A Platform for Smart Manufacturing

By Tiffany Trader

September 25, 2014

21st century market dynamics put a great deal of pressure on manufacturers to operate differently. Driving forces are many but include the need to satisfy customer demands quickly and to deal with energy constraints and environmental concerns. On the flip side, the growth of network technology in tandem with service-oriented architectures can have a transformative effect by providing real-time insight into manufacturing processes. This is the basis of smart manufacturing, which applies networked information-based technologies throughout the manufacturing and supply chain enterprise to achieve increased efficiency, productivity, competitive advantage, and ultimately better ROI.

When it comes to the role of HPC in manufacturing, much of the focus has been given to virtual design and prototyping, using computer modeling and simulation for product design and improvement. Smart manufacturing cuts a wider path, leveraging data and information to enable proactive and intelligent manufacturing decisions.

To find out more about this emerging paradigm, HPCwire spoke with Jim Davis, CIO of UCLA and cofounder of the Smart Manufacturing Leadership Coalition (SMLC), an organization that is driving standards in processes and developing the nation’s first open smart manufacturing platform.

“We still interface with the design side,” Davis said, “but our emphasis is on the real-time nature of manufacturing. We’re interested in real-time data, the real-time use of computation/analytics, the orchestration of the software into actionable forms that are interfacing with automation and control, or with real-time decision-making or with real-time events at the supply-chain level. So the notion of time, real-time, actionable tasks and decision-making are what distinguishes smart manufacturing from the design chain.”

Davis goes on to explain that when his group began looking at a number of different industry segments, a common theme emerged that they needed the ability to access computation/analytics in a much better way. They needed to be able to scale IT infrastructure; they needed the connectors to interface with automation and control or factory platforms in a better way, but at the same time, they needed to be able to merge data and orchestrate it for broader kinds of metrics that would extend across offices, or supply chains, or operations.

“This took us down the path of platform technology, and led to the development of our Smart Manufacturing Platform,” said Davis. “We’ve been looking at a whole set of services that allows the computation analytics and so forth to be accessed at scale for real-time actionable use.”

“At the platform level, there is quite a bit of overlap with the design, and in fact the design models make really good sense of the manufacturing space and vice versa but design is distinctly different from manufacturing the actually delivery.”

Last year, SMLC won a Department of Energy contract to develop the nation’s first open smart manufacturing technology platform for collaborative industrial networked information applications. The first two test beds funded by the $10 million award are at a General Dynamics Army Munitions plant to optimize heat treating furnaces and at a Praxair Hydrogen Processing plant to optimize steam methane reforming furnaces. The test bed project technologies stand to reduce annual generation of CO2 emissions by 69 million tons, and waste heat by 1.3 quads, or approximately 1.3 percent of total US energy use.

In the case of the steam methane reforming furnace, Davis explains that managing the furnace and its energy use in a better way is a good fit for a high-fidelity computational fluid dynamics model. Understanding flow and heat distribution characteristics within the furnace has been difficult because the harshness of the furnace environment tends to preclude sensor placement. Now project participants are working to put infrared cameras around the furnace that allows the internals of the furnace to be measured and visualized on a real-time basis. Then they’re taking that data and bringing it together with other measurements using a computational fluid dynamics model to predict the overall heat distribution, optimize it and then update a control model that’s running the plant.

“We use a computational-fluid dynamics model to predict and update parameters in a control model and that allows us to run this in real time,” Davis explained. “There’s a substantial energy savings by using the high-fidelity model.”

The team is doing the model development using a 12,000 core UCLA cluster. While the application isn’t optimized to use all the cores, there are sufficient computational resources such that compute times went from a matter of days and weeks down to just hours, tractable ranges from a process standpoint.

SMLC is also working with another company that fabricates metal parts, and this plant involves heating and foraging steps, heat treatment steps, followed by shaping and machining steps. By using modeling to achieve the right metallurgical properties, the company saves on the machining maintenance, machine time, and machine utilities. Doing it this way also saves energy in the heat treatment process.

