StackIQ Widens Net

By Steve Campbell

August 31, 2011

Continuing adventures in San Diego led me to StackIQ to meet with President and Co-founder Tim McIntire. Many of you will know StackIQ by their original name Clustercorp and the highly successful HPC cluster management suite Rocks.

Rocks was first developed at UCSD, circa 2000, and over a ten-year period, there are over ten thousand Rocks clusters worldwide. Many in traditional HPC environments but also growing installed base in the enterprises such as Aerospace, Automotive, Oil and Gas.  The one thing in common is managing large cluster installations in environments generating and processing vast amounts of data.

It is clearly an exciting time for StackIQ; a new name, new funding in place, new location and two of the original Rocks co-founders Greg Bruno and Mason Katz recently joining the team full time. More importantly, however, is the growing list of new customers for Rocks in their traditional HPC community and growing commercial enterprise installed base.

StackIQ is seeing growing acceptance in the new market segments of big data and naturally cloud computing. Recent alliances with Amazon for a port of Rocks to EC2 together with a recently announced OEM relationship with Amazon. In addition, their HPC expertise and heritage is well suited to Big Data solutions such as Apache Hadoop, Cassandra and others.  Rocks enables Hadoop clusters to be deployed in fraction of the time compared to conventional methods, StackIQ put a lot of emphasis on the ease of use and ease of deployment.

HPCc: You recently changed the company name from Clustercorp to StackIQ.  What’s behind the name change?

McIntire: Many people associated our previous name exclusively with High Performance Computing  (HPC). We wanted to change our name to better reflect what we do, which is to apply a decade of cluster computing expertize and experience to full-stack enterprise automation.
 
HPCc: I heard you also closed a round of financing.  Was this a private round or VC round and what can you tell me about it?

McIntire: It was a VC round, Series A. We secured the funding we needed to take the company to the next level by bringing in the resources to address the cloud and big data markets. We are very excited to be working with both Avalon Ventures and Anthem Venture Partners.
 
HPCc: How do you plan to spend the dollars raised?

McIntire: Some of it is focused on accelerating product development in the cloud and big data market segments while maintaining our leadership position in HPC. Some is earmarked to help us get our message out. We’ve been quite fortunate in building a strong community in the HPC space, but we need to let people outside of HPC know how much easier things are when you use Rocks+ to deploy and manage their servers.
 
HPCc: When I look at your website it is very clear that you have three distinct yet related segments, Clouds, Clusters and Big Data?  Starting with Clouds what is the plan?

McIntire: I’m glad you asked. Rocks is a horizontal platform for building out scalable solutions, but one of the things that made Rocks so successful in the cluster space was its ability to nail down a vertical solution (HPC) into a single, integrated stack. Open Source Rocks provided the first turnkey downloadable cluster ISO that allowed anybody — even researchers with little to no system administration experience — to build and manage a supercomputer.  

We are approaching the Cloud and Big Data verticals with the same strategy. You’ll notice that there is a complete downloadable stack for each of the segments on our website, so aspiring cloud and big data administrators can leverage the same simple turnkey solution we offer for HPC community. In addition, it’s free to use for clusters of up to 16 nodes.
 
HPCc: What about HPC clusters?

McIntire: Our roots are in HPC, and we’re proud of our position as the market leader in the HPC cluster management space. We are actively developing our HPC product line, and remain committed to delivering the best-of-breed cluster management solution for open source and commercial users.
 
There is a growing discussion of Big Data. What is your vision and why is this important to StackIQ and why is it such a hot topic in the world of data management?

The need to extract value from ever-growing volumes of both structured, and unstructured data is very real, and represents a tremendous business opportunity.

Apache Hadoop is a powerful, accessible open source tool that many are turning to, and at the heart of every Hadoop solution lies a high-performance cluster. Our HPC heritage puts us in the perfect position to bring best-of-breed technology to the foundation of Big Data solutions such as Apache Hadoop, Cassandra, and others. By laying the foundation of a rock-solid, predictable, reliable cluster at the base of each big data installation, Rocks+ leaves our customers free to focus on the business problem they’re trying to solve. Using conventional methods, building a working Hadoop solution can take weeks or months. Using our products, customers can bring up their Hadoop cluster in minutes.

