Life Sciences Storage Issues and Computational Workflow Acceleration

By Nicole Hemsoth

October 8, 2012

Introduction

Life Sciences can mean different things to different people. In genomic research, it referrers to the art of sequencing; in BioPharma, it covers molecular dynamics and protein docking; and in clinical, electronic records. However, all three markets have one thing in common, the sequencing of the human genome and the control, analysis, and distribution of that data. Today with the continued decrease in sequencing costs, life sciences research is moving from beakers to bytes and increasingly relies on the analysis of large volumes of data. 

Data-dominated efforts today aim to accelerate drug R&D, improve clinical trials, and personalize medicine. Most of the work in these areas requires the use of high performance computing clusters or supercomputers to derive decision-making information from terabytes to petabytes of data.

Across these widely disparate areas of work, researchers face similar computational infrastructure problems that can impede progress. To avoid obstacles and accelerate their research, life scientists need a low-latency, high-performance computing infrastructure that delivers predictable and consistent performance. They also need to collaborate and share large datasets with upstream and downstream partners. And they need an infrastructure that supports automation to simplify data aggregation, assimilation, and management.

The need for speed

Life sciences research and development increasingly relies on computational analysis. Such analysis provides the critical information needed to make intelligent decisions about which new drug candidates hold promise and should be advanced and which should be put aside.

With the growing reliance on computational analysis, and the changes in data generation and usage, life sciences organizations need an IT infrastructure that ensures computational workflows are optimized and not impeded.

Pressure to run the workflows as fast as possible so research decisions can be made sooner comes from several business drivers.

Many pharmaceutical companies today have sparse new drug pipelines. Delays caused by slowdowns in research due to slow data analysis simply keep the pipelines empty.

Because less than one percent of all drug candidates make it to market and the cost of moving a drug along the development pipeline mounts hugely the further along it gets, knowing which drugs to fail out of the process early is key to financial success. Faster early-stage analysis provides the data needed to make an early decision providing a significant savings in time and investment.

Compounding the need to quickly identify promising candidates and fill the pipelines is the fact that many blockbuster drugs have gone or are going off-patent and must be replaced. In fact, patent expirations from 2010 to 2013 will jeopardize revenues amounting to more than $95 billion for ten of the largest drug companies, according to Nature.

Competition to fill the pipelines is heating up. The drastic reduction in new lab equipment operating costs is allowing even the smallest life sciences organizations to compete in early stage R&D.

These factors are forcing companies to change the way they approach new drug research.

First, there is a greater focus on computational analysis during early stage research and development. The idea is to use information-based models, simulations, virtual molecule screening, and other techniques identify promising new drug candidates quickly and kill off less promising candidates to avoid incurring the costs of later stage clinical trials, development, and approval.

Second, many organizations are seeking to reduce their R&D costs. To accomplish this while still trying to fill their pipelines, they are expanding collaborations with universities, non-profit organizations, and the government. Specifically, beyond opening offices in university-rich places like Cambridge, MA, many pharmaceutical and biotech companies are joining collaborative groups such as the Structural Genomics Consortium, a public-private partnership that supports the discovery of new medicines through open access research. There are also government-led early-stage R&D efforts, such as those underway at the National Center for Advancing Translational Sciences, a group with the goal of developing new methods and technologies to improve diagnostics capabilities and therapeutic efforts across a wide range of human diseases.

Third, the desire to cut costs is creating an emerging market for Sequencing-as-a-Service (SEQaaS). Rather than invest in the sequencing equipment, chemicals, and experienced staff needed to perform the operations, many companies are outsourcing their sequencing to providers such as Illumina, PerkinElmer, and others. This allows them to concentrate on other aspects of drug discovery and development pipeline.  

Storage complications and challenges that can impede analysis workflows

These business drivers, combined with the adoption of new lab technologies such as next-generation sequencing, confocal microscopy, and X-ray crystallography, are driving up the volumes of data that life sciences organizations must store and manage. These large volumes and the collaborative nature of life sciences research are placing new demands on storage solution performance and data manageability. 

For example, new lab equipment, particularly next-generation sequences are producing multiple terabytes of data per run that must be analyzed and compared to large genomic databases. And while the format of raw data from sequences has varied over time as sequencing vendors have incorporated different processing steps into their algorithms, organizations using the sequencing data must still perform post-sequencing computations and analysis on various size files to derive useful information. From an infrastructure perspective, the sequencing data needs to be staged on high-performance parallel storage arrays so analytic workflows can run at top speeds.

Another factor to consider is that much of the data generated in life sciences organizations now must be retained. When sequencing for clinical applications is approved by the FDA, the Health Insurance Portability and Accountability Act of 1996 (HIPAA) requires that this patient data be retained for 20+ years. 

In pharmaceutical companies, long-term access to experimental data is growing as companies seek indications for previously approved drugs. With pipelines sparse, this area of work is exploding. From a storage perspective, older data must be moved to lower cost storage after its initial analysis or use and then be easily found and migrated to higher performance storage when exploring its use for a new indication.

