What Drives Investment in the Middle of HPC?

By Nicole Hemsoth

May 15, 2014

When it comes to covering supercomputers, the most attention falls on the front runners on the Top 500. However, a closer look at the tail-end of the rankings reveals some rather interesting use cases—not to mention courses of development, system design, and user-driven requirements for future build out.

The University of Florida is home to one expanding system, which rests just at the cutoff of the top supercomputing rankings at #493. The university’s Director of Research Computing, Dr. Erik Deumens, tells us the real purpose of the system is to support as many diverse applications as possible with as few queue barriers as possible. While this is a familiar claim no matter what size the site may be, the team has gone through great lengths to ensure that current developments to make their flagship system, called HiPerGator, are fed solely by user demand.

It might not be surprising then, at least to those in research computing, that the demand for the latest generation of processors with a 10 or 20% performance jump is far less critical than simply being able to onboard an application without a long queue and run in a reasonable amount of time. But meeting that need requires some serious thought about capacity, scheduling, and meeting diverse application requirements. In other words, for those tuning in for the ultra-high performance computing story, this isn’t the most exciting tale, but there are some important lessons to be learned from his team’s experiences working with a broad range of applications and over 600 users to find out what really creates a fully functional system—all based on what amounts to an “economic” decision-making process for their HPC investments.

In essence, the economics of demand determine the spending decisions at the University of Florida and several other similar centers. This isn’t so different than the large scientific computing sites in theory, except user requests trump all—including power or other considerations. “If the users are asking for the latest novel technology but it’s not the most efficient, we aren’t going to deny them what they need for their research,” says Deumens. In the case of HiPerGator, the university funds the system and staffing so that that individual researchers can use their grants to buy a desired number of cores for their jobs. Flexibility is built into the “purchase” as users can go past 10x what they requested as needed to avoid added complexity in terms of scheduling and managing their jobs. Deumens and team use Moab and Torque to handle the many requests, in addition to offering the capability for more sophisticated users to fine-tune their requests according to the mix of available architectures. The system tends to run under its maximum capacity at all times so that there are not long wait times since the one thing that researchers want—timely (if not immediate) access to computational resources that run in the anticipated timeframe. And essentially, says Deumens, everyone is happy.

For some background, the HiPerGator system in its original incarnation (announced last year) offered up over 16,000 AMD “Abu Dhabi” cores with Dell underpinnings, a 2.88 petabyte Terascala-built Lustre-based system and Mellanox’s Infiniband throughout. They’ve since added an additional round of cores from pre-existing systems (both Intel and AMD), bringing their HPC core count to over 21,000. There is a set of nodes that provide a total of 80 GPUs in addition and more planned for the future—in addition to the possibility of Xeon Phi cores as well as they plan their build-out to be completed by this time next year. “There are always exceptions but most of our users don’t care what processor generation they’re running on. They just want to get their work done.” And all the while, his team keeps very careful track of what the users are looking for in terms of new or existing hardware and they use this information to tally what they ask vendors for during each year’s hardware and software buying cycles.

To put this in context, when the original HiPerGator emerged, there were a total of 8 GPUs available to researchers, which they bought simply to support the mission of a semester-long class that required them for special projects. However, once researchers at the university knew they were available, they began experimenting with porting codes, including AMBER on the molecular dynamics front. These development activities led the application teams to desire full production runs, which required more GPUs. And so their unexpected influx of GPU nodes occurred organically. This is the exact type of case that will feed how the next generation of their system develops—actual user interest means more “purchases” from researchers, but to keep their one main goal of providing solid resources without the wait times, they’ll make sure to supply ample nodes with whatever the research community seems to desire.

Deumens and team are taking those desires on the road in the next months. They’re currently in the midst of looking for vendors to help them supply the needs of HiPerGator 2, which again, is slated for this time next year. He gave us a sense of what works—and doesn’t—when it comes to supporting research at a university that wants to become a top tier research center based on its HPC capabilities.

First, he says, there are some successes in terms of their approach to scheduling. It used to be a manual process, but has been eased through their Moab and Torque engines. Further, he highlighted the increasing role of Galaxy, the open source scientific gateway project for creating, tracking and sharing scientific workflows that has taken off in the biosciences community. He also says that for a research center their size, the more cores they have available, the better. While some of their users can take advantage of their Infiniband fabric and run MPI or SMP jobs, in the end it’s all about getting up and running.

The other element that has worked for research teams at the University of Florida is having a stable, strong storage system like their Terascala solution, which is capable of handling massive data flows—an increasing problem for all scientific computing sites as data demands scramble to meet the computing capacity that is available.

What’s missing from their system is something that will be difficult for any of the vendors who supply the next iteration of the machine next year. And it’s something we’ve heard from much larger centers. There is a dramatic need to make a “super app” of sorts that turns a researcher’s desktop machine into a direct link to the supercomputing site, handling scheduling, data movement, and output in a seamless, portable interface. While this seems like it might be easy in this era of web-based interfaces for everything, it’s what’s really missing for centers designed around simply serving scientific users—and something that he and his team will continue to look toward in the coming years.