HPCc: What’s the intersection of Big Data with Cloud Computing?

McIntire: The most interesting intersection I’ve seen is Amazon’s Elastic Map Reduce (EMR). Here, you have a market leading public cloud provider with customers who are generating a tremendous amount of data in the cloud. Rather than making customers deal with the difficult problem of downloading their data to a Hadoop cluster for analysis, Amazon brought compute to the data. They now have a very strong position in the big data space.

HPCc: Speaking of Amazon, are you also working to solve management problems in public cloud?

McIntire: Yes, one of our first cloud projects was a deep port of Rocks to support Amazon EC2. In June, we announced that Amazon has OEM’d Rocks+ (in much the same way they’ve done with Red Hat Enterprise Linux and Microsoft Windows) and now offers Rocks+ as an instance type. Thanks to Amazon, administrators can use the exact same management tools in public clouds that they use in their own data center. This can be a tremendous advantage. For example, you could leverage EC2 for development and testing purposes, running our Hadoop Roll there, tweak it to suit your needs, or you might even develop your own Roll. Then, should you decide to move your project in-house for large-scale deployment, you could simply download your Roll as an ISO and do complete bare metal provisioning of an in-house private cloud. You’d even have the same automated check-box installation Rocks users have become accustomed to.  

HPCc: You have been very successful with Rocks in the traditional HPC market. Are you changing direction and going after the commercial enterprise and if so why?

McIntire: The customers for our Cloud and Big Data solutions are primarily enterprises, but we’ve already seen great penetration in the enterprise HPC space. Therefore, while we are indeed marketing to the enterprise, we don’t see it as a change of direction. It’s just the next step along the path we were already on — bringing the power of Rocks to everyone who needs it.

HPCc: Can you do both markets? Do you have resources people and funds for both?

McIntire: Yes, one of the neat things about Rocks is its modularity.  Much of the work we do is applicable to all three verticals, which gives us a multiplying effect on the development side. By combining our architecture, leveraging the funding we talked about earlier, and some incredible work done by our partner network, we have everything we need to get the job done.
 
HPCc: Who is your primary competition and why should potential prospect choose you?

McIntire: There is no other product that can spin up Clouds, HPC Clusters, and Hadoop Clusters with the ease and efficiency of Rocks+. However, we do run in to different companies in certain spaces. Fortunately for us, most of them are in the early days of discovering all the ins and outs of deploying and managing large groups of connected servers at scale.

What we’ve seen is that people can be fairly successful in building small-scale clusters using less powerful tools, but once they grow beyond the test and proof-of-concept stage, the processes they’d been using fall short. We recommend people start with the end in mind, and use a proven cloud and cluster power tool right from the start. We make that easy by offering it free for clusters up to 16 nodes.
 
HPCc: What is you product roadmap, what’s next?

McIntire: I don’t like talking too far into the future when it comes to product features, however, there are some key items that are under development now.

We have a project underway that will significantly improve the Rocks user interface. We are also broadening the Big Data product line to include Cassandra, and plan to add other Big Data services to that package in the future as well.

In the Cloud space, work is well underway on an OpenStack product, and we are adding new capabilities to our Amazon EC2 offering. We’re also pushing ahead on the open source Rocks project, and plan to contribute RHEL and CentOS 6 support soon.
 
What’s your go to market approach?

We have a three-pronged Go-to-market approach:

-Hardware partners resell our software stacks to their customers to go along with their servers. Our current partners include Dell and HP, who have both given us tremendous support considering our company size.  Amazon, while not a traditional hardware partner, also fits into this channel go-to-market strategy.

-We also have a strong ISV partner network — we realize that our customers have a wide breadth of needs — our strategy here has always been to be supportive, yet agnostic at the applications layer.  For instance in HPC, we have Rocks Rolls for

Adaptive Computing’s Moab, Univa Grid Engine, and Altair PBS Pro.  You’ll notice we are executing the same strategy in the Big Data space, as we’ve already rolled out support for Apache Hadoop, MapR, and Cloudera.  In the coming months, you’ll see this list continue to grow.

