Heresies of the New HPC Cloud Universe

By John Russell

January 25, 2017

Perhaps ‘heresies’ is a bit strong, but HPC in the cloud, even for academics, is a fast-changing domain that’s increasingly governed by a new mindset, says Tim Carroll, head of ecosystem development and sales at Cycle Computing, an early pioneer in HPC cloud orchestration and provisioning software. The orthodoxy of the past – an emphatic focus on speeds and feeds, if you will – is being erased by changing researcher attitudes and the advancing capabilities of public (AWS, Microsoft, Google et al.) and private (Penguin, et al.) clouds.

Maybe this isn’t a revelation in enterprise settings where cost and time-to-market usually trump fascination with leading edge technology. True enough, agrees Carroll, but the maturing cloud infrastructure’s ability to handle the majority of science workflows – from simple Monte Carlo simulations to many demanding deep learning and GPU-accelerated workloads – is not only boosting enterprise HPC use, but also catching the attention of government and academic researchers. The job logjam (and hidden costs) when using institutional and NSF resources is prompting researchers to seek ways to avoid long queues, speed time-to-result, exercise closer control over their work and (potentially) trim costs, he says.

If all of that sounds like a good marketing pitch, well Carroll is after all in sales. No matter, he is also a long-time industry veteran who has watched the cloud’s evolution for years and has played a role in mainstreaming HPC, notably including seven years at Dell (first as senior manager HPC and later executive director emerging enterprise), before joining Cycle in 2014.

As a provider of a software platform that links HPC users to clouds, Cycle has a front row seat on changing HPC cloud user demographics and attitudes as well as cloud provider strengths and weaknesses. In this interview with HPCwire, Carroll discusses market and technology dynamics shaping HPC use in the cloud. Technology is no longer the dominant driver, he says. See if you agree with Carroll’s survey of the changing cloud landscape.

HPCwire: The democratization of HPC has been bandied about for quite awhile with the cloud portrayed as a critical element. There’s also a fair amount of argument around how much of the new HPC really is HPC. Let’s start at the top. How is cloud changing HPC and why is there so much debate over it?

Tim Carroll: Running HPC in the cloud runs antithetical to how most people were brought up in what was essentially an infrastructure centric world. Most of what you did [with] HPC was improve your ability to break through performance ceilings or to handle corners cases that were not traditional enterprise problems; so it was an industry that prided itself on breakthrough performance and corner cases. That was the mindset.

What HPC in the cloud is saying is “All of the HPC people who for years have been saying how big this industry is going to grow were exactly right, but not $25B being spent by people worrying about limits and corner cases. A healthy part of the growth came from people who didn’t care about anything but getting their work done. Some people still care about the traditional definition, but I would say there are a whole bunch of people who don’t even define it, they just see a way to do things that they couldn’t do five years ago or ten years ago.

HPCwire: So are the new users not really running HPC workloads and has the HPC definition changed?

Carroll: HPC workloads are always changing and perhaps the definition of the HPC along with it, but I think what’s really happening is the customer demographics are changing. It’s a customer demographic defined not by the software or the system, but the answer. When you ask someone in the research environment what their job is, they say I’m a geneticist or I am a computational chemist. Speak with an industrial engineer and they describe themselves as, no surprise, an industrial engineer. No one describes themselves as an “HPCer.” They all say I‘ve got a job to do and I’ve got to get it done in a certain amount of time at a certain price point. Their priority is the work.

I think what we did at Dell (now Dell EMC) was a huge step towards democratizing HPC. The attitude was that TOP500 was not the measure of success. Our goal very early on was to deliver more compute to academic researchers than another vendor. We did not strive for style points or the number of flops of any one particular system but we were determined to enable more scientists using more Dell flops than anybody else. That was our strategy and Dell was very successful with it.

HPCwire: That sounds a little too easy, as if they don’t need to know any or at least much computational technology. It’s clear systems vendors are racing to create easier-to-use machines and efforts like OpenHPC are making progress in creating a reference HPC stack. What do users need to know?

Carroll: Users and researchers absolutely need to understand the capabilities of their software and what can they actually do relative to the problem they need to solve, but they should not be required to know much more than that. For the last 20 years engineers and researchers defined the size of the problem they could tackle by the resources they knew they could get access. So if I know I have only got a 40-node cluster, what do I do? I start sizing my problems to fit on my cluster. Self-limiting happened unconsciously. But it doesn’t matter; the net effect was an artificial cap on growth.

So today, we’re not saying get rid of that 40-node cluster and make it bigger, but give people the choice to go bigger. Today, an engineer should be able to go to their boss and say, “I think I can deliver this research four months ahead of schedule if I have the ability to access 60 million core hours over a two week period and it’s going to cost – I am just making up numbers – $100,000.” Then the engineer and her boss go to the line of business and see if they want to come up with opex that will cover that and pull in their schedule by three months. Cloud gives people and organizations choice.

HPCwire: Stepping away from the enterprise for a moment, what’s the value proposition for academic and government researchers many of whom are very well versed indeed in HPC? Aren’t they simply likely to use internal resources or NSF cyberinfrastructure as opposed to the public cloud?

Carroll: The academic portion is really interesting because of how important the funding models are and the rules set by funding agencies. Because of that, it’s not always obvious if cloud is even an option. It also depends how the individual institutions charge for the other pieces [of a cloud deployment] that are being done. Often there is overhead and so it doesn’t matter how cost effective the cloud is because by the time it gets to the researcher, the landed cost to them is going to be prohibitive.

As a result, cloud for academic HPC has been murky for the last couple of years. People aren’t ready to get there yet. Jetstream was a step in the right direction (NSF-funded initiative) but I’m wondering if anyone put themselves in the shoes of users, big and small, to judge how that experience compares to the public cloud providers.

The cloud thing is going to be here this year, and next year, and many years after. And guess what there’s also going to be a refresh cycle on internal hardware next year and a refresh cycle the year after. People are going to have to get more and more granular in their justification for deploying in their internal infrastructure versus using public cloud. And I am not saying that’s an either or proposition. But if the demand for compute is growing at 50 percent per year and budgets are going up a lot less, how are you going to fill that gap providing the researcher what they need to get their jobs done. What is the value proposition of longer job queues?

How can academia or the funding agencies not embrace what is arguably the fastest moving, innovating, cost-effective platform in order to fill the compute demand gap. Cloud is just one more tool, but if one views it as the Trojan Horse to get inside academia eliminate infrastructure, that is just wrong. Cloud is going to get its portion of the overall market where it makes sense for certain workloads, but not necessarily entire segments. Embrace it.

HPCwire: What’s been the Cycle experience in dealing with the academic community?

Carroll: I am still somewhat surprised at the amount of pushback I get from the academic community based on anecdotal information – the number of people who talk about what can and can’t be done although they haven’t tried. And there are so many people at the public cloud providers who would love to help them. Who knows, it may work out that they run the workload to see how much it would cost and the data says it is still twice as expensive as [internally]. That’s great, now we have a hard data point rather than something anecdotal.

One of the great things cloud will do for academia is to clear the decks for people who are truly building specialized infrastructure to solve really hard problems. What’s typically happened is that institutions had to support a breadth of researchers and were faced with the challenge solving diverse needs from a demanding community. The result was commodity clusters became the best middle ground; good enough for the middle but not really what the high and the low needed. In trying to serve a diverse market with a single architecture, few people got exactly what their research required. What you are going to see is that bell curve is going to get turned upside down and centers are going to reallocate capex to specialized systems and run high throughput workloads on public cloud.

I should note that the major cloud providers all have enormous respect for the HPC market segment and appreciate the fact that the average customer at the high end probably consumes ten to several hundred times the compute of a typical enterprise customer. They are all staffing up with very talented people and are eager to collaborate with academia to deliver solutions to them.

HPCwire: What will be the big driver of cloud computing in academic research centers, beyond NSF resources I mean?

Carroll: In the last ten years universities have become far more competitive as far as attracting the right researchers. It used to be that every new researcher got his or her cluster in a startup package and that model is flat-out unsustainable. But it’s a small enough community that researchers will quickly hear when the word is “ABC University has a great system and their researchers have no queue times, with no workload limits.” Who cares where the compute is performed, the university will have generated a reputation that if you’re a researcher there, you just get what you need to get the job done. Competition for talent is going to drive larger cloud adoption in academia.

It can also be powerful for individual researchers working on small projects. There’s a professor who reached out to us who wanted to include [cloud computing] in her class as part of her teaching and doing real science. She wanted to start the job when the semester began and have it finish by the time it ended. We said, how about if instead of taking four months like you thought, we knock it down to a couple of days. It is not grant based research but the cost fit within her discretionary budget. So she is not doing it as an academic exercise. This is a piece of science that would not be done were it not for this. And it is part of the teaching.

HPCwire: How do you characterize the cloud providers and how does Cycle fit in?

Carroll: We (Cycle) are a software platform that gives people the ability to run their workloads under their control on whichever cloud is best for them. It is not a SaaS model. Users can still protect their corporate IP, with their existing workload, and run multiple workloads for multiple users across multiple clouds from a single control point. We fit in with the cloud providers by helping them and their users do what they do best.

My experience is that customers are not looking for “cloud”; they have a problem and a deadline and they are just trying to figure out how can they run workloads securely and cost effectively that can’t get run today on their internal infrastructure. If I came and said it’s the public cloud, that would be ok. Private cloud, ok. If I said we would pull a trailer stuffed with servers to their building, they’d say ok. They will make their choice based on how much it costs; is it secure, how much work is required to get started and keep it running. It just so happens that the winner is increasingly public cloud.

Back to your earlier question about change within HPC. Cloud is not causing infrastructure to disappear, it is causing labels to disappear. Customers who have been early adopters are now on their sixth or eighth or tenth workload and are starting to get into workloads that are considered “traditional HPC.” But they did not label the first workloads as “non-HPC.” They just view them as compute intensive applications and don’t care whether it is called HPC or something else.

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!

Democratization of HPC Part 3: Ninth Graders Tap HPC in the Cloud to Design Flying Boats

October 18, 2018

This is the third in a series of articles demonstrating the growing acceptance of high-performance computing (HPC) in new user communities and application areas. In this article we present UberCloud use case #208 on how Read more…

By Wolfgang Gentzsch and Håkon Bull Hove

Penguin Computing Launches Consultancy for Piecing AI Strategies Together

October 18, 2018

AI stands before the HPC industry as a beacon of great expectations, yet market research repeatedly shows that AI adoption is commonly stuck in the talking phase, on the near side of a difficult chasm to cross. In respon Read more…

By Tiffany Trader

When Water Quality—Not Quantity—Hinders HPC Cooling

October 18, 2018

Attention has been paid to the sheer quantity of water consumed by supercomputers’ cooling towers – and rightly so, as they can require thousands of gallons per minute to cool. But in the background, another factor can emerge, bottlenecking efficiency and raising costs: water quality. Read more…

By Oliver Peckham

HPE Extreme Performance Solutions

One Small Step Toward Mars: One Giant Leap for Supercomputing

Since the days of the Space Race between the U.S. and the former Soviet Union, we have continually sought ways to perform experiments in space. Read more…

IBM Accelerated Insights

Paper Offers ‘Proof’ of Quantum Advantage on Some Problems

October 18, 2018

Is quantum computing worth all the effort being poured into it or should we just wait for classical computing to catch up? An IBM blog today posed those questions and, you won’t be surprised, offers a firm “it’s wo Read more…

By John Russell

Penguin Computing Launches Consultancy for Piecing AI Strategies Together

October 18, 2018

AI stands before the HPC industry as a beacon of great expectations, yet market research repeatedly shows that AI adoption is commonly stuck in the talking phas Read more…

By Tiffany Trader

When Water Quality—Not Quantity—Hinders HPC Cooling

October 18, 2018

Attention has been paid to the sheer quantity of water consumed by supercomputers’ cooling towers – and rightly so, as they can require thousands of gallons per minute to cool. But in the background, another factor can emerge, bottlenecking efficiency and raising costs: water quality. Read more…

By Oliver Peckham

Paper Offers ‘Proof’ of Quantum Advantage on Some Problems

October 18, 2018

Is quantum computing worth all the effort being poured into it or should we just wait for classical computing to catch up? An IBM blog today posed those questio Read more…

By John Russell

Dell EMC to Supply U Michigan’s Great Lakes Cluster

October 16, 2018

The University of Michigan (U-M) today announced Dell EMC is the lead vendor for U-M’s $4.8 million Great Lakes HPC cluster scheduled for deployment in first Read more…

By John Russell

Houston to Field Massive, ‘Geophysically Configured’ Cloud Supercomputer

October 11, 2018

Based on some news stories out today, one might get the impression that the next system to crack number one on the Top500 would be an industrial oil and gas mon Read more…

By Tiffany Trader

Nvidia Platform Pushes GPUs into Machine Learning, High Performance Data Analytics

October 10, 2018

GPU leader Nvidia, generally associated with deep learning, autonomous vehicles and other higher-end enterprise and scientific workloads (and gaming, of course) Read more…

By Doug Black

Federal Investment in Exascale – What It Really Means

October 10, 2018

Earlier this month, the EuroHPC JU (Joint Undertaking) reached critical mass, and it seems all EU and affiliated member states, bar the UK (unsurprisingly), have or will sign on. The EuroHPC JU was born from a recognition that individual EU member states, and the EU as a whole, were significantly underinvesting in HPC compared to the US, China and Japan, who all have their own exascale investment and delivery strategies (NSCI, 13th 5 Year Plan, Post-K, etc). Read more…

By Dairsie Latimer

NERSC-9 Clues Found in NERSC 2017 Annual Report

October 8, 2018

If you’re eager to find out who’ll supply NERSC’s next-gen supercomputer, codenamed NERSC-9, here’s a project update to tide you over until the winning bid and system details are revealed. The upcoming system is referenced several times in the recently published 2017 NERSC annual report. Read more…