Complicating data management and computational workflows is that fact that life sciences research has become more multi-disciplinary and more collaborative. Within an organization, data from new lab equipment is incredibly rich and of interest to many groups. Researchers in the different disciplines use different analysis tools running on clients with different operating systems and they need to perform their analysis at different times in the data’s life cycle. This makes computational workflows highly unpredictable. This can result in a vastly different user experience from day-to-day. A run that takes two minutes one day might take 45 minutes the next.

An additional implication of the multi-disciplinary and more collaborative nature of life sciences research is that data increasingly must be shared. This can pose problems within a company and it certainly needs special attention when organizations team together and must share petabyte-size databases across widely dispersed geographical regions.

DDN as your technology partner

All of these factors mean storage plays an increasingly important role in life sciences success. Solutions must support highly variable workloads in an HPC environment and be capable of supporting the collaborative nature of the industry. They also must allow researchers using different clients and hosts to have shared access to the data needed for their analysis.

Additionally, solutions must provide life sciences organizations with the flexibility to store data for longer times on appropriate cost/performance devices, while offering data management tools to migrate and protect that data. And there must be a way to facilitate the sharing of very large datasets.

Traditional storage solutions can introduce major performance and management problems when scaled to meet today’s increased requirements for the life sciences. This is why the Cornell Center for Advanced Computing, the National Cancer Institute, TGen, Virginia Tech, the Wellcome Trust Sanger Institute, and many more life sciences organizations are partnering with DataDirect Networks (DDN).

DDN offers an array of storage solutions with different I/O and throughput capabilities to meet the cost/performance requirements of any life sciences workflow. The solutions are extremely scalable in capacity and density. Based on its Storage Fusion Architecture, the DDN SFA 12K line offers a number of firsts including up to 40 GB/s host throughput for reads AND writes, 3.6 PB per rack, and the ability to scale to more than 7.2 PB per system. Furthermore, DDN lets organizations control their cost and performance profile by mixing a variety of media in the same system – SSD, SAS, and SATA – to achieve the appropriate cost/performance mix for their applications.

By consolidating on DDN storage, organizations get fast, scalable storage that solves performance inconsistency issues and provides easy-to-manage long term data retention.

In addition, DDN offers several technologies that help with the common challenges in life sciences research.

For researchers that must share and exchange large datasets within their organization, with collaborative partners, or with sequencing providers, DDN offers Web Object Scaler (WOS), a scale-out cloud storage appliance solution. WOS is an object-based storage system that allows organizations to easily build and deploy their own storage clouds across geographically distributed sites. The storage can scale to unprecedented levels while still being managed as a single entity. WOS provides high-speed access to hyperscale-sized data in the cloud from anywhere in the world, enabling globally distributed users to collaborate as part of a powerful peer-to-peer workflow.

To simplify and automate data management issues so researchers from multiple disciplines can all access the same data, DDN has integrated WOS with the Integrated Rule-Oriented Data-management System (iRODS). The iRODS data grid is an open source, next-generation adaptive middleware architecture for data management that helps researchers organize, share, and find collections of data in file systems.

And to ensure researchers get a high-performance, consistent experience, DDN offers DirectMon, an advanced storage configuration and monitoring solution. DirectMon works across DDN’s line of DDN SFA Storage Arrays, as well as GRIDScaler and EXAScaler shared file system appliances. DirectMon removes the complexity out of managing storage, its ease-of-use features and notifications allow administrators to quickly resolve problems, freeing-up valuable time to concentrate on more important tasks.

For more information about DDN solutions for the life sciences, visit http://www.ddn.com/en/applications/biopharma

Additional information can be found by visiting
http://www.ddn.com/en/applications/life-sciences

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!

China Plans 2019 Exascale Machine To Grow Sea Power

August 23, 2017

The glory of having the world's fastest supercomputer, as measured by the Linpack benchmark, has been China's for four years running, first with the 33-petaflops Tianhe-2 and currently with the 93-petaflops TaihuLight. T Read more…

By Tiffany Trader

Microsoft, Intel Unveil FPGA-driven Project Brainwave

August 23, 2017

We know about the seeming light-speed processing power of FPGAs and the natural fit they pose for data-dense AI workloads. But we also know that FPGAs present usability and programmability problems that flummox IT shops. Read more…

By Doug Black

Study Identifies Best Practices for Public-Private HPC Engagement

August 22, 2017

What's the best way for HPC centers in the public sphere to engage with private industry partners to boost the competitiveness of the companies and the larger communities? That question is at the heart of a new study pub Read more…

By Tiffany Trader

HPE Extreme Performance Solutions

Leveraging Deep Learning for Fraud Detection

Advancements in computing technologies and the expanding use of e-commerce platforms have dramatically increased the risk of fraud for financial services companies and their customers. Read more…