“This is an example of a discrete process where we’re actually able to save electricity and gas/fuel-based energy in substantial ways and at the same time improve the production and quality of the product,” said Davis.

SMLC analyzed across industries, across manufacturing structures, and across problems, and put together a set of requirements which were used to spec the Smart Manufacturing Platform. The platform is based on the services infrastructure developed by Nimbis Services, the originator of the cloud-based technical computing marketplace. SMLC added a unique workflow-as-a-service layer that allows companies to select and put together different components ranging from ‘how do I collect data?’ to ‘how do I analyze it?’ and finally ‘how do I interface it back with the plant?’ Put another way, the workflow-as-a-service layer arranges a series of pieces of code into an organized format that can be put into actionable use.

SM Platform Apps Store

SMLC and its partners are now in the process of building out the platform against specific test beds in automotive, food, ammunition, gas, refining, chemicals, and pharmaceuticals. The prototype contains a vertical stack of all the services – the computational and storage layers, the cloud management layers, and the workflow-as-a-service layer – and it has the ability to bring those environments together. The next step is building out capabilities and robustness within each of the layers, so for the cloud management layer, for example, they will be implementing OpenStack.

“We’re seeing a set of tools that are relatively invariant across companies,” said Davis, “these tools have to do with access to computational resources, the ability to spin up and down instances of computation. The platform is basically architecting out those invariant elements and leaving a layer called the smart manufacturing marketplace, a layer in which the companies can come in and select different components that they need that are specific to their own uses and missions.”

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!

Battle Brews over Trump Intentions for Funding Science

February 27, 2017

The battle over science funding – how much and for what kinds of science – Read more…

By John Russell

Google Gets First Dibs on New Skylake Chips

February 27, 2017

As part of an ongoing effort to differentiate its public cloud services, Google made good this week on its intention to bring custom Xeon Skylake chips from Intel Corp. Read more…

By George Leopold

Thomas Sterling on CREST and Academia’s Role in HPC Research

February 27, 2017

The US advances in high performance computing over many decades have been a product of the combined engagement of research centers in industry, government labs, and academia. Read more…

By Thomas Sterling, Indiana University

Advancing Modular Supercomputing with DEEP and DEEP-ER Architectures

February 24, 2017

Knowing that the jump to exascale will require novel architectural approaches capable of delivering dramatic efficiency and performance gains, researchers around the world are hard at work on next-generation HPC systems. Read more…

By Sean Thielen

HPE Extreme Performance Solutions

Manufacturers Reaping the Benefits of Remote Visualization

Today’s manufacturers are operating in an ever-changing atmosphere, and finding new ways to boost productivity has never been more vital.

This is why manufacturers are ramping up their investments in high performance computing (HPC), a trend which has helped give rise to the “connected factory” and Industrial Internet of Things (IIoT) concepts that are proliferating throughout the industry today. Read more…

Weekly Twitter Roundup (Feb. 23, 2017)

February 23, 2017

Here at HPCwire, we aim to keep the HPC community apprised of the most relevant and interesting news items that get tweeted throughout the week. Read more…

By Thomas Ayres

HPE Server Shows Low Latency on STAC-N1 Test

February 22, 2017

The performance of trade and match servers can be a critical differentiator for financial trading houses. Read more…

By John Russell

HPC Financial Update (Feb. 2017)

February 22, 2017

In this recurring feature, we’ll provide you with financial highlights from companies in the HPC industry. Check back in regularly for an updated list with the most pertinent fiscal information. Read more…

By Thomas Ayres

Rethinking HPC Platforms for ‘Second Gen’ Applications

February 22, 2017

Just what constitutes HPC and how best to support it is a keen topic currently. Read more…

By John Russell

Thomas Sterling on CREST and Academia’s Role in HPC Research

February 27, 2017

The US advances in high performance computing over many decades have been a product of the combined engagement of research centers in industry, government labs, and academia. Read more…

By Thomas Sterling, Indiana University

Advancing Modular Supercomputing with DEEP and DEEP-ER Architectures

February 24, 2017

Knowing that the jump to exascale will require novel architectural approaches capable of delivering dramatic efficiency and performance gains, researchers around the world are hard at work on next-generation HPC systems. Read more…

By Sean Thielen

HPC Technique Propels Deep Learning at Scale

February 21, 2017

Researchers from Baidu’s Silicon Valley AI Lab (SVAIL) have adapted a well-known HPC communication technique to boost the speed and scale of their neural network training and now they are sharing their implementation with the larger deep learning community. Read more…

By Tiffany Trader

IDC: Will the Real Exascale Race Please Stand Up?

