At the Nexus of Grid, Cloud and HPC

By Dennis Barker

October 17, 2008

What’s the big difference between cloud computing and grid computing? The goal of cloud computing is to put system administrators out of work.

That’s one way of looking at it, at least. Steve Armentrout, CEO of Parabon Computation, says that was the perspective tossed out by a couple of Google and IBM reps at a panel discussion in which he recently participated. Armentrout suggests a less Dickensian way of looking at it: cloud computing is about “providing a datacenter that is fully automated.” (More on cloud versus grid later.)

Armentrout sees cloud and grid as complementary in some ways — bipartisan, you might say — but he is an unapologetic grid partisan — especially when it comes to his company’s collection of solutions. “We have no intention of changing our grid stripes,” he says. “What Parabon provides is grid software as a service. We enable individuals with grid applications to scale them across a large infrastructure without having to go out and buy hardware. They can just buy capacity as it’s needed. It’s a pay-as-you-go model.”

Basically, Parabon’s Frontier Grid Services offering is a high-performance computing utility. If you need a few thousand nodes to run a financial risk model or some other long and winding analysis, Parabon will hook you up to the resources you need. “We broker computation,” Armentrout says. Like its customers, the company doesn’t own datacenters. What it has is contracts with universities and institutions with big server farms and HPC clusters to aggregate their unused capacity. “All that compute power we use to provide computation on demand,” explains Armentrout.

There’s a lot of computational capability sitting around doing nothing, Armentrout says. “You often hear the estimate that standard servers are typically running at anywhere from 5 to 20 percent capacity. Just think of 80 percent capacity going to waste. Even in a virtualized environment, seldom do you see capacity usage at over 50 percent. All that idle capacity allows us to deploy across a university datacenter, for example, and execute large-scale jobs in the background. Frontier is our technology that lets us capture that unused capacity and make it available as a grid service.”

Parabon’s technology can be used, as just described, across worldwide “public” resources like campus networks — that’s the Parabon Computation Grid — but can also be applied to a company’s own network as the Frontier Enterprise Grid.
 
Parabon built its platform around the Frontier Grid Server, which provides grid services and shared resources to users and developers, whether using the Internet-based Parabon Computation Grid or an in-house Frontier Enterprise grid. The Frontier Grid Server manages execution of jobs across hundreds or thousands of compute nodes. “It can scale up to arbitrarily large grids,” Armentrout says. “Tens of thousands of machines.” Frontier always reserves excess capacity to handle unexpected scale-out demands, he says.

The Frontier Compute Engine is the agnostic agent application that runs on each grid node to actually do the work. It executes tasks only when the resource, the virtual machine in many cases, is not handling a primary task. “Frontier runs as a low-priority process,” Armentrout says, “so if running in a virtualized datacenter — a cloud, you could say — the Compute Engine backs off if a request comes in from the cloud application. It takes precedence. But when resources are not busy, we can fully saturate the datacenter during that unused period of time.”

For example, Parabon might have an arrangement with a research facility in Australia to use its cluster when the scientists are home at night. That could be prime work time for scientists on the other side of the globe. That’s when Frontier could saturate compute nodes to calculate solutions more quickly.

Parabon just released a browser-based interface called the Dashboard that provides an intuitive front-end to the Frontier Grid Platform. “It lets you easily monitor a job, kill a job, assign resources, plus some back-office and accounting functions like looking up how much you’re paying for use,” Armentrout says.

Parabon’s pricing structure is better explained by the company, but the basic idea is that customers pay for units of computational power using a formula that involves kilo-cap hours.

The company provides an API and suite of tools to simplify adapting applications to take advantage of Frontier grid capabilities. And there’s a collection of Frontier-ready programs for applications, including data mining and biological modeling. “It’s kind of like Apple’s App Store but for distributed applications,” Armentrout analogizes. 

Parabon has been around since 2000, when it introduced “the first commercial grid,” Armentrout says. Customers include not just scientific researchers, but also financial analysts, commercial enterprises with high-end analytical demands, bioinformatics, traditional HPC users and government agencies. “Our customers are doing modeling and simulation with very large models, immense data sets,” he explains. “We enable them to run not just one complex scenario but 10,000 scenarios. With Frontier you can explore an entire space of possibilities at once instead of running one simulation, then another, then another.”

Grid vs. Cloud: Parabon-Style

“In terms of grid vs. cloud, there’s lots of confusion around those two terms,” Armentrout says. “But, honestly, the fact that cloud has so much hype surrounding it now makes it easier for us to clarify to customers the benefits of grid computing. Grid, I think, is becoming clearer in people’s minds, while cloud is still, if I might say it, a ‘cloudy’ term.”

There are certainly commonalities, he says: computational utility, virtualized use of computing resources, eliminating the need for dedicated resources and dramatically improved price/performance. “But cloud computing is more about auto-provisioning virtual machines,” explains Armentrout. “It’s about software that lets you go out into a cloud infrastructure, a virtualized datacenter, and say give me one or two VMs and get them in an automated and orderly way. It’s about a datacenter that is completely automated. Sure, customers can scale up and down — that’s one of the benefits of the model — but they typically don’t scale in large-scale numbers. That’s the nature of most Web applications, which is typically what runs in the cloud. In that environment, you still have a lot of capacity that’s available.” 

