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!

ARM Waving: Attention, Deployments, and Development

January 18, 2017

It’s been a heady two weeks for the ARM HPC advocacy camp. At this week’s Mont-Blanc Project meeting held at the Barcelona Supercomputer Center, Cray announced plans to build an ARM-based supercomputer in the U.K. while Mont-Blanc selected Cavium’s ThunderX2 ARM chip for its third phase of development. Last week, France’s CEA and Japan’s Riken announced a deep collaboration aimed largely at fostering the ARM ecosystem. This activity follows a busy 2016 when SoftBank acquired ARM, OpenHPC announced ARM support, ARM released its SVE spec, Fujistu chose ARM for the post K machine, and ARM acquired HPC tool provider Allinea in December. Read more…

By John Russell

Women Coders from Russia, Italy, and Poland Top Study

January 17, 2017

According to a study posted on HackerRank today the best women coders as judged by performance on HackerRank challenges come from Russia, Italy, and Poland. Read more…

By John Russell

Spurred by Global Ambitions, Inspur in Joint HPC Deal with DDN

January 17, 2017

Inspur, the fast-growth cloud computing and server vendor from China that has several systems on the current Top500 list, and DDN, a leader in high-end storage, have announced a joint sales and marketing agreement to produce solutions based on DDN storage platforms integrated with servers, networking, software and services from Inspur. Read more…

By Doug Black

Weekly Twitter Roundup (Jan. 12, 2017)

January 12, 2017

Here at HPCwire, we aim to keep the HPC community apprised of the most relevant and interesting news items that get tweeted throughout the week. Read more…

By Thomas Ayres

HPE Extreme Performance Solutions

Remote Visualization: An Integral Technology for Upstream Oil & Gas

As the exploration and production (E&P) of natural resources evolves into an even more complex and vital task, visualization technology has become integral for the upstream oil and gas industry. Read more…

NSF Seeks Input on Cyberinfrastructure Advances Needed

January 12, 2017

In cased you missed it, the National Science Foundation posted a “Dear Colleague Letter” (DCL) late last week seeking input on needs for the next generation of cyberinfrastructure to support science and engineering. Read more…

By John Russell

NSF Approves Bridges Phase 2 Upgrade for Broader Research Use

January 12, 2017

The recently completed phase 2 upgrade of the Bridges supercomputer at the Pittsburgh Supercomputing Center (PSC) has been approved by the National Science Foundation (NSF) making it now available for research allocations to the national scientific community, according to an announcement posted this week on the XSEDE web site. Read more…

By John Russell

Clemson Software Optimizes Big Data Transfers

January 11, 2017

Data-intensive science is not a new phenomenon as the high-energy physics and astrophysics communities can certainly attest, but today more and more scientists are facing steep data and throughput challenges fueled by soaring data volumes and the demands of global-scale collaboration. Read more…

By Tiffany Trader

For IBM/OpenPOWER: Success in 2017 = (Volume) Sales

January 11, 2017

To a large degree IBM and the OpenPOWER Foundation have done what they said they would – assembling a substantial and growing ecosystem and bringing Power-based products to market, all in about three years. Read more…

By John Russell

ARM Waving: Attention, Deployments, and Development

January 18, 2017

It’s been a heady two weeks for the ARM HPC advocacy camp. At this week’s Mont-Blanc Project meeting held at the Barcelona Supercomputer Center, Cray announced plans to build an ARM-based supercomputer in the U.K. while Mont-Blanc selected Cavium’s ThunderX2 ARM chip for its third phase of development. Last week, France’s CEA and Japan’s Riken announced a deep collaboration aimed largely at fostering the ARM ecosystem. This activity follows a busy 2016 when SoftBank acquired ARM, OpenHPC announced ARM support, ARM released its SVE spec, Fujistu chose ARM for the post K machine, and ARM acquired HPC tool provider Allinea in December. Read more…

By John Russell

Spurred by Global Ambitions, Inspur in Joint HPC Deal with DDN

January 17, 2017

Inspur, the fast-growth cloud computing and server vendor from China that has several systems on the current Top500 list, and DDN, a leader in high-end storage, have announced a joint sales and marketing agreement to produce solutions based on DDN storage platforms integrated with servers, networking, software and services from Inspur. Read more…

