Ahead by a Century: Utility Supercomputing Advances Stem Cell Research

By Tiffany Trader

October 8, 2012

The use of the term “computer” to mean “calculating machine” dates back to 1897, according to The Oxford English Dictionary, Second Edition. One-hundred and fifteen years later, we’re on the verge of not only exascale calculating machines, but a new era in health care: personalized medicine. This emerging field in which health care decisions and practices are customized to the individual patient using genetic information rests on decades of scientific achievement. And just as advances in digital technology continue to bring HPC into the mainstream, advances in computer science and genomics are democratizing medical care.

Cycle Computing - Victor Ruotti slide imageOne of the key enablers behind both of these trends is cloud computing, a way of delivering computing that relies on economies of scale. Making supercomputing accessible to a new class of user is the purview of utility supercomputing vendor Cycle Computing. In the weeks running up to SC11, Cycle CEO Jason Stowe introduced the Big Science Challenge to demonstrate the capabilities of on-demand supercomputing. What if researchers could have access to virtually unlimited resources, Stowe asked, what kinds of big science questions could they answer?

While the big labs and well-funded researchers from academia and industry very often have access to the largest clusters, there are countless smaller researchers who are relegated to relying on much smaller machines, multicore workstations if they’re lucky, or even generic desktop systems if they’re not. These types of users probably can’t afford a million dollar supercomputer, but what if they could rent such a system, even for a few hours? That is exactly the kind of proposition that Cycle Computing is offering.

In April, Cycle announced the creation of a 50,000 core mega-cluster on behalf of computational chemistry outfit Schrödinger. What would have cost $20-30 million to build from scratch was provisioned using the Amazon EC2 system for $4,828.85 per hour, and Schrödinger researchers were able to analyze 21 million drug compounds in just 3 hours.

Just last week another compelling HPC cloud use case came out of the Cycle-Amazon camp involving Victor Ruotti, a computational biologist with the Morgridge Institute for Research and winner of Cycle’s Big Science Challenge. In March, Ruotti was selected as the recipient of a $10,000 award from Cycle Computing. (Amazon initially promised an additional $2,500, but later upped its share to $9,500.) What appealed to the BigScience Challenge judges including CEO Stowe was the innovative aspect of the work and the potential to benefit humanity with potential disease treatments.

Ruotti is using the computational time to create a knowledgebase indexing system for stem cells and their derivatives. In this era of next-generation sequencing and personalized medicine, stem cell-based therapies will be vital in combating a multitude of diseases, but the pertinent information first needs to be organized into an accessible format – and this is precisely what Ruotti is working toward. When we spoke with Ruotti last week, he was still transferring the results of the run and preparing to build the database.


Ruotti’s Run – Basic Metrics

Using spot instances and some creative thinking, Cycle engineers were able to transform the monetary award into nearly 115 compute years, enabling 11,955 pairs of samples to be processed in one week. The total run cost $19,555, which works out to $0.0175 per core-hour or $116/hr. The project used 5,000 cores on average, 8,000 cores at peak, and accessed 78TB of storage in the Amazon cloud. Cycle noted an equivalent cluster comprised of 400 servers would cost nearly $2,000,000 to purchase outright – 100 times more than the AWS approach, not including the cost of storage.

To arrive at the number of compute years, take the total number of compute hours (1,003,404) and divide by the hours in one compute year (8,760 hours), which comes out to 114.54 years. In earth years, this would mean starting the calculation on a single-core server in 1897 in order to finish in 2012. 1897 just so happens to be the year that the term computer, as an electric-computation device, was first used.

“If you look at it that way, we could have started this calculation on a single-core server back in 1897,” remarked Stowe, “ran it through the entirety of the 20-century, from jazz of the roaring 20s, through the depression to the space race and the cold war and disco in the 80s and grudge and techno, all the way to Gangnam style, and finishing this year.”


Ruotti’s Run – Additional Information

>> NEXT: Spot Instances Save Money

Everyone on the project wanted to get the most out of the award dollars so the Cycle engineers considered the problem carefully. By employing Amazon’s spot pricing, the budget stretched to accommodate 114.5 years of computation, whereas the same money put toward on-demand instances would have generated just nine years of compute. The spot instance approach extracted nearly 13 times more computing from the award spend; however, there was a catch, as Ian Alderman, Cycle’s senior software engineer, explained: “If we bid, say, 15 cents, and the market for the server is 14 cents, we pay 14 cents, but if the market crosses 15 cents and goes up to 16 cents, then we lose the server and the job is interrupted.”

