Opening Sequences for HPC on Demand

By Nicole Hemsoth

May 11, 2011

Next generation DNA sequencing has brought a wealth of opportunities in research, pharmaceutical and clinical contexts, but for those who are in the high performance computing space, this particular market is bursting with a different array of opportunities. From specialty clusters dedicated exclusively to crunching the overwhelming amounts of data coming of sequencers (not to mention the storage might to keep it all in check) the biosciences industry is a prime target for vendors of all stripes.

Interestingly, with the rise of cloud computing and on-demand resources, investment in hardware for many companies isn’t always the first option. According to Tom Coull, Senior Vice President at Penguin Computing, a large number of DNA-driven companies are finding on-demand HPC a perfect fit, especially since their demands for high throughput computing are large but generally sporadic.

Providers of on-demand high performance computing that have an eye on this particular industry (Penguin Computing, Cycle Computing, and SGI in particular) have little elbow room in this tight market to garner valuable life sciences business. In addition to competing with public cloud resources like Amazon EC2, not to mention competition from traditional modes of computing (buying your own cluster) such services have to run a tight ship to keep their own hardware investments churning at peak capacity.

This issue of peak capacity is critical for both users of on-demand HPC and for the providers themselves. Naturally a provider like Penguin wants to make sure their investment is being fully utilized and they’re retuning a profit on the core hours spent. On the flip side, however, life sciences companies want to make sure that they’re balancing time-to-market concerns with core competency arguments.

To be more specific about this balance of issues, we spoke to Abe Lietz who heads IT for a major life sciences firm, Life Technologies. This global company provides a range of solutions for customers in the industry, from biological products for research to the instrumentation to back next-generation DNA sequencing efforts. In short, as Abe told us, “our core competency is about keeping pace with a rapidly changing industry; things change quickly and it’s not part of our goal to put the extreme time and resources into running our own IT the right way.”

Life Technologies is using Penguin Computing’s HPC on-demand (POD) offering to back a web interface into one of its most popular software packages for gene sequence analysis, Bioscope. While on the surface this might sound like a simple enough offering, the complexity of Bioscope and the fact that it is residing on collocated servers in Salt Lake City goes deeper than one might imagine.

Users log in through solidbioscope.com and are able to use the pay as you go model to analyze genomic data, using Penguin’s storage and resources exclusively. Penguin’s Coull noted that the pricing is roughly equivalent to what you might get with a similar cloud provider but unlike with a public cloud, users are able to know exactly where their data is at any given moment—an important issue for the HIPPA compliance-aware.

Coull also noted that for genomics researchers considering this from a purely cost-driven basis, if you’ve built and maintained a cluster based on peak requirements and you’re not using it at 35 percent on a full-time basis, you’re better off using an on-demand resource provider.  During our phone interview he was watching POD activity from his screen and noted that of the applications that were running at any given moment, a good estimation is that 50% of users have replaced their in-house systems electing to use POD exclusively while the other half were the sporadic users who make up a nice portion of the life sciences on-demand market due to the spotty need for big computation.

On a side note, Coull says that Penguin expects 4-fold growth over the next year for their POD service with the build-out of two AMD and Intel partnerships for new POD centers. Although he didn’t comment what percent of the business was life sciences driven, he noted this market was “significant” and that they’d seen a surprising uptick from academic institutions that needed extra resources.

Coull noted as well that their software stack has been tweaked by users to be able to bridge over to other cloud computing options, including Amazon’s S3, due to the fact that it seems to be one of the most popular storage options for this type of user.

It’s worth noting, by the way, that this was not Life Technology’s first interaction with Penguin Computing. The company had been providing hardware services to support Life Technologies’ proprietary software since 2007.

According to Penguin, this is a side effect of having a solid reputation with customers who are software-driven—if their in-house systems perform well and they like the service and support, it’s a natural fit for users to consider using their remote resources if they fit the bill.

Coull noted that some users are getting creative about using the POD service. For instance, during his occasional glances at the real-time reports from the POD interface, there were Life Technologies training sessions going on in real time, which gave users the chance to work in a hands-on fashion with the software.