By Doug Black

For IBM/OpenPOWER: Success in 2017 = (Volume) Sales

January 11, 2017

To a large degree IBM and the OpenPOWER Foundation have done what they said they would – assembling a substantial and growing ecosystem and bringing Power-based products to market, all in about three years. Read more…

By John Russell

UberCloud Cites Progress in HPC Cloud Computing

January 10, 2017

200 HPC cloud experiments, 80 case studies, and a ton of hands-on experience gained, that’s the harvest of four years of UberCloud HPC Experiments. Read more…

By Wolfgang Gentzsch and Burak Yenier

A Conversation with Women in HPC Director Toni Collis

January 6, 2017

In this SC16 video interview, HPCwire Managing Editor Tiffany Trader sits down with Toni Collis, the director and founder of the Women in HPC (WHPC) network, to discuss the strides made since the organization’s debut in 2014. Read more…

By Tiffany Trader

BioTeam’s Berman Charts 2017 HPC Trends in Life Sciences

January 4, 2017

Twenty years ago high performance computing was nearly absent from life sciences. Today it’s used throughout life sciences and biomedical research. Genomics and the data deluge from modern lab instruments are the main drivers, but so is the longer-term desire to perform predictive simulation in support of Precision Medicine (PM). There’s even a specialized life sciences supercomputer, ‘Anton’ from D.E. Shaw Research, and the Pittsburgh Supercomputing Center is standing up its second Anton 2 and actively soliciting project proposals. There’s a lot going on. Read more…

By John Russell

Fast Rewind: 2016 Was a Wild Ride for HPC

December 23, 2016

Some years quietly sneak by – 2016 not so much. It’s safe to say there are always forces reshaping the HPC landscape but this year’s bunch seemed like a noisy lot. Among the noisemakers: TaihuLight, DGX-1/Pascal, Dell EMC & HPE-SGI et al., KNL to market, OPA-IB chest thumping, Fujitsu-ARM, new U.S. President-elect, BREXIT, JR’s Intel Exit, Exascale (whatever that means now), NCSA@30, whither NSCI, Deep Learning mania, HPC identity crisis…You get the picture. Read more…

By John Russell

AWI Uses New Cray Cluster for Earth Sciences and Bioinformatics

December 22, 2016

The Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (AWI), headquartered in Bremerhaven, Germany, is one of the country's premier research institutes within the Helmholtz Association of German Research Centres, and is an internationally respected center of expertise for polar and marine research. In November 2015, AWI awarded Cray a contract to install a cluster supercomputer that would help the institute accelerate time to discovery. Now the effort is starting to pay off. Read more…

By Linda Barney

AWS Beats Azure to K80 General Availability

September 30, 2016

Amazon Web Services has seeded its cloud with Nvidia Tesla K80 GPUs to meet the growing demand for accelerated computing across an increasingly-diverse range of workloads. The P2 instance family is a welcome addition for compute- and data-focused users who were growing frustrated with the performance limitations of Amazon's G2 instances, which are backed by three-year-old Nvidia GRID K520 graphics cards. Read more…

By Tiffany Trader

US, China Vie for Supercomputing Supremacy

November 14, 2016

The 48th edition of the TOP500 list is fresh off the presses and while there is no new number one system, as previously teased by China, there are a number of notable entrants from the US and around the world and significant trends to report on. Read more…

By Tiffany Trader

Vectors: How the Old Became New Again in Supercomputing

September 26, 2016

Vector instructions, once a powerful performance innovation of supercomputing in the 1970s and 1980s became an obsolete technology in the 1990s. But like the mythical phoenix bird, vector instructions have arisen from the ashes. Here is the history of a technology that went from new to old then back to new. Read more…

By Lynd Stringer

For IBM/OpenPOWER: Success in 2017 = (Volume) Sales

January 11, 2017

To a large degree IBM and the OpenPOWER Foundation have done what they said they would – assembling a substantial and growing ecosystem and bringing Power-based products to market, all in about three years. Read more…

By John Russell

Container App ‘Singularity’ Eases Scientific Computing

October 20, 2016

HPC container platform Singularity is just six months out from its 1.0 release but already is making inroads across the HPC research landscape. It's in use at Lawrence Berkeley National Laboratory (LBNL), where Singularity founder Gregory Kurtzer has worked in the High Performance Computing Services (HPCS) group for 16 years. Read more…