So the engineers needed to optimize the workflow to run on spots, while not allowing the interruptions to impact the workloads. This necessitated breaking the job into small components and being able to restart workloads as close to where they left off as possible. To provision the large number of instances, the team used schedulers, such as GridEngine, HTCondor, and Torque, and configured the cluster with Opscode Chef.

Compared to the 50,000 core use case, this job was about efficiency, said Stowe. Making the most of the compute-hours and supporting interruptions were key goals. Another aspect that was different was that instead of dealing with molecular data, this project processed genomic data. At its peak, the current project used 78 TB of data, bumping it into big data territory.

When I asked Stowe if it was fair to draw conclusions about utility supercomputing based on embarrassingly parallel workloads, he noted that more and more science workloads that were once rigid in their parallelism are now “pleasantly-parallel,” especially when it comes to the analysis phase. This enables researchers to achieve scale without the need for expensive high-speed interconnects and allows more options in how you run the computation (public cloud, private cloud, hybrid model, etc.). There’s a shift going on and genomics is a prime example, said Stowe. The massive amount of data coming off of instruments is inherently data-parallel and well-suited for high-throughput use cases.

Ruotti, for his part, was eager to cover the merits of the project, and noted how the Amazon-Cycle run generated enough data to build a useful resource that will in turn support future genomics work.

He explained how he and his colleagues at Morgridge Institute have established a large collection of genetic samples, including human embryonic cells and cells that are in the process of differentiating into other cell types. Out of about 800 samples that they’ve accumulated, they selected 124 samples for this project. Normally they would analyze the samples one by one depending on the needs of a given project, but the basis for this project is to run the comparison in an n-squared algorithm. Doing analysis on these 124×124 sample pairs, then gathering and recording information on which pairs are closer and farther apart.

The goal is to eventually build an inventory of every cell type they have in the lab. To begin with, this will allow them to cluster all the samples, but Ruotti notes an equally important benefit. The differentiating process is not linear, meaning that there are a lot of pathways a given cell can go into. The more information they have on the probabilities and ramifications of the different types of cell divergence, the more control they will be able to exert on the process of turning embryonic cells into desired cell types. So far they have developed some good protocols for transforming the embryonic cells into neural cells, hepatic cells and muscle cells, but there are still a lot of unanswered questions regarding the process.

Right now, the group at Morgridge is focused on building a useful resource with the data that they have, and the hope is that as they get more samples, they will be able to keep adding to the inventory in a streamlined way. And while Ruotti characterized the current stage as proof-of-concept, that did not get in the way of his enthusiasm or forward-thinking aspirations. He noted that there are other repositories of raw data such as the SCOR database, and perhaps these could be added to the inventory as well. He is confident that as more and more labs will start doing large genomics runs, they too will need a resource for querying samples.

As exciting as these first steps are, they open up the door to even more ground-breaking science and discovery, Ruotti remarked. “The field of next-generation sequencing is growing at an exponential rate,” he added, “We’re only going to get more data as the companies push the boundary on longer reads and more samples per run.”

The indexing system will provide a way for scientists to obtain information on the most current genes being looked at for their potential for treatments and cures. There are some resources out there currently, Ruotti noted, but they are not up-to-date as far as the latest next-generation sequencing and in terms of RNA, so his group hopes that the new system will provide the best way for scientists to query for their favorite genes.

As for in-house resources, the Morgridge Institute currently has a sequencer from Illumina, a 40-core Sun Grid Engine cluster. While considered a large cluster several years ago, it’s now one of their smaller resources. Although it’s generally sufficient for extracting information from one experiment, the process takes a few hours, and when there are multiple samples, this cluster becomes a bottleneck, Ruotti said. Public cloud resources, like the Cycle/Amazon solution, are also on their radar. The Morgridge Institute is in talks with the Condor Project to discuss ways to supplement their current resources with public cloud.

On the subject of using owned and rented computing resources in a complementary way, Stowe discussed some IP they’ve developed called Cement-Once. This is basically a cloud-bursting mechanism that takes advantage of as much internal capacity as possible, and when needed will provision additional resources externally.

“We definitely think there are large portions of our customer base that have internal HPC and potentially want to be able to run large compute both internally and externally when it’s appropriate, so we’ve done a considerable amount of work in enabling that area,” Stowe remarked. “We see that across multiple portions of our customer base. Internal environments are too small when you need them the most and too large every other time. Cloud has the potential to balance these imbalances.”

