Cray Advances Hadoop for HPC

By Tiffany Trader

February 4, 2014

In a recent blog entry, Mike Boros, Hadoop Product Marketing Manager at Cray, Inc., writes about the company’s positioning of Hadoop for scientific big data. Like the old adage, “when the only tool you have is a hammer, every problem begins to resemble a nail,” Boros suggests that the Law of the Instrument may be true for technical computing professionals assessing the most well-known of the big data tools: Hadoop.

Boros writes: “When used inappropriately, and incorporating technologies not suited for scientific Big Data, using Hadoop may indeed feel like wielding a cumbersome hammer. But when used appropriately, and with a technology stack that’s specifically suited to the realities of scientific Big Data, Hadoop can feel like a Swiss Army knife — a multipurpose tool capable of doing a wide range of things.”

“Of course, whether Hadoop feels like a Swiss Army knife or not depends not only on the experience level of the user, but also on whether it’s designed and implemented for scientific Big Data. And scientific Big Data is different from the Big Data much of the rest of the world is dealing with,” he adds.

It all boils down to suitability for the job at hand. Hadoop was developed to handle bite-sized pieces of data that are aggregated into larger files and then analyzed in their entirety. It’s an ideal approach for assessing user sentiment in social media feeds and it can also be applied to big data science applications that incorporate a large number of sensor data. But when it comes to analyzing a seismic or weather model file as part of a big data application, Hadoop’s usefulness starts to break down.

“Hadoop can indeed feel like a blunt and inappropriate hammer when this inefficient process of analyzing unnecessary blocks of data is repeated dozens or hundreds of times a day,” writes Boros. “This is a case where random access to files is necessitated, and frankly, that’s not in HDFS’s (the Hadoop Distributed File System) wheelhouse.”

But because of its Swiss Army knife-like design, Hadoop lends itself to some interesting workarounds and modifications. By leveraging MapReduce and wrapping HDFS with a POSIX compliant file system such as Lustre, users can simply skip the extraneous or uninteresting data blocks in order to devote more resources towards analyzing large hierarchical files. To those who would point out that this isn’t really Hadoop but MapReduce on a POSIX-compliant file system, Boros explains that the approach is done in way that doesn’t affect Hadoop’s other operations, meaning the MapReduce ecosystem is still in tact. “In other words,” writes Boros, “the storage has to be presented to MapReduce and its constituents as if it was HDFS, even though another file system lies underneath it. And yes, that’s one of the ways we are looking at for designing our Hadoop solutions here at Cray.”

Boros understands that standard Hadoop implementations aren’t the best fit for traditional HPC applications, but he believes that the tool has the inherent flexibility to bridge the big data / big compute divide. While scientific environments work well with standard file systems built around Posix file access, Boros suggests that mounting the POSIX compliant volume with MapReduce would be an ideal situation. There are still some kinks to iron out, but Cray is working on these, and according to Boros, “you won’t be obligated to take on a 200 percent availability overhead tax as the file system you’re using will likely require 15-20 percent RAID parity which most organizations find appropriate for even their most mission-critical data.”

The message here is that one environment can excel at both analytical and compute-intensive workloads. For Cray, the flexibility of the Hadoop stack and supporting infrastructure plays a major role in this vision. The company is taking steps to modify Hadoop to be more efficient and perhaps even more cost-effective than using an ad-hoc distributed infrastructure.

As for why organizations would want a system to do double-duty in this way, Boros emphasizes the benefits of a flexible infrastructure and workflow. Being able to use the same infrastructure for multiple job types will allow users to focus on different parts of a project at different points in time. They also have the option to work on grand challenge problems that don’t easily fall into standard application buckets. And depending on how the approach is implemented, they will be able to manage disparate workloads and workflows in parallel by employing resource management and job scheduling techniques. Such a machine will lend itself to sharing across departments, helping to achieve an equitable division of budgetary and staffing resources.

Boros expects to get some pushback for the Swiss Army knife analogy, as HPC has historically opted for the best possible tool for a given job even if that meant developing it in-house. But that paradigm is changing. Commoditization is entrenched in HPC – and the maker of the iconic Cray-1 is leading the charge on this aspect of the convergence.

Here’s Boros, describing the evolution of Hadoop: “It was initially conceived in the service provider world where huge staffs maintain thousands upon thousands of cheap white boxes by spending their days applying the DevOps equivalent of duct tape and bailing wire with Perl scripts and Ruby,” he writes. “It’s somewhat green, and still requires a great deal of fiddling to get right. And it’s out of the box design seems to be a full 180 degrees off of anything HPC stands for. But I believe it’s going to have a prominent role in your datacenter in the not too distant future.”

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!