It was interesting to listen to the difference in concerns about power, performance and ease of access from the perspective of a much smaller HPC site than the top ten system managers we so often talk to. Power is always a concern, of course, but at smaller scale when exascale is something for the DoE and other government labs internationally to worry about, the problems of real-world daily operations boil down to one simple factor—make a supercomputer easy to use, quick to load into, and predictable in its time to result. A humbling reminder after so many conversations about eeking performance out of the hottest processors, largest systems and biggest power footprints on the planet.

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!

AWS Expands Worldwide Availability to AMD-based Instances

July 22, 2019

Setting aside potential setbacks caused by U.S. trade policies, the steady cadence of AMD’s revival in HPC and the datacenter continued last week with AWS expanding availability of its AMD Epyc-based instances. Recall Read more…

By Staff

Microsoft Investing $1B in OpenAI Artificial General Intelligence R&D

July 22, 2019

Artificial general intelligence (AGI) is AI’s moonshot, the next giant leap for the AI field. Microsoft regards it to be feasible enough to warrant a $1 billion investment in OpenAI, the not-for-profit research organi Read more…

By Doug Black

Researchers Use Supercomputing to Study Links Between Hurricanes and Climate Change

July 19, 2019

As climate change looms, researchers are scrambling to answer the question of how a warming planet will affect the frequency and severity of already-deadly hurricanes. Now, a team of researchers from the University of Il Read more…

By Oliver Peckham

AWS Solution Channel

Unleashing Seismic Modeling at Scale: We Can’t Stop Quakes, But We Can Be Better Prepared

It has been a scary July so far for many residents of California. A magnitude 6.4 quake struck on July 4 near Ridgecrest (about 194 kilometers northeast of Los Angeles), followed by a magnitude 7.1 quake in the same region on July 5. Read more…

HPE Extreme Performance Solutions

Bring the Combined Power of HPC and AI to Your Business Transformation

A growing number of commercial businesses are implementing HPC solutions to derive actionable business insights, to run higher performance applications and to gain a competitive advantage. Read more…

IBM Accelerated Insights

Visual Capital: Seeing Digital Image and Video Archives as Potential Revenue Streams

As most business owners agree, cash is king. But what if there was a hidden source of revenue that companies are only starting to learn how to exploit? Read more…

San Diego Supercomputer Center to Welcome ‘Expanse’ Supercomputer in 2020

July 18, 2019

With a $10 million dollar award from the National Science Foundation, San Diego Supercomputer Center (SDSC) at the University of California San Diego is procuring a new supercomputer, called Expanse, to be deployed next Read more…

By Staff report

Microsoft Investing $1B in OpenAI Artificial General Intelligence R&D

July 22, 2019

Artificial general intelligence (AGI) is AI’s moonshot, the next giant leap for the AI field. Microsoft regards it to be feasible enough to warrant a $1 billi Read more…

By Doug Black

Informing Designs of Safer, More Efficient Aircraft with Exascale Computing

July 18, 2019

During the process of designing an aircraft, aeronautical engineers must perform predictive simulations to understand how airflow around the plane impacts fligh Read more…

By Rob Johnson

Intel Debuts Pohoiki Beach, Its 8M Neuron Neuromorphic Development System

July 17, 2019

Neuromorphic computing has received less fanfare of late than quantum computing whose mystery has captured public attention and which seems to have generated mo Read more…

By John Russell

Goonhilly Unveils New Immersion-Cooled Platform, Doubles Down on Sustainability Mission

July 16, 2019

Goonhilly Earth Station has opened its new datacenter – an enhancement to its existing tier 3 facility – in Cornwall, England, touting an ambitious commitme Read more…

By Oliver Peckham

ISC19 Cluster Competition: Application Results, Finally!

July 15, 2019

Our exhaustive coverage of the ISC19 Student Cluster Competition continues as we discuss the application scores below. While the scores were typically high, som Read more…

By Dan Olds

Nvidia Expands DGX-Ready AI Program to 19 Countries

July 11, 2019

Nvidia’s DGX-Ready Data Center Program, announced in January and designed to provide colo and public cloud-like options to access the company’s GPU-powered Read more…

By Doug Black

Argonne Team Makes Record Globus File Transfer

July 10, 2019

A team of scientists at Argonne National Laboratory has broken a data transfer record by moving a staggering 2.9 petabytes of data for a research project.  The data – from three large cosmological simulations – was generated and stored on the Summit supercomputer at the Oak Ridge Leadership Computing Facility (OLCF)... Read more…

By Oliver Peckham

Nvidia, Google Tie in Second MLPerf Training ‘At-Scale’ Round

July 10, 2019

Results for the second round of the AI benchmarking suite known as MLPerf were published today with Google Cloud and Nvidia each picking up three wins in the at Read more…

By Tiffany Trader

High Performance (Potato) Chips

May 5, 2006

In this article, we focus on how Procter & Gamble is using high performance computing to create some common, everyday supermarket products. Tom Lange, a 27-year veteran of the company, tells us how P&G models products, processes and production systems for the betterment of consumer package goods. Read more…