On the other hand, he believes that grid computing is all about massive parallelization and running large-scale jobs on unused capacity rather than dedicated capacity. The goal is to accelerate large jobs from days to minutes and hours to seconds, and grid computing can enable computations that “just aren’t possible,” he says.

“The folks we’re talking to understand they need grid-scale compute capacity, and that’s not something they’ll get from a pure cloud approach,” Armentrout says. “We routinely run jobs on several thousand machines. It’s that mass parallelization that you just wouldn’t run in the cloud. You want a job done in 5 minutes, not days. Our grid service reaches out to thousands and thousands of boxes and returns an answer in minutes.”

“We’ve got a high-performance solution that works for our customers. We can take advantage of a cloud infrastructure, but we don’t need to chase the cloud phenomenon.”

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!

Ohio Supercomputer Center Dedicates ‘Owens’ Cluster

March 29, 2017

In a dedication ceremony held earlier today (March 29), officials from Ohio Supercomputer Center (OSC) along with state representatives gathered to celebrate the launch of OSC’s newest cluster: Read more…

By Tiffany Trader

EU Ratchets up the Race to Exascale Computing

March 29, 2017

The race to expand HPC infrastructure, including exascale machines, to advance national and regional interests ratcheted up a notch yesterday with announcement that seven European countries – Read more…

By John Russell

Data-Hungry Algorithms and the Thirst for AI

March 29, 2017

At Tabor Communications’ Leverage Big Data + EnterpriseHPC Summit in Florida last week, esteemed HPC professional Jay Boisseau, chief HPC technology strategist at Dell EMC, engaged the audience with his presentation, “Big Computing, Big Data, Big Trends, Big Results.” Read more…

By Tiffany Trader

Bill Gropp – Pursuing the Next Big Thing at NCSA

March 28, 2017

About eight months ago Bill Gropp was elevated to acting director of the National Center for Supercomputing Applications (NCSA). Read more…

By John Russell

HPE Extreme Performance Solutions

Leveraging the Power of Big Data to Improve Customer Satisfaction & Brand Loyalty

In the dynamic world of retail, retailers must find ways to recognize and effectively respond to shopping behaviors, patterns, and trends in order to succeed. Read more…

UK to Launch Six Major HPC Centers

March 27, 2017

Six high performance computing centers will be formally launched in the U.K. later this week intended to provide wider access to HPC resources to U.K. Read more…

By John Russell

AI in the News: Rao in at Intel, Ng out at Baidu, Nvidia on at Tencent Cloud

March 26, 2017

Just as AI has become the leitmotif of the advanced scale computing market, infusing much of the conversation about HPC in commercial and industrial spheres, it also is impacting high-level management changes in the industry. Read more…

By Doug Black

Scalable Informatics Ceases Operations

March 23, 2017

On the same day we reported on the uncertain future for HPC compiler company PathScale, we are sad to learn that another HPC vendor, Scalable Informatics, is closing its doors. Read more…

By Tiffany Trader

‘Strategies in Biomedical Data Science’ Advances IT-Research Synergies

March 23, 2017

“Strategies in Biomedical Data Science: Driving Force for Innovation” by Jay A. Etchings is both an introductory text and a field guide for anyone working with biomedical data. Read more…

By Tiffany Trader

Data-Hungry Algorithms and the Thirst for AI

March 29, 2017

At Tabor Communications’ Leverage Big Data + EnterpriseHPC Summit in Florida last week, esteemed HPC professional Jay Boisseau, chief HPC technology strategist at Dell EMC, engaged the audience with his presentation, “Big Computing, Big Data, Big Trends, Big Results.” Read more…

By Tiffany Trader

Bill Gropp – Pursuing the Next Big Thing at NCSA

March 28, 2017

About eight months ago Bill Gropp was elevated to acting director of the National Center for Supercomputing Applications (NCSA). 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 assets. 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. Read more…

By John Russell

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 campaign. 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

US Supercomputing Leaders Tackle the China Question

March 15, 2017

Joint DOE-NSA report responds to the increased global pressures impacting the competitiveness of U.S. supercomputing. Read more…

By Tiffany Trader

New Japanese Supercomputing Project Targets Exascale

March 14, 2017

Another Japanese supercomputing project was revealed this week, this one from emerging supercomputer maker, ExaScaler Inc., and Keio University. The partners are working on an original supercomputer design with exascale aspirations. Read more…

By Tiffany Trader

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

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. Read more…

By John Russell

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 campaign. 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 assets. 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

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

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

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

Leading Solution Providers

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

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

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

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

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

US Supercomputing Leaders Tackle the China Question

March 15, 2017

Joint DOE-NSA report responds to the increased global pressures impacting the competitiveness of U.S. supercomputing. Read more…

By Tiffany Trader

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

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

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