-to-use, provides accurate simulation results and allows customization to enforce best practices. Such design engineering simulation software allows teams to develop and refine design ideas early in the design cycle when the cost of making design changes is still low.

Join us for this webinar to discover how AIM’s intuitive simulation workflow delivers high levels of automation and allows customization to automate engineering simulation best practice. Learn how AIM’s custom applications enable every engineer in your organization to benefit from simulation insights.
This webinar will be held on August 24th from 1:00 pm – 2:00 pm PT

 

Click Here to register for this webinar

AIM Webinar Title Page2