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!

Exascale Escapes 2018 Budget Axe; Rest of Science Suffers

May 23, 2017

President Trump's proposed $4.1 trillion FY 2018 budget is good for U.S. exascale computing development, but grim for the rest of science and technology spend Read more…

By Tiffany Trader

Hedge Funds (with Supercomputing help) Rank First Among Investors

May 22, 2017

In case you didn’t know, The Quants Run Wall Street Now, or so says a headline in today’s Wall Street Journal. Quant-run hedge funds now control the largest Read more…

By John Russell

IBM, D-Wave Report Quantum Computing Advances

May 18, 2017

IBM said this week it has built and tested a pair of quantum computing processors, including a prototype of a commercial version. That progress follows an an Read more…

By George Leopold

PRACEdays 2017 Wraps Up in Barcelona

May 18, 2017

Barcelona has been absolutely lovely; the weather, the food, the people. I am, sadly, finishing my last day at PRACEdays 2017 with two sessions: an in-depth loo Read more…

By Kim McMahon

HPE Extreme Performance Solutions

Exploring the Three Models of Remote Visualization

The explosion of data and advancement of digital technologies are dramatically changing the way many companies do business. With the help of high performance computing (HPC) solutions and data analytics platforms, manufacturers are developing products faster, healthcare providers are improving patient care, and energy companies are improving planning, exploration, and production. Read more…

US, Europe, Japan Deepen Research Computing Partnership

May 18, 2017

On May 17, 2017, a ceremony was held during the PRACEdays 2017 conference in Barcelona to announce the memorandum of understanding (MOU) between PRACE in Europe Read more…

By Tiffany Trader

NSF, IARPA, and SRC Push into “Semiconductor Synthetic Biology” Computing

May 18, 2017

Research into how biological systems might be fashioned into computational technology has a long history with various DNA-based computing approaches explored. N Read more…

By John Russell

DOE’s HPC4Mfg Leads to Paper Manufacturing Improvement

May 17, 2017

Papermaking ranks third behind only petroleum refining and chemical production in terms of energy consumption. Recently, simulations made possible by the U.S. D Read more…

By John Russell

PRACEdays 2017: The start of a beautiful week in Barcelona

May 17, 2017

Touching down in Barcelona on Saturday afternoon, it was warm, sunny, and oh so Spanish. I was greeted at my hotel with a glass of Cava to sip and treated to a Read more…

By Kim McMahon

Exascale Escapes 2018 Budget Axe; Rest of Science Suffers

May 23, 2017

President Trump's proposed $4.1 trillion FY 2018 budget is good for U.S. exascale computing development, but grim for the rest of science and technology spend Read more…

By Tiffany Trader

Cray Offers Supercomputing as a Service, Targets Biotechs First

May 16, 2017

Leading supercomputer vendor Cray and datacenter/cloud provider the Markley Group today announced plans to jointly deliver supercomputing as a service. The init Read more…

By John Russell

HPE’s Memory-centric The Machine Coming into View, Opens ARMs to 3rd-party Developers

May 16, 2017

Announced three years ago, HPE’s The Machine is said to be the largest R&D program in the venerable company’s history, one that could be progressing tow Read more…

By Doug Black

What’s Up with Hyperion as It Transitions From IDC?

May 15, 2017

If you’re wondering what’s happening with Hyperion Research – formerly the IDC HPC group – apparently you are not alone, says Steve Conway, now senior V Read more…

By John Russell

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

HPE Launches Servers, Services, and Collaboration at GTC

May 10, 2017

Hewlett Packard Enterprise (HPE) today launched a new liquid cooled GPU-driven Apollo platform based on SGI ICE architecture, a new collaboration with NVIDIA, a Read more…

By John Russell

IBM PowerAI Tools Aim to Ease Deep Learning Data Prep, Shorten Training 

May 10, 2017

A new set of GPU-powered AI software announced by IBM today brings automation to many of the tedious, time consuming and complex aspects of AI project on-rampin Read more…

By Doug Black

Bright Computing 8.0 Adds Azure, Expands Machine Learning Support

May 9, 2017

Bright Computing, long a prominent provider of cluster management tools for HPC, today released version 8.0 of Bright Cluster Manager and Bright OpenStack. The 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. Just how close real-wo 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 cam Read more…

By John Russell

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

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

Last week, Google reported that its custom ASIC Tensor Processing Unit (TPU) was 15-30x faster for inferencing workloads than Nvidia's K80 GPU (see our coverage Read more…

By Tiffany Trader

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

Since our first formal product releases of OSPRay and OpenSWR libraries in 2016, CPU-based Software Defined Visualization (SDVis) has achieved wide-spread adopt Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

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 ne Read more…

By Tiffany Trader

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

Leading Solution Providers

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 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 w 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 Read more…

By Steve Campbell

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 Eng 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 Read more…

By John Russell

US Supercomputing Leaders Tackle the China Question

March 15, 2017

As China continues to prove its supercomputing mettle via the Top500 list and the forward march of its ambitious plans to stand up an exascale machine by 2020, 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 networ Read more…

By Tiffany Trader

DOE Supercomputer Achieves Record 45-Qubit Quantum Simulation

April 13, 2017

In order to simulate larger and larger quantum systems and usher in an age of "quantum supremacy," researchers are stretching the limits of today's most advance Read more…

By Tiffany Trader

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