Coding Bioinformatics into the Cloud

By Ian Armas Foster

July 24, 2013

A team comprised primarily of researchers from the Technical University of Munich published a paper this month on a system that will offer cloud functionality to a bioinformatics toolset that will cover high performance applications.

“On demand servers in the cloud promise to fit computing power to most tasks economically, and without a fair portion of the usual worries of system management: hardware purchasing, recruiting a system manager, high availability issues, and so forth,” the report noted in discussing some of the benefits of cloud on high tech problems like genomics. The problems for them start with porting a toolkit that was designed to run on non-virtualized systems.

“One problem remains: how to get the often adhoc analysis toolset from the desktop environment into the cloud?” They answer that question by offering up the PredictProtein bioinformatics toolkit over an operating system called Cloud BioLinux. “Directly addressing this problem, here we report the first Debian package release of the protein feature prediction toolset “PredictProtein,” developed at the Rost Lab.”

Third Wave Systems, a Minnesota-based manufacturing company, recently answered a similar question regarding specialized HPC cloud services, where they too developed a specific toolkit for cloud based high performance manufacturing applications 

The paper starts with an assertion that will not surprise those familiar with the HPC cloud space: cloud computing is gaining greater acceptance in the bioinformatics realm. The focus here has largely been on genomics, as the massive datasets associated with genomics are hypothetically easier to access and utilize when input into a cloud.

The attractiveness of cloud computing for bioinformatics runs deeper. According to the team, cost analytics models favor sustainability in the cloud. But more importantly, a common cited common advantage of cloud computing is cited here in the ability to offload peak workloads without needing large in-house clusters.

“The rate of data generation of “next generation” sequencing (NGS) drives the efforts to turn to cloud computing as a solution to handling peak-time loads, without the need to maintain large clusters,” they argue. And finally, according to the team, there exists high performance functionality. “Cloud-enabled bioinformatics tools are now available in the context of high throughput sequencing and genomics.”

Those tools, as mentioned earlier, come in the PredictProtein toolset, an open source package that has been around and improved upon for several years. Specifically, according to the report, “The PredictProtein cloud solution builds upon the open source operating system Debian and provides its functionality as a set of free software packages.”

They tested the system on two bioinformatics use cases, both of which focused on dealing with the “increase in the protein annotation gap.” Per the report, of a specific database called the UniProt Knowledgebase, only 500 thousand of 35 million sequences have been annotated.

For their research, they set up an inhouse compute system along with a cluster in an OpenNebula cloud provided by CSCFinland. Over the course of a couple of years, they managed to combine those resources in a manner where resource and workload management appeared to be done manually.  “Grid job submissions to the local and the cloud grid were manually adjusted according to available resources. Over 9 million disorder predictions were made over the course of the past few years,” the report stated.

The second was more cloud intensive, set up on the Amazon Elastic Compute Cloud (EC2), a provider familiar to scientific HPC instances. Here, they were able to allocate workloads through the Grid Engine. “Because every protein can be computed independently, we formed a grid job out of each protein and used the Grid Engine (GE) to distribute work on the machine instances,” the report noted on the workload management point.

Each individual PredictProtein that was to be analyzed took up 28 GB of space. Due to the large data requirements and the intensity of the computing that was to take place, the group opted for high performance instances. “We used StarCluster to automate grid setup on the EC2. Because a lot of CPU power was needed, the ‘Cluster Compute Eight Extra Large Instance’ was chosen,” the team said.

Each instance reportedly had 60.5 GB of memory and contained 88 EC2 Compute Units, each of which was equipped 2 eight-core Intel Xeon E5-2670 processors. In total, each instance provided 3370 GB of storage.

Over the course of their study, they were able to process precisely 29,036 sequences with over 16 million residues. According to the team, “this amounted to predicting the functional effect of 315,753,552 individual amino acid changes.”

Like the Third Wave Systems discussed earlier this month, this group looks to provide access to HPC capabilities through a cloud-available specific toolkit. For TWS, that toolkit dealt with drill manufacturing, while this one landed more in the scientific research field with bioinformatics and genomics. As such, it makes sense that the former is a commercially available SaaS, while this example tells of an open source package developed over years by computational biologists.

Either way, as cloud gains acceptance as a medium for high performance applications, more institutions are developing software to facilitate those applications in the cloud.

 

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!

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 “pre-exascale” award), parsed out additional information ab Read more…

By Tiffany Trader

Tsinghua Crowned Eight-Time Student Cluster Champions at ISC

June 22, 2017

Always a hard-fought competition, the Student Cluster Competition awards were announced Wednesday, June 21, at the ISC High Performance Conference 2017. Amid whoops and hollers from the crowd, Thomas Sterling presented t Read more…

By Kim McMahon

GPUs, Power9, Figure Prominently in IBM’s Bet on Weather Forecasting

June 22, 2017

IBM jumped into the weather forecasting business roughly a year and a half ago by purchasing The Weather Company. This week at ISC 2017, Big Blue rolled out plans to push deeper into climate science and develop more gran Read more…

By John Russell

Intersect 360 at ISC: HPC Industry at $44B by 2021

June 22, 2017

The care, feeding and sustained growth of the HPC industry increasingly is in the hands of the commercial market sector – in particular, it’s the hyperscale companies and their embrace of AI and deep learning – tha Read more…

By Doug Black

HPE Extreme Performance Solutions

Creating a Roadmap for HPC Innovation at ISC 2017

In an era where technological advancements are driving innovation to every sector, and powering major economic and scientific breakthroughs, high performance computing (HPC) is crucial to tackle the challenges of today and tomorrow. Read more…

At ISC – Goh on Go: Humans Can’t Scale, the Data-Centric Learning Machine Can

June 22, 2017

I've seen the future this week at ISC, it’s on display in prototype or Powerpoint form, and it’s going to dumbfound you. The future is an AI neural network designed to emulate and compete with the human brain. In thi Read more…

By Doug Black

Cray Brings AI and HPC Together on Flagship Supers

June 20, 2017

Cray took one more step toward the convergence of big data and high performance computing (HPC) today when it announced that it’s adding a full suite of big data and artificial intelligence software to its top-of-the-l Read more…

By Alex Woodie

AMD Charges Back into the Datacenter and HPC Workflows with EPYC Processor

June 20, 2017

AMD is charging back into the enterprise datacenter and select HPC workflows with its new EPYC 7000 processor line, code-named Naples, announced today at a “global” launch event in Austin TX. In many ways it was a fu Read more…

By John Russell

Hyperion: Deep Learning, AI Helping Drive Healthy HPC Industry Growth

June 20, 2017

To be at the ISC conference in Frankfurt this week is to experience deep immersion in deep learning. Users want to learn about it, vendors want to talk about it, analysts and journalists want to report on it. Deep learni Read more…

By Doug Black

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

Tsinghua Crowned Eight-Time Student Cluster Champions at ISC

June 22, 2017

Always a hard-fought competition, the Student Cluster Competition awards were announced Wednesday, June 21, at the ISC High Performance Conference 2017. Amid wh Read more…

By Kim McMahon

GPUs, Power9, Figure Prominently in IBM’s Bet on Weather Forecasting

June 22, 2017

IBM jumped into the weather forecasting business roughly a year and a half ago by purchasing The Weather Company. This week at ISC 2017, Big Blue rolled out pla Read more…

By John Russell

Intersect 360 at ISC: HPC Industry at $44B by 2021

June 22, 2017

The care, feeding and sustained growth of the HPC industry increasingly is in the hands of the commercial market sector – in particular, it’s the hyperscale Read more…

By Doug Black

At ISC – Goh on Go: Humans Can’t Scale, the Data-Centric Learning Machine Can

June 22, 2017

I've seen the future this week at ISC, it’s on display in prototype or Powerpoint form, and it’s going to dumbfound you. The future is an AI neural network Read more…

By Doug Black

Cray Brings AI and HPC Together on Flagship Supers

June 20, 2017

Cray took one more step toward the convergence of big data and high performance computing (HPC) today when it announced that it’s adding a full suite of big d Read more…

By Alex Woodie

AMD Charges Back into the Datacenter and HPC Workflows with EPYC Processor

June 20, 2017

AMD is charging back into the enterprise datacenter and select HPC workflows with its new EPYC 7000 processor line, code-named Naples, announced today at a “g Read more…

By John Russell

Hyperion: Deep Learning, AI Helping Drive Healthy HPC Industry Growth

June 20, 2017

To be at the ISC conference in Frankfurt this week is to experience deep immersion in deep learning. Users want to learn about it, vendors want to talk about it Read more…

By Doug Black

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

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

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

Google Pulls Back the Covers on Its First Machine Learning Chip

April 6, 2017

This week Google released a report detailing the design and performance characteristics of the Tensor Processing Unit (TPU), its custom ASIC for the inference Read more…

By Tiffany Trader

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

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

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

Facebook Open Sources Caffe2; Nvidia, Intel Rush to Optimize

April 18, 2017

From its F8 developer conference in San Jose, Calif., today, Facebook announced Caffe2, a new open-source, cross-platform framework for deep learning. Caffe2 is the successor to Caffe, the deep learning framework developed by Berkeley AI Research and community contributors. Read more…

By Tiffany Trader

Leading Solution Providers

MIT Mathematician Spins Up 220,000-Core Google Compute Cluster

April 21, 2017

On Thursday, Google announced that MIT math professor and computational number theorist Andrew V. Sutherland had set a record for the largest Google Compute Engine (GCE) job. Sutherland ran the massive mathematics workload on 220,000 GCE cores using preemptible virtual machine instances. Read more…

By Tiffany Trader

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

US Supercomputing Leaders Tackle the China Question

March 15, 2017

Joint DOE-NSA report responds to the increased global pressures impacting the competitiveness of U.S. supercomputing. Read more…

By Tiffany Trader

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

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

DOE Supercomputer Achieves Record 45-Qubit Quantum Simulation

April 13, 2017

In order to simulate larger and larger quantum systems and usher in an age of “quantum supremacy,” researchers are stretching the limits of today’s most advanced supercomputers. Read more…

By Tiffany Trader

Messina Update: The US Path to Exascale in 16 Slides

April 26, 2017

Paul Messina, director of the U.S. Exascale Computing Project, provided a wide-ranging review of ECP’s evolving plans last week at the HPC User Forum. Read more…

By John Russell

Knights Landing Processor with Omni-Path Makes Cloud Debut

April 18, 2017

HPC cloud specialist Rescale is partnering with Intel and HPC resource provider R Systems to offer first-ever cloud access to Xeon Phi "Knights Landing" processors. The infrastructure is based on the 68-core Intel Knights Landing processor with integrated Omni-Path fabric (the 7250F Xeon Phi). Read more…

By Tiffany Trader

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