By Tiffany Trader

Dell EMC Engineers Strategy to Democratize HPC

September 29, 2016

The freshly minted Dell EMC division of Dell Technologies is on a mission to take HPC mainstream with a strategy that hinges on engineered solutions, beginning with a focus on three industry verticals: manufacturing, research and life sciences. "Unlike traditional HPC where everybody bought parts, assembled parts and ran the workloads and did iterative engineering, we want folks to focus on time to innovation and let us worry about the infrastructure," said Jim Ganthier, senior vice president, validated solutions organization at Dell EMC Converged Platforms Solution Division. Read more…

By Tiffany Trader

Lighting up Aurora: Behind the Scenes at the Creation of the DOE’s Upcoming 200 Petaflops Supercomputer

December 1, 2016

In April 2015, U.S. Department of Energy Undersecretary Franklin Orr announced that Intel would be the prime contractor for Aurora: Read more…

By Jan Rowell

Enlisting Deep Learning in the War on Cancer

December 7, 2016

Sometime in Q2 2017 the first ‘results’ of the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) will become publicly available according to Rick Stevens. He leads one of three JDACS4C pilot projects pressing deep learning (DL) into service in the War on Cancer. Read more…

By John Russell

Leading Solution Providers

D-Wave SC16 Update: What’s Bo Ewald Saying These Days

November 18, 2016

Tucked in a back section of the SC16 exhibit hall, quantum computing pioneer D-Wave has been talking up its new 2000-qubit processor announced in September. Forget for a moment the criticism sometimes aimed at D-Wave. This small Canadian company has sold several machines including, for example, ones to Lockheed and NASA, and has worked with Google on mapping machine learning problems to quantum computing. In July Los Alamos National Laboratory took possession of a 1000-quibit D-Wave 2X system that LANL ordered a year ago around the time of SC15. Read more…

By John Russell

CPU Benchmarking: Haswell Versus POWER8

June 2, 2015

With OpenPOWER activity ramping up and IBM’s prominent role in the upcoming DOE machines Summit and Sierra, it’s a good time to look at how the IBM POWER CPU stacks up against the x86 Xeon Haswell CPU from Intel. Read more…

By Tiffany Trader

Nvidia Sees Bright Future for AI Supercomputing

November 23, 2016

Graphics chipmaker Nvidia made a strong showing at SC16 in Salt Lake City last week. Read more…

By Tiffany Trader

New Genomics Pipeline Combines AWS, Local HPC, and Supercomputing

September 22, 2016

Declining DNA sequencing costs and the rush to do whole genome sequencing (WGS) of large cohort populations – think 5000 subjects now, but many more thousands soon – presents a formidable computational challenge to researchers attempting to make sense of large cohort datasets. Read more…

By John Russell

Beyond von Neumann, Neuromorphic Computing Steadily Advances

March 21, 2016

Neuromorphic computing – brain inspired computing – has long been a tantalizing goal. The human brain does with around 20 watts what supercomputers do with megawatts. And power consumption isn’t the only difference. Fundamentally, brains ‘think differently’ than the von Neumann architecture-based computers. While neuromorphic computing progress has been intriguing, it has still not proven very practical. Read more…

By John Russell

The Exascale Computing Project Awards $39.8M to 22 Projects

September 7, 2016

The Department of Energy’s Exascale Computing Project (ECP) hit an important milestone today with the announcement of its first round of funding, moving the nation closer to its goal of reaching capable exascale computing by 2023. Read more…

By Tiffany Trader

Dell Knights Landing Machine Sets New STAC Records

November 2, 2016

The Securities Technology Analysis Center, commonly known as STAC, has released a new report characterizing the performance of the Knight Landing-based Dell PowerEdge C6320p server on the STAC-A2 benchmarking suite, widely used by the financial services industry to test and evaluate computing platforms. The Dell machine has set new records for both the baseline Greeks benchmark and the large Greeks benchmark. Read more…

By Tiffany Trader

What Knights Landing Is Not

June 18, 2016

As we get ready to launch the newest member of the Intel Xeon Phi family, code named Knights Landing, it is natural that there be some questions and potentially some confusion. Read more…

By James Reinders, Intel

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