By Michael Feldman

Cray, AMD to Extend DOE’s Exascale Frontier

May 7, 2019

Cray and AMD are coming back to Oak Ridge National Laboratory to partner on the world’s largest and most expensive supercomputer. The Department of Energy’s Read more…

By Tiffany Trader

Graphene Surprises Again, This Time for Quantum Computing

May 8, 2019

Graphene is fascinating stuff with promise for use in a seeming endless number of applications. This month researchers from the University of Vienna and Institu Read more…

By John Russell

AMD Verifies Its Largest 7nm Chip Design in Ten Hours

June 5, 2019

AMD announced last week that its engineers had successfully executed the first physical verification of its largest 7nm chip design – in just ten hours. The AMD Radeon Instinct Vega20 – which boasts 13.2 billion transistors – was tested using a TSMC-certified Calibre nmDRC software platform from Mentor. Read more…

By Oliver Peckham

TSMC and Samsung Moving to 5nm; Whither Moore’s Law?

June 12, 2019

With reports that Taiwan Semiconductor Manufacturing Co. (TMSC) and Samsung are moving quickly to 5nm manufacturing, it’s a good time to again ponder whither goes the venerable Moore’s law. Shrinking feature size has of course been the primary hallmark of achieving Moore’s law... Read more…

By John Russell

Deep Learning Competitors Stalk Nvidia

May 14, 2019

There is no shortage of processing architectures emerging to accelerate deep learning workloads, with two more options emerging this week to challenge GPU leader Nvidia. First, Intel researchers claimed a new deep learning record for image classification on the ResNet-50 convolutional neural network. Separately, Israeli AI chip startup Hailo.ai... Read more…

By George Leopold

Nvidia Embraces Arm, Declares Intent to Accelerate All CPU Architectures

June 17, 2019

As the Top500 list was being announced at ISC in Frankfurt today with an upgraded petascale Arm supercomputer in the top third of the list, Nvidia announced its Read more…

By Tiffany Trader

Top500 Purely Petaflops; US Maintains Performance Lead

June 17, 2019

With the kick-off of the International Supercomputing Conference (ISC) in Frankfurt this morning, the 53rd Top500 list made its debut, and this one's for petafl Read more…

By Tiffany Trader

Leading Solution Providers

ISC 2019 Virtual Booth Video Tour

CRAY
CRAY
DDN
DDN
DELL EMC
DELL EMC
GOOGLE
GOOGLE
ONE STOP SYSTEMS
ONE STOP SYSTEMS
PANASAS
PANASAS
VERNE GLOBAL
VERNE GLOBAL

Intel Launches Cascade Lake Xeons with Up to 56 Cores

April 2, 2019

At Intel's Data-Centric Innovation Day in San Francisco (April 2), the company unveiled its second-generation Xeon Scalable (Cascade Lake) family and debuted it Read more…

By Tiffany Trader

Cray – and the Cray Brand – to Be Positioned at Tip of HPE’s HPC Spear

May 22, 2019

More so than with most acquisitions of this kind, HPE’s purchase of Cray for $1.3 billion, announced last week, seems to have elements of that overused, often Read more…

By Doug Black and Tiffany Trader

A Behind-the-Scenes Look at the Hardware That Powered the Black Hole Image

June 24, 2019

Two months ago, the first-ever image of a black hole took the internet by storm. A team of scientists took years to produce and verify the striking image – an Read more…

By Oliver Peckham

Announcing four new HPC capabilities in Google Cloud Platform

April 15, 2019

When you’re running compute-bound or memory-bound applications for high performance computing or large, data-dependent machine learning training workloads on Read more…

By Wyatt Gorman, HPC Specialist, Google Cloud; Brad Calder, VP of Engineering, Google Cloud; Bart Sano, VP of Platforms, Google Cloud

Chinese Company Sugon Placed on US ‘Entity List’ After Strong Showing at International Supercomputing Conference

June 26, 2019

After more than a decade of advancing its supercomputing prowess, operating the world’s most powerful supercomputer from June 2013 to June 2018, China is keep Read more…

By Tiffany Trader

In Wake of Nvidia-Mellanox: Xilinx to Acquire Solarflare

April 25, 2019

With echoes of Nvidia’s recent acquisition of Mellanox, FPGA maker Xilinx has announced a definitive agreement to acquire Solarflare Communications, provider Read more…

By Doug Black

Qualcomm Invests in RISC-V Startup SiFive

June 7, 2019

Investors are zeroing in on the open standard RISC-V instruction set architecture and the processor intellectual property being developed by a batch of high-flying chip startups. Last fall, Esperanto Technologies announced a $58 million funding round. Read more…

By George Leopold

Nvidia Claims 6000x Speed-Up for Stock Trading Backtest Benchmark

May 13, 2019

A stock trading backtesting algorithm used by hedge funds to simulate trading variants has received a massive, GPU-based performance boost, according to Nvidia, Read more…

By Doug Black

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