With the final bits from the 2011 contest still streaming in, Cycle Computing is keeping the momentum going with the announcement of a second annual BigScience Challenge. Interested applicants are asked to complete an entry explaining who they are and what big question they want to answer. Any and all researchers are invited to apply, but the focus for the contest is on big data and big compute problems and their big benefits to humanity.

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!

SC Bids Farewell to Denver, Heads to Dallas for 30th

November 17, 2017

After a jam-packed four-day expo and intensive six-day technical program, SC17 has wrapped up another successful event that brought together nearly 13,000 visitors to the Colorado Convention Center in Denver for the larg Read more…

By Tiffany Trader

SC17 Keynote – HPC Powers SKA Efforts to Peer Deep into the Cosmos

November 17, 2017

This week’s SC17 keynote – Life, the Universe and Computing: The Story of the SKA Telescope – was a powerful pitch for the potential of Big Science projects that also showcased the foundational role of high performance computing in modern science. It was also visually stunning. Read more…

By John Russell

How Cities Use HPC at the Edge to Get Smarter

November 17, 2017

Cities are sensoring up, collecting vast troves of data that they’re running through predictive models and using the insights to solve problems that, in some cases, city managers didn’t even know existed. Speaking Read more…

By Doug Black

HPE Extreme Performance Solutions

Harness Scalable Petabyte Storage with HPE Apollo 4510 and HPE StoreEver

As a growing number of connected devices challenges IT departments to rapidly collect, manage, and store troves of data, organizations must adopt a new generation of IT to help them operate quickly and intelligently. Read more…

SC17 Student Cluster Competition Configurations: Fewer Nodes, Way More Accelerators

November 16, 2017

The final configurations for each of the SC17 “Donnybrook in Denver” Student Cluster Competition have been released. Fortunately, each team received their equipment shipments on time and undamaged, so the teams are r Read more…

By Dan Olds

SC Bids Farewell to Denver, Heads to Dallas for 30th

November 17, 2017

After a jam-packed four-day expo and intensive six-day technical program, SC17 has wrapped up another successful event that brought together nearly 13,000 visit Read more…

By Tiffany Trader

SC17 Keynote – HPC Powers SKA Efforts to Peer Deep into the Cosmos

November 17, 2017

This week’s SC17 keynote – Life, the Universe and Computing: The Story of the SKA Telescope – was a powerful pitch for the potential of Big Science projects that also showcased the foundational role of high performance computing in modern science. It was also visually stunning. Read more…

By John Russell

How Cities Use HPC at the Edge to Get Smarter

November 17, 2017

Cities are sensoring up, collecting vast troves of data that they’re running through predictive models and using the insights to solve problems that, in some Read more…

By Doug Black

Student Cluster LINPACK Record Shattered! More LINs Packed Than Ever before!

November 16, 2017

Nanyang Technological University, the pride of Singapore, utterly destroyed the Student Cluster Competition LINPACK record by posting a score of 51.77 TFlop/s a Read more…

By Dan Olds

Hyperion Market Update: ‘Decent’ Growth Led by HPE; AI Transparency a Risk Issue

November 15, 2017

The HPC market update from Hyperion Research (formerly IDC) at the annual SC conference is a business and social “must,” and this year’s presentation at S Read more…

By Doug Black

Nvidia Focuses Its Cloud Containers on HPC Applications

November 14, 2017

Having migrated its top-of-the-line datacenter GPU to the largest cloud vendors, Nvidia is touting its Volta architecture for a range of scientific computing ta Read more…

By George Leopold

HPE Launches ARM-based Apollo System for HPC, AI

November 14, 2017

HPE doubled down on its memory-driven computing vision while expanding its processor portfolio with the announcement yesterday of the company’s first ARM-base Read more…

By Doug Black

OpenACC Shines in Global Climate/Weather Codes

November 14, 2017

OpenACC, the directive-based parallel programming model used mostly for porting codes to GPUs for use on heterogeneous systems, came to SC17 touting impressive 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

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

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

AMD Showcases Growing Portfolio of EPYC and Radeon-based Systems at SC17

November 13, 2017

AMD’s charge back into HPC and the datacenter is on full display at SC17. Having launched the EPYC processor line in June along with its MI25 GPU the focus he Read more…

By John Russell

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

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

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

Leading Solution Providers

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

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

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

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

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

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

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