VP of Life Technologies, Jeff Cafferty also weighed on this, noting that beyond sheer training, potential customers interested in evaluating analytics options (since there are many—and many are non-proprietary) could hop on the POD-driven solidbioscope.com resource and compare results, including mappability and other specific factors.

In addition to extolling the benefits of the cloud beyond just analytics, Cafferty told us, “We are in the post-human genome sequencing project phase of life sciences” what’s happened in this last decade is that companies like ours have been developing evermore high throughput technologies for sequencing DNA and furthermore the cost of sequencing has gone down tremendously. What this means is that there’s been a huge explosion in the amount of sequence information available for life sciences researchers.

This is a fact that is driving the next big buzzphrase after cloud computing—“big data”—into every marketing message, particularly on the storage end, for obvious reasons. While the massive data end of the equation is a major factor that is causing genomics researchers to consider looking beyond physical hardware, the computational requirements are nothing to sneeze at either.

Caffrey put this in context, noting that to sequence a human genome researchers are dealing with something that is 3 billion base pairs long. Their instrumentation for next generation sequencing creates what are called “short reads” of DNA and in one genome, this creates billions such reads that then need to be mapped back to a reference genome.

He also elaborated on a topic that is growing nearer and dearer to storage, compute, software and cloud vendors alike: “Life sciences researchers have traditionally functioned on an experimental model that involved a great deal of time generating data (biological samples can be rare or hard to extra information from) and relatively small amounts of time analyzing it, in part because there just wasn’t very much of it. In sequencing in particular this paradigm has been flipped—we’re now generating a tremendous amount of data in a very short period of time and thus the length now is because of the mining, management, comparing and analysis of all that data.”

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industry updates delivered to you every week!

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing power it brings to artificial intelligence.  Nvidia's DGX Read more…

Call for Participation in Workshop on Potential NSF CISE Quantum Initiative

March 26, 2024

Editor’s Note: Next month there will be a workshop to discuss what a quantum initiative led by NSF’s Computer, Information Science and Engineering (CISE) directorate could entail. The details are posted below in a Ca Read more…

Waseda U. Researchers Reports New Quantum Algorithm for Speeding Optimization

March 25, 2024

Optimization problems cover a wide range of applications and are often cited as good candidates for quantum computing. However, the execution time for constrained combinatorial optimization applications on quantum device Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at the network layer threatens to make bigger and brawnier pro Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HBM3E memory as well as the the ability to train 1 trillion pa Read more…

Nvidia Appoints Andy Grant as EMEA Director of Supercomputing, Higher Education, and AI

March 22, 2024

Nvidia recently appointed Andy Grant as Director, Supercomputing, Higher Education, and AI for Europe, the Middle East, and Africa (EMEA). With over 25 years of high-performance computing (HPC) experience, Grant brings a Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing po Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HB Read more…

Nvidia Looks to Accelerate GenAI Adoption with NIM

March 19, 2024

Today at the GPU Technology Conference, Nvidia launched a new offering aimed at helping customers quickly deploy their generative AI applications in a secure, s Read more…

The Generative AI Future Is Now, Nvidia’s Huang Says

March 19, 2024

We are in the early days of a transformative shift in how business gets done thanks to the advent of generative AI, according to Nvidia CEO and cofounder Jensen Read more…

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, codenamed Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from Read more…

Nvidia Showcases Quantum Cloud, Expanding Quantum Portfolio at GTC24

March 18, 2024

Nvidia’s barrage of quantum news at GTC24 this week includes new products, signature collaborations, and a new Nvidia Quantum Cloud for quantum developers. Wh Read more…

Houston We Have a Solution: Addressing the HPC and Tech Talent Gap

March 15, 2024

Generations of Houstonian teachers, counselors, and parents have either worked in the aerospace industry or know people who do - the prospect of entering the fi Read more…

Alibaba Shuts Down its Quantum Computing Effort

November 30, 2023

In case you missed it, China’s e-commerce giant Alibaba has shut down its quantum computing research effort. It’s not entirely clear what drove the change. Read more…

Nvidia H100: Are 550,000 GPUs Enough for This Year?