February 21, 2017

So the exascale race is on. And lots of organizations are in the pack. Government announcements from the US, China, India, Japan, and the EU indicate that they are working hard to make it happen – some sooner, some later. Read more…

By Bob Sorensen, IDC

TSUBAME3.0 Points to Future HPE Pascal-NVLink-OPA Server

February 17, 2017

Since our initial coverage of the TSUBAME3.0 supercomputer yesterday, more details have come to light on this innovative project. Of particular interest is a new board design for NVLink-equipped Pascal P100 GPUs that will create another entrant to the space currently occupied by Nvidia's DGX-1 system, IBM's "Minsky" platform and the Supermicro SuperServer (1028GQ-TXR). Read more…

By Tiffany Trader

Tokyo Tech’s TSUBAME3.0 Will Be First HPE-SGI Super

February 16, 2017

In a press event Friday afternoon local time in Japan, Tokyo Institute of Technology (Tokyo Tech) announced its plans for the TSUBAME3.0 supercomputer, which will be Japan’s “fastest AI supercomputer,” Read more…

By Tiffany Trader

Drug Developers Use Google Cloud HPC in the Fight Against ALS

February 16, 2017

Within the haystack of a lethal disease such as ALS (amyotrophic lateral sclerosis / Lou Gehrig’s Disease) there exists, somewhere, the needle that will pierce this therapy-resistant affliction. Read more…

By Doug Black

Azure Edges AWS in Linpack Benchmark Study

February 15, 2017

The “when will clouds be ready for HPC” question has ebbed and flowed for years. Read more…

By John Russell

For IBM/OpenPOWER: Success in 2017 = (Volume) Sales

January 11, 2017

To a large degree IBM and the OpenPOWER Foundation have done what they said they would – assembling a substantial and growing ecosystem and bringing Power-based products to market, all in about three years. Read more…

By John Russell

US, China Vie for Supercomputing Supremacy

November 14, 2016

The 48th edition of the TOP500 list is fresh off the presses and while there is no new number one system, as previously teased by China, there are a number of notable entrants from the US and around the world and significant trends to report on. Read more…

By Tiffany Trader

Lighting up Aurora: Behind the Scenes at the Creation of the DOE’s Upcoming 200 Petaflops Supercomputer

December 1, 2016

In April 2015, U.S. Department of Energy Undersecretary Franklin Orr announced that Intel would be the prime contractor for Aurora: Read more…

By Jan Rowell

D-Wave SC16 Update: What’s Bo Ewald Saying These Days

November 18, 2016

Tucked in a back section of the SC16 exhibit hall, quantum computing pioneer D-Wave has been talking up its new 2000-qubit processor announced in September. Forget for a moment the criticism sometimes aimed at D-Wave. This small Canadian company has sold several machines including, for example, ones to Lockheed and NASA, and has worked with Google on mapping machine learning problems to quantum computing. In July Los Alamos National Laboratory took possession of a 1000-quibit D-Wave 2X system that LANL ordered a year ago around the time of SC15. Read more…

By John Russell

IBM Wants to be “Red Hat” of Deep Learning

January 26, 2017

IBM today announced the addition of TensorFlow and Chainer deep learning frameworks to its PowerAI suite of deep learning tools, which already includes popular offerings such as Caffe, Theano, and Torch. Read more…