Google Launches Site to Share its NYC-based Algorithm Research

August 22, 2017

Much of Google’s algorithm development occurs in groups scattered throughout New York City. Yesterday, Google launched a single website - NYC Algorithms and Optimization Team page - to provide a deeper view into all of Read more…

By John Russell

China Plans 2019 Exascale Machine To Grow Sea Power

August 23, 2017

The glory of having the world's fastest supercomputer, as measured by the Linpack benchmark, has been China's for four years running, first with the 33-petaflop Read more…

By Tiffany Trader

Microsoft, Intel Unveil FPGA-driven Project Brainwave

August 23, 2017

We know about the seeming light-speed processing power of FPGAs and the natural fit they pose for data-dense AI workloads. But we also know that FPGAs present u Read more…

By Doug Black

Study Identifies Best Practices for Public-Private HPC Engagement

August 22, 2017

What's the best way for HPC centers in the public sphere to engage with private industry partners to boost the competitiveness of the companies and the larger c Read more…

By Tiffany Trader

Tech Giants Outline Battle Plans for Future HPC Market

August 21, 2017

Four companies engaged in a cage fight for leadership in the emerging HPC market of the 2020s are, despite deep differences in some areas, in violent agreement Read more…

By Doug Black

Microsoft Bolsters Azure With Cloud HPC Deal

August 15, 2017

Microsoft has acquired cloud computing software vendor Cycle Computing in a move designed to bring orchestration tools along with high-end computing access capabilities to the cloud. Terms of the acquisition were not disclosed. Read more…

By George Leopold

HPE Ships Supercomputer to Space Station, Final Destination Mars

August 14, 2017

With a manned mission to Mars on the horizon, the demand for space-based supercomputing is at hand. Today HPE and NASA sent the first off-the-shelf HPC system i Read more…

By Tiffany Trader

AMD EPYC Video Takes Aim at Intel’s Broadwell

August 14, 2017

Let the benchmarking begin. Last week, AMD posted a YouTube video in which one of its EPYC-based systems outperformed a ‘comparable’ Intel Broadwell-based s Read more…

By John Russell

Deep Learning Thrives in Cancer Moonshot

August 8, 2017

The U.S. War on Cancer, certainly a worthy cause, is a collection of programs stretching back more than 40 years and abiding under many banners. The latest is t Read more…

By John Russell

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

Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a Read more…

By Tiffany Trader

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

Russian Researchers Claim First Quantum-Safe Blockchain

May 25, 2017

The Russian Quantum Center today announced it has overcome the threat of quantum cryptography by creating the first quantum-safe blockchain, securing cryptocurrencies like Bitcoin, along with classified government communications and other sensitive digital transfers. Read more…

By Doug Black

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

Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Just how close real-wo Read more…

By John Russell

Google Debuts TPU v2 and will Add to Google Cloud

May 25, 2017

Not long after stirring attention in the deep learning/AI community by revealing the details of its Tensor Processing Unit (TPU), Google last week announced the Read more…

By John Russell

Groq This: New AI Chips to Give GPUs a Run for Deep Learning Money

April 24, 2017

CPUs and GPUs, move over. Thanks to recent revelations surrounding Google’s new Tensor Processing Unit (TPU), the computing world appears to be on the cusp of Read more…

By Alex Woodie

Leading Solution Providers

HPC Compiler Company PathScale Seeks Life Raft

March 23, 2017

HPCwire has learned that HPC compiler company PathScale has fallen on difficult times and is asking the community for help or actively seeking a buyer for its a Read more…

By Tiffany Trader

Six Exascale PathForward Vendors Selected; DoE Providing $258M

June 15, 2017

The much-anticipated PathForward awards for hardware R&D in support of the Exascale Computing Project were announced today with six vendors selected – AMD Read more…

By John Russell

Trump Budget Targets NIH, DOE, and EPA; No Mention of NSF

March 16, 2017

President Trump’s proposed U.S. fiscal 2018 budget issued today sharply cuts science spending while bolstering military spending as he promised during the cam Read more…

By John Russell

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

In this contributed perspective piece, Intel’s Jim Jeffers makes the case that CPU-based visualization is now widely adopted and as such is no longer a contrarian view, but is rather an exascale requirement. Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

Top500 Results: Latest List Trends and What’s in Store

June 19, 2017

Greetings from Frankfurt and the 2017 International Supercomputing Conference where the latest Top500 list has just been revealed. Although there were no major Read more…

By Tiffany Trader

IBM Clears Path to 5nm with Silicon Nanosheets

June 5, 2017

Two years since announcing the industry’s first 7nm node test chip, IBM and its research alliance partners GlobalFoundries and Samsung have developed a proces Read more…

By Tiffany Trader

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

Singularity HPC Container Technology Moves Out of the Lab

May 4, 2017

Last week, Singularity – the fast-growing HPC container technology whose development has been spearheaded by Gregory Kurtzer at Lawrence Berkeley National Lab Read more…

By John Russell

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