Bringing Digital Manufacturing to Market

By Nicole Hemsoth

October 6, 2010

Last week in Washington, D.C., the National Center for Manufacturing Sciences (NCMS) and Intersect 360 Research presented the results of a survey based on over 300 manufacturing firms in the United States about the current state of digital manufacturing technologies.  More specifically, the questions were aimed at ultimately identifying the key barriers and drivers for their adoption of complex technologies to drive innovation.

While we will touch on some of the findings of the research and its potential implications in a while, it should come as no surprise that the “missing middle” designation became a key, guiding phrase. Many in the HPC community have already heard this term repeated elsewhere, it is not always a common, understood concept for those who hold the purse strings—namely representatives in the U.S. government, some of whom might not have even been familiar with the broad domain of high-performance computing and the many layers of meaning and technology that comprise it.

 The goal of the research and presentation was to convince government leaders of the inherent value of making high-performance computing software and resources accessible to the manufacturing sector from the bottom up. Without having access to core technologies, particularly in the realm of modeling and simulation, many smaller design and manufacturing shops have a hard time remaining competitive—and we all know what this “trickle up” impact is on the national economy.

Again, chances are you’re well aware of the concept of the missing middle, but let’s take it one step further and enter the realm of manufacturing, HPC and this presumably vast, lost subset of the HPC-denied American economy.

The Digital Manufacturing Angle

The concept of digital manufacturing itself can appear, at first, as a bit too broad or nebulous, in part because from first glance, it implies that the final product is digital in nature or otherwise not tangible enough to apply to something as solid as manufactured products. Digital manufacturing, however, refers to the entire lifecycle of a design or product that was based on the use of advanced computational resources and technologies to deploy simulation and modeling for multiple aspects of the design and development process. As Intersect 360 Research notes, “by creating a digital model of a product, a manufacturer can perform a wide range of tests, such as manufacturability analysis or performance testing, before physically building a new design.” It is in this total solution based on technology that digital manufacturing as a term is best applied.

What this overarching concept of digital manufacturing ultimately means is that companies who feed the manufacturing supply chain are able to improve their final product through refined design and testing efforts and furthermore, many are able to speed the time to market for their products since testing engineered parts or complete products can be time-consuming and expensive.

One of the better ways to think about digital manufacturing is to consider it in the case of a manufacturing firm at the top of the food chain, heavy-equipment maker Caterpillar.

Feeding a Caterpillar

Although not a missing middle company by any stretch of the imagination, providing an overview of how HPC does work for refining the product lifecycle (and the challenges that are present when the HPC absent from it) can almost be better realized via a case study of a company that is thriving versus one of the many missing middles who plod along with legacy systems and 2D rendering software. After all, we know what their challenges are.  But for a massive manufacturer who has both high-end systems yet still occasionally resorts to older, more expensive testing and design methods, we have a more thorough perspective.

Fortune 50 heavy equipment manufacturer Caterpillar’s research program manager for virtual products, Keven Hoftstetter, highlighted the key benefits and challenges of modeling and simulation for the company’s product cycle and bottom line last week during the manufacturing and HPC-related HPC 360 event in Champaign-Urbana, Illinois. The equipment manufacturer is a top-tier user of HPC, thus they certainly do not fall into the “missing middle” that has been clearly defined for the manufacturing sector and includes the smaller firms that support the supply chain that migrates to a summit that would include a company like Caterpillar.

Caterpillar is a company on the bleeding edge of modeling and simulation for manufacturing, both on a software and hardware/GPU level. Caterpillar places significant emphasis on research and development projects to helps refine product development and bring their line of equipment to customer as well as environmental standards. In addition to the core elements of their manufacturing business, they also house other broad divisions handling equipment financing, logistics, and remanufacturing/rebuilding.

As one might imagine, Caterpillar’s needs go far beyond modeling or simulating the machine functions since there are many parts that are required in advance, all of which much operate a peak performance, both individually and inside the specific machine. Accordingly, research and development at Caterpillar is the backbone of profitability on the micro level (testing pistons, for instance) to the macro level (making sure an earth moving vehicle performs on target).

While Caterpillar’s basic product cycle model is the same as many other manufacturing companies (concept; design, then on to the build and test phase, and finally the production phase) the products that they are developing and testing are on the massive scale. It is not feasible to build giant centers to house and test the actual prototypes in a repeated, 24×7 manner as they have been doing before taking advantage of simulation to handle their design and test process. Hoftstetter noted that they built a 10-acre facility to test their earth moving equipment but if they had to continue to build out centers like this they would be unable to compete.

Caterpillar also devotes a significant amount of time and resources into the many parts and components that are critical to their earth moving equipment. For instance, Hofstetter noted that since they are a leader in diesel and natural gas engines and industrial gas turbines a great deal of their research and development efforts are related to computational fluid dynamics and combustion system interaction.

Since it would not be viable for a company like Caterpillar, who sits at the top of the supply chain and has far more resources to continue to competitively develop and test its products without sophisticated modeling and simulation software and the resources required to power it, why would it make sense to think that smaller companies who help drive Caterpillar by providing components of its larger parts and final products can scavenge enough resources?

Since it all feeds into the top, if there could be a way to empower those at the lower end of the supply chain, let’s say a small engine parts maker for Caterpillar, why wouldn’t it make sense to encourage this? Let’s say for example this small, hypothetical parts design and manufacturing company could deliver high-quality products at a lower cost to Caterpillar, all due to a dramatic reduction in development and time-to-market periods because of the boost of added HPC capacity or even first-time HPC software and resource capability?

This is what lies at the heart of all of these “missing middle” debates, yet still there are no answers on how to best reach out to these smaller providers of manufactured designs and products when all they really need are the resources. And as you know, these are some very expensive resources we’re talking about here.

While HPC on demand providers are putting themselves forward as the next best thing to an in-house cluster, there are still hefty software license issues to contend with that will still drive up the cost. On the other but related side, there are cloud services providers boasting superior services with a performance hit that won’t (they will promise) be dramatic.

While nothing seems to appeal to most quite like the good old workstation for modeling and simulation tasks and since the GPU revolution is still just in its infancy in terms of HPC resource providers with affordable solutions, one has to wonder how much longer this missing middle in manufacturing will remain lost.

Research in Context

So let’s get back to the Intersect 360 and NCMS research that started this whole conversation in the first place. Actually, let’s back way up…

What is rather unique about the study was that the 321 respondents were not told that the survey had anything to do with HPC. As Addison Snell noted during a presentation similar to the one he gave in conjunction with NCMS in Washington, D.C. the day before, in order to take care of the sticky issue of sample bias, the focus was on technology as a general concept versus the far narrower HPC distinction.

This does mark the study as different from several others that have emerged that are distinctly related to high-performance computing. However, if potential respondents were asked to take the survey, even if there was the possibility of marking an answer “we do not currently use HPC,” they might be far less likely to objectively consider the questions.

As it stands, 80% of the respondents came from the industrial or commercial manufacturing space (with the remaining 20% in supporting roles in academia, trade organizations and the public sector) and those on the commercial end of the spectrum were asked additional questions related to product design and development and to what extent they were deploying available high-end technologies to aid in their efforts, among other related questions. 

While the above numbers will do you far more good if you take time to unravel some of the study’s finer points (and the important elements have only been hinted at–there are a number of sub-issues) about the distribution of opinions about securing access to advanced technologies (don’t call them HPC resources if that makes it more palatable, of course) the main point is that there is a combination of general resistance and lack of insight about how these technologies can be leveraged (and to what benefit) for those in that missing middle of manufacturing.

What the research found was that “there is potential, untapped benefit to digital manufacturing technology usage among U.S. manufacturers, particularly small to mid-sized manufacturers. For these companies to get over the hurdles inherent to adoption of advanced technologies, they will seek partners and programs that mitigate risk and help defray costs so they can make investments required to improve their competitiveness technologically.”

Still, what gives some pause is to consider the inferior technology that is driving many manufacturers feeding the supply chain. If we see Caterpillar as a representative example of the power of having access to modeling and simulation, except of the vast scale, it’s pertinent to apply the example of them building a 10-acre facility to test pre-built testable equipment to run constantly for days on end when software might have taken over the task.”

Although companies further down the supply chain don’t have the same sized products or design challenges, this example reverberates—when scaled down, what many of these smaller companies are doing is the cost, time, and resource equivalent of the 10-acre test lot.
 

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

Graphcore Readies Launch of 16nm Colossus-IPU Chip

July 20, 2017

A second $30 million funding round for U.K. AI chip developer Graphcore sets up the company to go to market with its “intelligent processing unit” (IPU) in 2017 with scale-up production for enterprise datacenters and Read more…

By Tiffany Trader

Fine-Tuning Severe Hail Forecasting with Machine Learning

July 20, 2017

Depending on whether you’ve been caught outside during a severe hail storm, the sight of greenish tinted clouds on the horizon may cause serious knots in the pit of your stomach, or at least give you pause. There’s g Read more…

By Sean Thielen

Trinity Supercomputer’s Haswell and KNL Partitions Are Merged

July 19, 2017

Trinity supercomputer’s two partitions – one based on Intel Xeon Haswell processors and the other on Xeon Phi Knights Landing – have been fully integrated are now available for use on classified work in the Nationa Read more…

By HPCwire Staff

Fujitsu Continues HPC, AI Push

July 19, 2017

Summer is well under way, but the so-called summertime slowdown, linked with hot temperatures and longer vacations, does not seem to have impacted Fujitsu's output. The Japanese multinational has made a raft of HPC and A Read more…

By Tiffany Trader

HPE Extreme Performance Solutions

HPE Servers Deliver High Performance Remote Visualization

Whether generating seismic simulations, locating new productive oil reservoirs, or constructing complex models of the earth’s subsurface, energy, oil, and gas (EO&G) is a highly data-driven industry. Read more…

Researchers Use DNA to Store and Retrieve Digital Movie

July 18, 2017

From abacus to pencil and paper to semiconductor chips, the technology of computing has always been an ever-changing target. The human brain is probably the computer we use most (hopefully) and understand least. This mon Read more…

By John Russell

The Exascale FY18 Budget – The Next Step

July 17, 2017

On July 12, 2017, the U.S. federal budget for its Exascale Computing Initiative (ECI) took its next step forward. On that day, the full Appropriations Committee of the House of Representatives voted to accept the recomme Read more…

By Alex R. Larzelere

Summer Reading: IEEE Spectrum’s Chip Hall of Fame

July 17, 2017

Take a trip down memory lane – the Mostek MK4096 4-kilobit DRAM, for instance. Perhaps processors are more to your liking. Remember the Sh-Boom processor (1988), created by Russell Fish and Chuck Moore, and named after Read more…

By John Russell

Women in HPC Luncheon Shines Light on Female-Friendly Hiring Practices

July 13, 2017

The second annual Women in HPC luncheon was held on June 20, 2017, during the International Supercomputing Conference in Frankfurt, Germany. The luncheon provides participants the opportunity to network with industry lea Read more…

By Tiffany Trader

Graphcore Readies Launch of 16nm Colossus-IPU Chip

July 20, 2017

A second $30 million funding round for U.K. AI chip developer Graphcore sets up the company to go to market with its “intelligent processing unit” (IPU) in Read more…

By Tiffany Trader

Fine-Tuning Severe Hail Forecasting with Machine Learning

July 20, 2017

Depending on whether you’ve been caught outside during a severe hail storm, the sight of greenish tinted clouds on the horizon may cause serious knots in the Read more…

By Sean Thielen

Fujitsu Continues HPC, AI Push

July 19, 2017

Summer is well under way, but the so-called summertime slowdown, linked with hot temperatures and longer vacations, does not seem to have impacted Fujitsu's out Read more…

By Tiffany Trader

Researchers Use DNA to Store and Retrieve Digital Movie

July 18, 2017

From abacus to pencil and paper to semiconductor chips, the technology of computing has always been an ever-changing target. The human brain is probably the com Read more…

By John Russell

The Exascale FY18 Budget – The Next Step

July 17, 2017

On July 12, 2017, the U.S. federal budget for its Exascale Computing Initiative (ECI) took its next step forward. On that day, the full Appropriations Committee Read more…

By Alex R. Larzelere

Women in HPC Luncheon Shines Light on Female-Friendly Hiring Practices

July 13, 2017

The second annual Women in HPC luncheon was held on June 20, 2017, during the International Supercomputing Conference in Frankfurt, Germany. The luncheon provid Read more…

By Tiffany Trader

Satellite Advances, NSF Computation Power Rapid Mapping of Earth’s Surface

July 13, 2017

New satellite technologies have completely changed the game in mapping and geographical data gathering, reducing costs and placing a new emphasis on time series Read more…

By Ken Chiacchia and Tiffany Jolley

Intel Skylake: Xeon Goes from Chip to Platform

July 13, 2017

With yesterday’s New York unveiling of the new “Skylake” Xeon Scalable processors, Intel made multiple runs at multiple competitive threats and strategic Read more…

By Doug Black

Google Pulls Back the Covers on Its First Machine Learning Chip

April 6, 2017

This week Google released a report detailing the design and performance characteristics of the Tensor Processing Unit (TPU), its custom ASIC for the inference Read more…

By Tiffany Trader

Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Just how close real-wo Read more…

By John Russell

HPC Compiler Company PathScale Seeks Life Raft

March 23, 2017

HPCwire has learned that HPC compiler company PathScale has fallen on difficult times and is asking the community for help or actively seeking a buyer for its a Read more…

By Tiffany Trader

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

Trump Budget Targets NIH, DOE, and EPA; No Mention of NSF

March 16, 2017

President Trump’s proposed U.S. fiscal 2018 budget issued today sharply cuts science spending while bolstering military spending as he promised during the cam Read more…

By John Russell

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

In this contributed perspective piece, Intel’s Jim Jeffers makes the case that CPU-based visualization is now widely adopted and as such is no longer a contrarian view, but is rather an exascale requirement. Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a Read more…

By Tiffany Trader

Facebook Open Sources Caffe2; Nvidia, Intel Rush to Optimize

April 18, 2017

From its F8 developer conference in San Jose, Calif., today, Facebook announced Caffe2, a new open-source, cross-platform framework for deep learning. Caffe2 is the successor to Caffe, the deep learning framework developed by Berkeley AI Research and community contributors. Read more…

By Tiffany Trader

Leading Solution Providers

How ‘Knights Mill’ Gets Its Deep Learning Flops

June 22, 2017

Intel, the subject of much speculation regarding the delayed, rewritten or potentially canceled “Aurora” contract (the Argonne Lab part of the CORAL “ Read more…

By Tiffany Trader

Reinders: “AVX-512 May Be a Hidden Gem” in Intel Xeon Scalable Processors

June 29, 2017

Imagine if we could use vector processing on something other than just floating point problems.  Today, GPUs and CPUs work tirelessly to accelerate algorithms Read more…

By James Reinders

MIT Mathematician Spins Up 220,000-Core Google Compute Cluster

April 21, 2017

On Thursday, Google announced that MIT math professor and computational number theorist Andrew V. Sutherland had set a record for the largest Google Compute Engine (GCE) job. Sutherland ran the massive mathematics workload on 220,000 GCE cores using preemptible virtual machine instances. Read more…

By Tiffany Trader

Google Debuts TPU v2 and will Add to Google Cloud

May 25, 2017

Not long after stirring attention in the deep learning/AI community by revealing the details of its Tensor Processing Unit (TPU), Google last week announced the Read more…

By John Russell

Russian Researchers Claim First Quantum-Safe Blockchain

May 25, 2017

The Russian Quantum Center today announced it has overcome the threat of quantum cryptography by creating the first quantum-safe blockchain, securing cryptocurrencies like Bitcoin, along with classified government communications and other sensitive digital transfers. Read more…

By Doug Black

Groq This: New AI Chips to Give GPUs a Run for Deep Learning Money

April 24, 2017

CPUs and GPUs, move over. Thanks to recent revelations surrounding Google’s new Tensor Processing Unit (TPU), the computing world appears to be on the cusp of Read more…

By Alex Woodie

Six Exascale PathForward Vendors Selected; DoE Providing $258M

June 15, 2017

The much-anticipated PathForward awards for hardware R&D in support of the Exascale Computing Project were announced today with six vendors selected – AMD Read more…

By John Russell

Top500 Results: Latest List Trends and What’s in Store

June 19, 2017

Greetings from Frankfurt and the 2017 International Supercomputing Conference where the latest Top500 list has just been revealed. Although there were no major Read more…

By Tiffany Trader

  • arrow
  • Click Here for More Headlines
  • arrow
Share This