-Lastly, StackIQ works directly with a number of customers who have pre-existing hardware and/or heterogeneous data centers.  Direct customers are usually more on the hyper-scale end of the size spectrum.  

What are the big trends you are seeing and how do they impact your vision?

We see a continuation of the trend towards more data and more compute — clusters are now mission critical to enterprise.  StackIQ is in the unique position of participating in a leading edge market segment with a mature, robust software solution.

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!

TACC Helps ROSIE Bioscience Gateway Expand its Impact

April 26, 2017

Biomolecule structure prediction has long been challenging not least because the relevant software and workflows often require high-end HPC systems that many bioscience researchers lack easy access to. Read more…

By John Russell

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

IBM, Nvidia, Stone Ridge Claim Gas & Oil Simulation Record

April 25, 2017

IBM, Nvidia, and Stone Ridge Technology today reported setting the performance record for a “billion cell” oil and gas reservoir simulation. Read more…

By John Russell

ASC17 Makes Splash at Wuxi Supercomputing Center

April 24, 2017

A record-breaking twenty student teams plus scores of company representatives, media professionals, staff and student volunteers transformed a formerly empty hall inside the Wuxi Supercomputing Center into a bustling hub of HPC activity, kicking off day one of 2017 Asia Student Supercomputer Challenge (ASC17). Read more…

By Tiffany Trader

HPE Extreme Performance Solutions

Remote Visualization Optimizing Life Sciences Operations and Care Delivery

As patients continually demand a better quality of care and increasingly complex workloads challenge healthcare organizations to innovate, investing in the right technologies is key to ensuring growth and success. Read more…

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 a new generation of chips designed specifically for deep learning workloads. Read more…

By Alex Woodie

Musk’s Latest Startup Eyes Brain-Computer Links

April 21, 2017

Elon Musk, the auto and space entrepreneur and severe critic of artificial intelligence, is forming a new venture that reportedly will seek to develop an interface between the human brain and computers. Read more…

By George Leopold

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

NERSC Cori Shows the World How Many-Cores for the Masses Works

April 21, 2017

As its mission, the high performance computing center for the U.S. Department of Energy Office of Science, NERSC (the National Energy Research Supercomputer Center), supports a broad spectrum of forefront scientific research across diverse areas that includes climate, material science, chemistry, fusion energy, high-energy physics and many others. Read more…

By Rob Farber

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

ASC17 Makes Splash at Wuxi Supercomputing Center

April 24, 2017

A record-breaking twenty student teams plus scores of company representatives, media professionals, staff and student volunteers transformed a formerly empty hall inside the Wuxi Supercomputing Center into a bustling hub of HPC activity, kicking off day one of 2017 Asia Student Supercomputer Challenge (ASC17). Read more…

By Tiffany Trader

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 a new generation of chips designed specifically for deep learning workloads. Read more…

By Alex Woodie

NERSC Cori Shows the World How Many-Cores for the Masses Works

April 21, 2017

As its mission, the high performance computing center for the U.S. Department of Energy Office of Science, NERSC (the National Energy Research Supercomputer Center), supports a broad spectrum of forefront scientific research across diverse areas that includes climate, material science, chemistry, fusion energy, high-energy physics and many others. Read more…

By Rob Farber

Hyperion (IDC) Paints a Bullish Picture of HPC Future

April 20, 2017

Hyperion Research – formerly IDC’s HPC group – yesterday painted a fascinating and complicated portrait of the HPC community’s health and prospects at the HPC User Forum held in Albuquerque, NM. HPC sales are up and growing ($22 billion, all HPC segments, 2016). 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

CERN openlab Explores New CPU/FPGA Processing Solutions

April 14, 2017

Through a CERN openlab project known as the ‘High-Throughput Computing Collaboration,’ researchers are investigating the use of various Intel technologies in data filtering and data acquisition systems. Read more…

By Linda Barney

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

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 phase of neural networks (NN). 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

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

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

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

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

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

Leading Solution Providers

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

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

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

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

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

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