SAS Brings High Performance Analytics to Database Appliances

By Michael Feldman

April 19, 2011

In early April the SAS Institute (SAS) announced it had integrated its most advanced analytics software into database appliances from EMC Greenplum and Teradata Corporation. The new offerings marry high performance computing to “big data” and are designed to enable users to perform deep analysis on huge datasets hosted on purpose-built, parallel computing platforms.

Today SAS is considered the undisputed leader in advanced analytics — that according to IDC who, in 2009, pegged the company with a 34.7 percent market share in this category. A subset of business analytics, advanced analytics uses compute-intensive data mining and statistical software techniques to extract complex relationships from databases. For SAS, it’s a half a billion dollar business.

Competitors include IBM’s SPSS and lesser-used offerings from Microsoft, TIBCO, Oracle and others. Revolution Analytics, which recently developed an enterprise-capable version of R for statistical analysis, has only 100 or so deployments at this point, but its leverage of the popular open-source R language introduces a new model for advanced analytics users.

At the simplest level, advanced analytics allow you to develop models and then use them to ask “What if?” questions about your data. For example, developing a statistical model that associates buying behavior with customer profiles can then be applied to future behavior of customers. The application of that model is refered to as “scoring” and is the basis for predictive analytics.

That type of analysis is worlds away from traditional business intelligence, which is more about asking simple questions about data in one or two dimensions (e.g., How many shoes of Brand X do we have in stock?). That kind of analysis is fairly straightforward using a traditional database, needing only a small pipe to get the data in and out and a software component on the client to manage the interface.

“Business intelligence got shanghaied during the 1980s to just mean query and reporting,” says SAS CTO Keith Collins. “We are talking about much more than that.”

According to Collins, the high performance analytics SAS has in mind will be a “game changer” for the industry. He says it will do so by addressing both sides of the problem: the increasing size of enterprise datasets — terabytes, scaling to petabytes — and the need to get actionable intelligence from them in a timely manner. Traditionally, the compute- and data-intensive nature of advanced analytics tools has relegated their use to dataset samples, which not only requires extra time and effort, but also introduces inaccuracies associated with working on incomplete data.

The obvious solution is to put the compute next to the data, in this case, on the high performance data platforms themselves, thus eliminating the need to sample. And since these appliances are essentially HPC clusters (with added storage and software needed to house large databases), the CPUs and memory can be used to run the analytics natively. The data prep, model creation and scoring as well as the actual analytics are performed on the appliance servers, and in parallel fashion.

Conveniently, this can be done within the existing SAS language environment. Customers with legacy code can apply those applications to this new high performance environment with the trivial specification of HP (high performance) at the time of invocation. All of this is made possible by the invention of relatively inexpensive database appliances, which, like the HPC industry in general, has moved from SMP architectures to distributed clustered platforms employing commodity parts, Linux, and x86 CPUs.

In the case of Teradata and Greenplum, the basic appliance hardware is very similar, both based on dual-socket 2.93 GHz Westmere Xeon CPUs and outfitted with 48 GB of memory per node. The Teradata platform uses a proprietary system interconnect called BYNET, while the Greenplum machines rely on standard 10Gig Ethernet.

Storage-wise, the Teradata platform sports 1 and 2 TB SATA drives, and can scale from 45 TB on a single server instance up to 186 PB on 4,096 nodes. Alternatively, the company offers a performance version that uses SSD technology and tops out at 24 TB of total capacity.

Greenplum also has capacity and performance models of its appliance, employing both hard drives and SSDs accordingly. In this case, though, the spinning drives are Serial Attached SCSI. In Greenplum’s high capacity configuration, its appliance scales from 31 TB in a quarter rack up to 744 TB in six full racks.

In early April, SAS demonstrated the power of high performance analytics at its Global Forum meeting. In the first case, two racks (16 nodes) of Greenplum’s Data Computing Appliance (DCA) were used to run a logistic regression of bank loan defaults across a database with a billion records, applying just a few variables. The regression was able to complete in less than 80 seconds (as compared to 20 hours for an unspecified serial implementation). Another demonstration, this time on a 24-node Teradata platform, used 1,800 variables applied to 50 million observations. In this case, the analysis finished in 42 seconds.

Not everyone will require this integrated model for high performance, but every use case for advanced analytics is fair game. This includes everything from fraud detection, loan analysis, customer preference tracking, and financial risk scoring, to improving manufacturing yields. The San Antonio Spurs basketball team has even used the technology to “optimize player performance.”

Collins says the early adopters for its high performance analytics offerings will be in the insurance and financial sectors, where the value obtained is easily transferred to the bottom line. Although he wouldn’t name names, SAS already has some number of companies under trial with the technology. General availability for the product on both the Greenplum and Teradata platforms is scheduled for the fourth quarter of 2011.

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