August 17, 2023

The GPU Squeeze continues to place a premium on Nvidia H100 GPUs. In a recent Financial Times article, Nvidia reports that it expects to ship 550,000 of its lat Read more…

Shutterstock 1285747942

AMD’s Horsepower-packed MI300X GPU Beats Nvidia’s Upcoming H200

December 7, 2023

AMD and Nvidia are locked in an AI performance battle – much like the gaming GPU performance clash the companies have waged for decades. AMD has claimed it Read more…

DoD Takes a Long View of Quantum Computing

December 19, 2023

Given the large sums tied to expensive weapon systems – think $100-million-plus per F-35 fighter – it’s easy to forget the U.S. Department of Defense is a Read more…

Synopsys Eats Ansys: Does HPC Get Indigestion?

February 8, 2024

Recently, it was announced that Synopsys is buying HPC tool developer Ansys. Started in Pittsburgh, Pa., in 1970 as Swanson Analysis Systems, Inc. (SASI) by John Swanson (and eventually renamed), Ansys serves the CAE (Computer Aided Engineering)/multiphysics engineering simulation market. Read more…

Choosing the Right GPU for LLM Inference and Training

December 11, 2023

Accelerating the training and inference processes of deep learning models is crucial for unleashing their true potential and NVIDIA GPUs have emerged as a game- Read more…

Intel’s Server and PC Chip Development Will Blur After 2025

January 15, 2024

Intel's dealing with much more than chip rivals breathing down its neck; it is simultaneously integrating a bevy of new technologies such as chiplets, artificia Read more…

Baidu Exits Quantum, Closely Following Alibaba’s Earlier Move

January 5, 2024

Reuters reported this week that Baidu, China’s giant e-commerce and services provider, is exiting the quantum computing development arena. Reuters reported � Read more…

Leading Solution Providers

Contributors

Comparing NVIDIA A100 and NVIDIA L40S: Which GPU is Ideal for AI and Graphics-Intensive Workloads?

October 30, 2023

With long lead times for the NVIDIA H100 and A100 GPUs, many organizations are looking at the new NVIDIA L40S GPU, which it’s a new GPU optimized for AI and g Read more…

Shutterstock 1179408610

Google Addresses the Mysteries of Its Hypercomputer 

December 28, 2023

When Google launched its Hypercomputer earlier this month (December 2023), the first reaction was, "Say what?" It turns out that the Hypercomputer is Google's t Read more…

AMD MI3000A

How AMD May Get Across the CUDA Moat

October 5, 2023

When discussing GenAI, the term "GPU" almost always enters the conversation and the topic often moves toward performance and access. Interestingly, the word "GPU" is assumed to mean "Nvidia" products. (As an aside, the popular Nvidia hardware used in GenAI are not technically... Read more…

Shutterstock 1606064203

Meta’s Zuckerberg Puts Its AI Future in the Hands of 600,000 GPUs

January 25, 2024

In under two minutes, Meta's CEO, Mark Zuckerberg, laid out the company's AI plans, which included a plan to build an artificial intelligence system with the eq Read more…

Google Introduces ‘Hypercomputer’ to Its AI Infrastructure

December 11, 2023

Google ran out of monikers to describe its new AI system released on December 7. Supercomputer perhaps wasn't an apt description, so it settled on Hypercomputer Read more…

China Is All In on a RISC-V Future

January 8, 2024

The state of RISC-V in China was discussed in a recent report released by the Jamestown Foundation, a Washington, D.C.-based think tank. The report, entitled "E Read more…

Intel Won’t Have a Xeon Max Chip with New Emerald Rapids CPU

December 14, 2023

As expected, Intel officially announced its 5th generation Xeon server chips codenamed Emerald Rapids at an event in New York City, where the focus was really o Read more…

IBM Quantum Summit: Two New QPUs, Upgraded Qiskit, 10-year Roadmap and More

December 4, 2023

IBM kicks off its annual Quantum Summit today and will announce a broad range of advances including its much-anticipated 1121-qubit Condor QPU, a smaller 133-qu Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire