Scaling the Super Cloud

By Nicole Hemsoth

January 15, 2014

“The number one problem we face as humanity is getting people to think outside of the boxes they bought,” says Cycle Computing CEO, Jason Stowe.

His company has made big waves and proven that the combination of Amazon servers and their own innovations can open new infrastructure options for users with HPC applications. For instance, they recently spun up a 156,000-core Amazon Web Services (AWS) cluster for Schrödinger to power a quantum chemistry application across 8 geographical regions. While many of you can project what a supercomputer of that magnitude might cost, the duration of their run to sort compounds cost them around $33,000—and ran in less than a day distributed across 16,788 instances.

They’ve done similar projects at massive scale for a number of other users in life sciences and beyond—but as they continue to scale, they’ve encountered some of the same bare metal challenges HPC centers do, with the added complexity of adding compute across multiple regions, different datacenters, and the need to shut down and spin up machines in a more complicated fashion than an in-house supercomputer might.

The answer to these challenges is found in the company’s own custom-developed Jupiter, the code name for an out-of-this-world HPC cloud management tool that tackles a few key challenges of running large, complex workloads on AWS.

“Back when we did the 50,000 core and million hour runs, at a certain point, scaling the task distribution environment became particularly problematic because traditional batch schedulers and service oriented architectures aren’t geared toward large amounts of compute power coming and going as a workload increases and decreases,” said Stowe. “Also, these environments aren’t very failure friendly—we needed to develop something that would meet both scale and failure requirements.”

This required from-scratch development on Cycle’s part, however, since the workload management options that they might have tweaked (Stowe cites solid ones, including Condor, Grid Engine, PBS, and Platform/IBM) lacked the capabilities for cloud environments and the types of workload tricks needed to run HPC cloud jobs.

“With a lot of the supercomputing environments now that have millions of processors, the schedulers on those are really good at telling all of those processors to do one MPI job. But what we wanted is the exact opposite—we wanted some that could tell hundreds of thousands or millions of processors to do several thousand things at a one time.” In other words, it wasn’t a “simple” matter of telling the cloud-based system to handle one MPI job, for example. It would be doing 50,000 or more MPI jobs inside the distributed computing environment. “We didn’t want to do a batch necessarily but we wanted to support low overhead scheduling so you can do more programmatic scheduling of workloads and get interactive results back.”

One of the other challenges of working with servers across several geographic regions is making sure that there’s built-in fault tolerance as well as an eye on efficiency. Prices and compute cycles are in a state of flux, so Cycle needed to build in the ability to turn off entire servers, datacenters and even regions if needed to keep applications going in the event of downtime. Stowe says they experimented with this feature, which is both manual or automated depending on user policies. They shut down all the processors in Australia during one experimental run because they weren’t getting enough juice, which rerouted that processing to another region.

In terms of the overhead for Jupiter, Stowe says that there are very few servers required. “We were recently able to manage 16,000 servers with only a handful of servers—under 20,” Stowe said. These few servers provided all the task distribution services for the 156,000-core run across 8 geographic regions and if we needed to, we could have gone with fewer. The only reason we didn’t is because we wanted to have one head node in each region.”

The Chef-based Jupiter tools were built from the ground up, with early lessons about how to make a highly scalable, low overhead cloud scheduler coming from work in 2009 for a custom financial services cloud project. The goals toward scalability and reliability were similar, but they’ve been able to make the offering robust enough to tackle the Schrödinger example cost effectively and in the manner they’d hoped.

Cycle will ramp up the story and accessibility of Jupiter (named after the planet, which has massive clouds) in 2014 in ways similar to what happened with Yahoo and Hadoop. “We’ve had significant vetting around this software, we’re working toward making it easy to download so it will be more widely available.”

Despite the often-cited challenges for HPC clouds, including higher latencies, security and other perceived barriers, clouds adoption in high performance computing is growing. Just a few years ago, only around 10% of HPC sites reported using clouds, but according to the most recent IDC estimates, it’s jumped to close to 24%. While this can lead to a discussion about public versus private clouds (as the considerations are somewhat different), Stowe sees this is an affirmation of what his company has been pushing for the last several years—the idea that clouds can be rendered robust enough to perform well for complex applications at massive scale without borders.

The technical hurdles including security, onboarding applications, operational management, reporting and running cost effectively at high performance are being addressed in the many hyperscale environments that provide the web service many of us count on—from Facebook to Netflix and Google. Stowe and his company have stashed away lessons and tools from that world and meshed them with their long experiences working with HPC applications.

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!

2017 Gordon Bell Prize Finalists Named

October 23, 2017

The three finalists for this year’s Gordon Bell Prize in High Performance Computing have been announced. They include two papers on projects run on China’s Sunway TaihuLight system and a third paper on 3D image recon Read more…

By John Russell

Data Vortex Users Contemplate the Future of Supercomputing

October 19, 2017

Last month (Sept. 11-12), HPC networking company Data Vortex held its inaugural users group at Pacific Northwest National Laboratory (PNNL) bringing together about 30 participants from industry, government and academia t Read more…

By Tiffany Trader

AI Self-Training Goes Forward at Google DeepMind

October 19, 2017

DeepMind, Google’s AI research organization, announced today in a blog that AlphaGo Zero, the latest evolution of AlphaGo (the first computer program to defeat a Go world champion) trained itself within three days to play Go at a superhuman level (i.e., better than any human) – and to beat the old version of AlphaGo – without leveraging human expertise, data or training. Read more…

By Doug Black

HPE Extreme Performance Solutions

Transforming Genomic Analytics with HPC-Accelerated Insights

Advancements in the field of genomics are revolutionizing our understanding of human biology, rapidly accelerating the discovery and treatment of genetic diseases, and dramatically improving human health. Read more…

Researchers Scale COSMO Climate Code to 4888 GPUs on Piz Daint

October 17, 2017

Effective global climate simulation, sorely needed to anticipate and cope with global warming, has long been computationally challenging. Two of the major obstacles are the needed resolution and prolonged time to compute Read more…

By John Russell

Data Vortex Users Contemplate the Future of Supercomputing

October 19, 2017

Last month (Sept. 11-12), HPC networking company Data Vortex held its inaugural users group at Pacific Northwest National Laboratory (PNNL) bringing together ab Read more…

By Tiffany Trader

AI Self-Training Goes Forward at Google DeepMind

October 19, 2017

DeepMind, Google’s AI research organization, announced today in a blog that AlphaGo Zero, the latest evolution of AlphaGo (the first computer program to defeat a Go world champion) trained itself within three days to play Go at a superhuman level (i.e., better than any human) – and to beat the old version of AlphaGo – without leveraging human expertise, data or training. Read more…

By Doug Black

Student Cluster Competition Coverage New Home

October 16, 2017

Hello computer sports fans! This is the first of many (many!) articles covering the world-wide phenomenon of Student Cluster Competitions. Finally, the Student Read more…

By Dan Olds

Intel Delivers 17-Qubit Quantum Chip to European Research Partner

October 10, 2017

On Tuesday, Intel delivered a 17-qubit superconducting test chip to research partner QuTech, the quantum research institute of Delft University of Technology (TU Delft) in the Netherlands. The announcement marks a major milestone in the 10-year, $50-million collaborative relationship with TU Delft and TNO, the Dutch Organization for Applied Research, to accelerate advancements in quantum computing. Read more…

By Tiffany Trader

Fujitsu Tapped to Build 37-Petaflops ABCI System for AIST

October 10, 2017

Fujitsu announced today it will build the long-planned AI Bridging Cloud Infrastructure (ABCI) which is set to become the fastest supercomputer system in Japan Read more…

By John Russell

HPC Chips – A Veritable Smorgasbord?

October 10, 2017

For the first time since AMD's ill-fated launch of Bulldozer the answer to the question, 'Which CPU will be in my next HPC system?' doesn't have to be 'Whichever variety of Intel Xeon E5 they are selling when we procure'. Read more…

By Dairsie Latimer

Delays, Smoke, Records & Markets – A Candid Conversation with Cray CEO Peter Ungaro

October 5, 2017

Earlier this month, Tom Tabor, publisher of HPCwire and I had a very personal conversation with Cray CEO Peter Ungaro. Cray has been on something of a Cinderell Read more…

By Tiffany Trader & Tom Tabor

Intel Debuts Programmable Acceleration Card

October 5, 2017

With a view toward supporting complex, data-intensive applications, such as AI inference, video streaming analytics, database acceleration and genomics, Intel i Read more…

By Doug Black

Reinders: “AVX-512 May Be a Hidden Gem” in Intel Xeon Scalable Processors

June 29, 2017

Imagine if we could use vector processing on something other than just floating point problems.  Today, GPUs and CPUs work tirelessly to accelerate algorithms Read more…

By James Reinders

NERSC Scales Scientific Deep Learning to 15 Petaflops

August 28, 2017

A collaborative effort between Intel, NERSC and Stanford has delivered the first 15-petaflops deep learning software running on HPC platforms and is, according Read more…

By Rob Farber

Oracle Layoffs Reportedly Hit SPARC and Solaris Hard

September 7, 2017

Oracle’s latest layoffs have many wondering if this is the end of the line for the SPARC processor and Solaris OS development. As reported by multiple sources Read more…

By John Russell

US Coalesces Plans for First Exascale Supercomputer: Aurora in 2021

September 27, 2017

At the Advanced Scientific Computing Advisory Committee (ASCAC) meeting, in Arlington, Va., yesterday (Sept. 26), it was revealed that the "Aurora" supercompute Read more…

By Tiffany Trader

How ‘Knights Mill’ Gets Its Deep Learning Flops

June 22, 2017

Intel, the subject of much speculation regarding the delayed, rewritten or potentially canceled “Aurora” contract (the Argonne Lab part of the CORAL “ Read more…

By Tiffany Trader

Google Releases Deeplearn.js to Further Democratize Machine Learning

August 17, 2017

Spreading the use of machine learning tools is one of the goals of Google’s PAIR (People + AI Research) initiative, which was introduced in early July. Last w Read more…

By John Russell

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

GlobalFoundries Puts Wind in AMD’s Sails with 12nm FinFET

September 24, 2017

From its annual tech conference last week (Sept. 20), where GlobalFoundries welcomed more than 600 semiconductor professionals (reaching the Santa Clara venue Read more…

By Tiffany Trader

Leading Solution Providers

Graphcore Readies Launch of 16nm Colossus-IPU Chip

July 20, 2017

A second $30 million funding round for U.K. AI chip developer Graphcore sets up the company to go to market with its “intelligent processing unit” (IPU) in Read more…

By Tiffany Trader

Amazon Debuts New AMD-based GPU Instances for Graphics Acceleration

September 12, 2017

Last week Amazon Web Services (AWS) streaming service, AppStream 2.0, introduced a new GPU instance called Graphics Design intended to accelerate graphics. The Read more…

By John Russell

EU Funds 20 Million Euro ARM+FPGA Exascale Project

September 7, 2017

At the Barcelona Supercomputer Centre on Wednesday (Sept. 6), 16 partners gathered to launch the EuroEXA project, which invests €20 million over three-and-a-half years into exascale-focused research and development. Led by the Horizon 2020 program, EuroEXA picks up the banner of a triad of partner projects — ExaNeSt, EcoScale and ExaNoDe — building on their work... Read more…

By Tiffany Trader

Delays, Smoke, Records & Markets – A Candid Conversation with Cray CEO Peter Ungaro

October 5, 2017

Earlier this month, Tom Tabor, publisher of HPCwire and I had a very personal conversation with Cray CEO Peter Ungaro. Cray has been on something of a Cinderell Read more…

By Tiffany Trader & Tom Tabor

Cray Moves to Acquire the Seagate ClusterStor Line

July 28, 2017

This week Cray announced that it is picking up Seagate's ClusterStor HPC storage array business for an undisclosed sum. "In short we're effectively transitioning the bulk of the ClusterStor product line to Cray," said CEO Peter Ungaro. Read more…

By Tiffany Trader

Intel Launches Software Tools to Ease FPGA Programming

September 5, 2017

Field Programmable Gate Arrays (FPGAs) have a reputation for being difficult to program, requiring expertise in specialty languages, like Verilog or VHDL. Easin Read more…

By Tiffany Trader

IBM Advances Web-based Quantum Programming

September 5, 2017

IBM Research is pairing its Jupyter-based Data Science Experience notebook environment with its cloud-based quantum computer, IBM Q, in hopes of encouraging a new class of entrepreneurial user to solve intractable problems that even exceed the capabilities of the best AI systems. Read more…

By Alex Woodie

HPC Chips – A Veritable Smorgasbord?

October 10, 2017

For the first time since AMD's ill-fated launch of Bulldozer the answer to the question, 'Which CPU will be in my next HPC system?' doesn't have to be 'Whichever variety of Intel Xeon E5 they are selling when we procure'. Read more…

By Dairsie Latimer

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