By Tiffany Trader

TACC Wins Next NSF-funded Major Supercomputer

July 30, 2018

The Texas Advanced Computing Center (TACC) has won the next NSF-funded big supercomputer beating out rivals including the National Center for Supercomputing Ap Read more…

By John Russell

IBM at Hot Chips: What’s Next for Power

August 23, 2018

With processor, memory and networking technologies all racing to fill in for an ailing Moore’s law, the era of the heterogeneous datacenter is well underway, Read more…

By Tiffany Trader

Requiem for a Phi: Knights Landing Discontinued

July 25, 2018

On Monday, Intel made public its end of life strategy for the Knights Landing "KNL" Phi product set. The announcement makes official what has already been wide Read more…

By Tiffany Trader

CERN Project Sees Orders-of-Magnitude Speedup with AI Approach

August 14, 2018

An award-winning effort at CERN has demonstrated potential to significantly change how the physics based modeling and simulation communities view machine learni Read more…

By Rob Farber

House Passes $1.275B National Quantum Initiative

September 17, 2018

Last Thursday the U.S. House of Representatives passed the National Quantum Initiative Act (NQIA) intended to accelerate quantum computing research and developm Read more…

By John Russell

Summit Supercomputer is Already Making its Mark on Science

September 20, 2018

Summit, now the fastest supercomputer in the world, is quickly making its mark in science – five of the six finalists just announced for the prestigious 2018 Read more…

By John Russell

New Deep Learning Algorithm Solves Rubik’s Cube

July 25, 2018

Solving (and attempting to solve) Rubik’s Cube has delighted millions of puzzle lovers since 1974 when the cube was invented by Hungarian sculptor and archite Read more…

By John Russell

D-Wave Breaks New Ground in Quantum Simulation

July 16, 2018

Last Friday D-Wave scientists and colleagues published work in Science which they say represents the first fulfillment of Richard Feynman’s 1982 notion that Read more…

By John Russell

Leading Solution Providers

HPC on Wall Street 2018 Booth Video Tours Playlist

Arista

Dell EMC

IBM

Intel

RStor

VMWare

TACC’s ‘Frontera’ Supercomputer Expands Horizon for Extreme-Scale Science

August 29, 2018

The National Science Foundation and the Texas Advanced Computing Center announced today that a new system, called Frontera, will overtake Stampede 2 as the fast Read more…

By Tiffany Trader

HPE No. 1, IBM Surges, in ‘Bucking Bronco’ High Performance Server Market

September 27, 2018

Riding healthy U.S. and global economies, strong demand for AI-capable hardware and other tailwind trends, the high performance computing server market jumped 28 percent in the second quarter 2018 to $3.7 billion, up from $2.9 billion for the same period last year, according to industry analyst firm Hyperion Research. Read more…

By Doug Black

Intel Announces Cooper Lake, Advances AI Strategy

August 9, 2018

Intel's chief datacenter exec Navin Shenoy kicked off the company's Data-Centric Innovation Summit Wednesday, the day-long program devoted to Intel's datacenter Read more…

By Tiffany Trader

GPUs Power Five of World’s Top Seven Supercomputers

June 25, 2018

The top 10 echelon of the newly minted Top500 list boasts three powerful new systems with one common engine: the Nvidia Volta V100 general-purpose graphics proc Read more…

By Tiffany Trader

Germany Celebrates Launch of Two Fastest Supercomputers

September 26, 2018

The new high-performance computer SuperMUC-NG at the Leibniz Supercomputing Center (LRZ) in Garching is the fastest computer in Germany and one of the fastest i Read more…

By Tiffany Trader

MLPerf – Will New Machine Learning Benchmark Help Propel AI Forward?

May 2, 2018

Let the AI benchmarking wars begin. Today, a diverse group from academia and industry – Google, Baidu, Intel, AMD, Harvard, and Stanford among them – releas Read more…

By John Russell

Aerodynamic Simulation Reveals Best Position in a Peloton of Cyclists

July 5, 2018

Eindhoven University of Technology (TU/e) and KU Leuven research group conducts the largest numerical simulation ever done in the sport industry and cycling discipline. The goal was to understand the aerodynamic interactions in the peloton, i.e., the main pack of cyclists in a race. Read more…

Houston to Field Massive, ‘Geophysically Configured’ Cloud Supercomputer

October 11, 2018

Based on some news stories out today, one might get the impression that the next system to crack number one on the Top500 would be an industrial oil and gas mon Read more…

By Tiffany Trader

  • arrow
  • Click Here for More Headlines
  • arrow
Do NOT follow this link or you will be banned from the site!
Share This