Data-Hungry Algorithms and the Thirst for AI

March 29, 2017

At Tabor Communications’ Leverage Big Data + EnterpriseHPC Summit in Florida last week, esteemed HPC professional Jay Boisseau, chief HPC technology strategist at Dell EMC, engaged the audience with his presentation, “Big Computing, Big Data, Big Trends, Big Results.” Read more…

By Tiffany Trader

Bill Gropp – Pursuing the Next Big Thing at NCSA

March 28, 2017

About eight months ago Bill Gropp was elevated to acting director of the National Center for Supercomputing Applications (NCSA). Read more…

By John Russell

UK to Launch Six Major HPC Centers

March 27, 2017

Six high performance computing centers will be formally launched in the U.K. later this week intended to provide wider access to HPC resources to U.K. Read more…

By John Russell

AI in the News: Rao in at Intel, Ng out at Baidu, Nvidia on at Tencent Cloud

March 26, 2017

Just as AI has become the leitmotif of the advanced scale computing market, infusing much of the conversation about HPC in commercial and industrial spheres, it also is impacting high-level management changes in the industry. Read more…

By Doug Black

HPE Extreme Performance Solutions

Leveraging the Power of Big Data to Improve Customer Satisfaction & Brand Loyalty

In the dynamic world of retail, retailers must find ways to recognize and effectively respond to shopping behaviors, patterns, and trends in order to succeed. Read more…

Scalable Informatics Ceases Operations

March 23, 2017

On the same day we reported on the uncertain future for HPC compiler company PathScale, we are sad to learn that another HPC vendor, Scalable Informatics, is closing its doors. Read more…

By Tiffany Trader

‘Strategies in Biomedical Data Science’ Advances IT-Research Synergies

March 23, 2017

“Strategies in Biomedical Data Science: Driving Force for Innovation” by Jay A. Etchings is both an introductory text and a field guide for anyone working with biomedical data. Read more…

By Tiffany Trader

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 assets. Read more…

By Tiffany Trader

Google Launches New Machine Learning Journal

March 22, 2017

On Monday, Google announced plans to launch a new peer review journal and “ecosystem” Read more…

By John Russell

Data-Hungry Algorithms and the Thirst for AI

March 29, 2017

At Tabor Communications’ Leverage Big Data + EnterpriseHPC Summit in Florida last week, esteemed HPC professional Jay Boisseau, chief HPC technology strategist at Dell EMC, engaged the audience with his presentation, “Big Computing, Big Data, Big Trends, Big Results.” Read more…

By Tiffany Trader

Bill Gropp – Pursuing the Next Big Thing at NCSA

March 28, 2017

About eight months ago Bill Gropp was elevated to acting director of the National Center for Supercomputing Applications (NCSA). 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 assets. 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. 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 campaign. 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

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

New Japanese Supercomputing Project Targets Exascale

March 14, 2017

Another Japanese supercomputing project was revealed this week, this one from emerging supercomputer maker, ExaScaler Inc., and Keio University. The partners are working on an original supercomputer design with exascale aspirations. 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

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. 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 campaign. 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 assets. Read more…

By Tiffany Trader

TSUBAME3.0 Points to Future HPE Pascal-NVLink-OPA Server

February 17, 2017

Since our initial coverage of the TSUBAME3.0 supercomputer yesterday, more details have come to light on this innovative project. Of particular interest is a new board design for NVLink-equipped Pascal P100 GPUs that will create another entrant to the space currently occupied by Nvidia's DGX-1 system, IBM's "Minsky" platform and the Supermicro SuperServer (1028GQ-TXR). Read more…

By Tiffany Trader

Tokyo Tech’s TSUBAME3.0 Will Be First HPE-SGI Super

February 16, 2017

In a press event Friday afternoon local time in Japan, Tokyo Institute of Technology (Tokyo Tech) announced its plans for the TSUBAME3.0 supercomputer, which will be Japan’s “fastest AI supercomputer,” Read more…

By Tiffany Trader

IBM Wants to be “Red Hat” of Deep Learning

January 26, 2017

IBM today announced the addition of TensorFlow and Chainer deep learning frameworks to its PowerAI suite of deep learning tools, which already includes popular offerings such as Caffe, Theano, and Torch. Read more…

By John Russell

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

Leading Solution Providers

Is Liquid Cooling Ready to Go Mainstream?

February 13, 2017

Lost in the frenzy of SC16 was a substantial rise in the number of vendors showing server oriented liquid cooling technologies. Three decades ago liquid cooling was pretty much the exclusive realm of the Cray-2 and IBM mainframe class products. That’s changing. We are now seeing an emergence of x86 class server products with exotic plumbing technology ranging from Direct-to-Chip to servers and storage completely immersed in a dielectric fluid. Read more…

By Steve Campbell

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

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

HPC Startup Advances Auto-Parallelization’s Promise

January 23, 2017

The shift from single core to multicore hardware has made finding parallelism in codes more important than ever, but that hasn’t made the task of parallel programming any easier. Read more…

By Tiffany Trader

HPC Technique Propels Deep Learning at Scale

February 21, 2017

Researchers from Baidu’s Silicon Valley AI Lab (SVAIL) have adapted a well-known HPC communication technique to boost the speed and scale of their neural network training and now they are sharing their implementation with the larger deep learning community. Read more…

By Tiffany Trader

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

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

IDG to Be Bought by Chinese Investors; IDC to Spin Out HPC Group

January 19, 2017

US-based publishing and investment firm International Data Group, Inc. (IDG) will be acquired by a pair of Chinese investors, China Oceanwide Holdings Group Co., Ltd. Read more…

By Tiffany Trader

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