By John Russell

Enlisting Deep Learning in the War on Cancer

December 7, 2016

Sometime in Q2 2017 the first ‘results’ of the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) will become publicly available according to Rick Stevens. He leads one of three JDACS4C pilot projects pressing deep learning (DL) into service in the War on Cancer. Read more…

By John Russell

Tokyo Tech’s TSUBAME3.0 Will Be First HPE-SGI Super

February 16, 2017

In a press event Friday afternoon local time in Japan, Tokyo Institute of Technology (Tokyo Tech) announced its plans for the TSUBAME3.0 supercomputer, which will be Japan’s “fastest AI supercomputer,” Read more…

By Tiffany Trader

HPC Startup Advances Auto-Parallelization’s Promise

January 23, 2017

The shift from single core to multicore hardware has made finding parallelism in codes more important than ever, but that hasn’t made the task of parallel programming any easier. Read more…

By Tiffany Trader

Leading Solution Providers

CPU Benchmarking: Haswell Versus POWER8

June 2, 2015

With OpenPOWER activity ramping up and IBM’s prominent role in the upcoming DOE machines Summit and Sierra, it’s a good time to look at how the IBM POWER CPU stacks up against the x86 Xeon Haswell CPU from Intel. Read more…

By Tiffany Trader

BioTeam’s Berman Charts 2017 HPC Trends in Life Sciences

January 4, 2017

Twenty years ago high performance computing was nearly absent from life sciences. Today it’s used throughout life sciences and biomedical research. Genomics and the data deluge from modern lab instruments are the main drivers, but so is the longer-term desire to perform predictive simulation in support of Precision Medicine (PM). There’s even a specialized life sciences supercomputer, ‘Anton’ from D.E. Shaw Research, and the Pittsburgh Supercomputing Center is standing up its second Anton 2 and actively soliciting project proposals. There’s a lot going on. Read more…

By John Russell

Nvidia Sees Bright Future for AI Supercomputing

November 23, 2016

Graphics chipmaker Nvidia made a strong showing at SC16 in Salt Lake City last week. Read more…

By Tiffany Trader

TSUBAME3.0 Points to Future HPE Pascal-NVLink-OPA Server

February 17, 2017

Since our initial coverage of the TSUBAME3.0 supercomputer yesterday, more details have come to light on this innovative project. Of particular interest is a new board design for NVLink-equipped Pascal P100 GPUs that will create another entrant to the space currently occupied by Nvidia's DGX-1 system, IBM's "Minsky" platform and the Supermicro SuperServer (1028GQ-TXR). Read more…

By Tiffany Trader

IDG to Be Bought by Chinese Investors; IDC to Spin Out HPC Group

January 19, 2017

US-based publishing and investment firm International Data Group, Inc. (IDG) will be acquired by a pair of Chinese investors, China Oceanwide Holdings Group Co., Ltd. Read more…

By Tiffany Trader

Is Liquid Cooling Ready to Go Mainstream?

February 13, 2017

Lost in the frenzy of SC16 was a substantial rise in the number of vendors showing server oriented liquid cooling technologies. Three decades ago liquid cooling was pretty much the exclusive realm of the Cray-2 and IBM mainframe class products. That’s changing. We are now seeing an emergence of x86 class server products with exotic plumbing technology ranging from Direct-to-Chip to servers and storage completely immersed in a dielectric fluid. Read more…

By Steve Campbell

Dell Knights Landing Machine Sets New STAC Records

November 2, 2016

The Securities Technology Analysis Center, commonly known as STAC, has released a new report characterizing the performance of the Knight Landing-based Dell PowerEdge C6320p server on the STAC-A2 benchmarking suite, widely used by the financial services industry to test and evaluate computing platforms. The Dell machine has set new records for both the baseline Greeks benchmark and the large Greeks benchmark. Read more…

By Tiffany Trader

Intel and Trump Announce $7B for Fab 42 Targeting 7nm

February 8, 2017

In what may be an attempt by President Trump to reset his turbulent relationship with the high tech industry, he and Intel CEO Brian Krzanich today announced plans to invest more than $7 billion to complete Fab 42. Read more…

By John Russell

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