Cray Parlays Supercomputing Technology Into Big Data Appliance

By Michael Feldman

March 1, 2012

For the first time in its history, Cray has built something other than a supercomputer. On Wednesday, the company’s newly hatched YarcData division launched “uRiKA,” a hardware-software solution aimed at real-time knowledge discovery with terascale-sized data sets. The system is designed to serve businesses and government agencies that need to do high-end analytics in areas as diverse as social networking, financial management, healthcare, supply chain management, and national security.

As befits Cray’s MO, their target market for uRiKA, (pronounced Eureka) is slanted toward the cutting edge. It uses a graph-based data approach to do interactive analytics with large, complex, and often dynamic data sets. “We are not trying to be everything for everybody,” says YarcData general manager Arvind Parthasarathi.

Unlike Hadoop cluster implementations, which parallelizes queries across large cluster farms, uRiKA is a monolithic system with a lots of shared memory and massively multithreading processing. The supercomputer-style architecture allows uRiKA to load entire data sets into RAM and process them with hundreds or even thousands of threads. The idea is the avoid the performance penalty of dividing the database into pieces and processing it across disparate memory spaces. In such an environment, if a piece of a query on one node needs to talk to another piece on another node, communication has to be initiated across the network, which can be 100 times slower than a memory access.

The underlying hardware for uRiKA is Cray’s second generation XMT (previously known as XMT-2), which the company’s professional services group has been cranking on for the past three years. According to Shoaib Mufti, YarcData’s VP of Research & Development, the YarcData appliance repurposes the XT5 supercomputer infrastructure, including the AMD-style socket and SeaStar2 interconnect. Unlike the Opteron-powered XT5 though, uRiKA uses Cray’s latest custom-built Threadstorm processor, which supports 128 threads per chip and a memory reach of 512 TB. Processors and memory can be scaled independently, say Mufti.

Further boosting performance is the Threadstorm’s ability to support very fine-grained synchronization to hide latencies across threads. Merv Adrian, Research VP, of Information Management, at Gartner thinks that uRiKA hardware will be able to operate at speeds typical database appliances can’t match. “Processors will not wait on disk I/O, or even typical memory latency,” he says adding that the combo of hardware and software on uRiKA will “allow the company to target different, very challenging use cases.”

Up to 8,000 processors can be loaded on a single system, which would allow an application to scale to over a million threads. Most systems won’t approach anything of that size, though. “Our HPC customers tend to have a lot of processors,” says Mufti. “Here the customers we’re targeting tend to need a lot of memory.” That’s because the data sets YarcData has in mind are things like social media databases, financial asset portfolios, and genomic maps that span entire populations.

More to the point, uRiKA is designed to analyze graphs rather than simple tabular databases. A graph, one of the fundamental data abstractions in computer science, is basically a structure whose objects are linked together by some relationship. It is especially suited to structures like website links, social networks, and genetic maps — essentially any data set where the relationships between the objects are as important as the objects themselves.

This type of application exists further up the analytics food change than most business intelligence or data mining applications. In general, a lot of these more traditional applications involve searching for particular items or deriving simple relationships. The YarcData technology is focused on relationship discovery. And since it’s uses graph structures, the system can support graph-based reasoning and deductions to uncover new relationships.

A typical example is pattern-based queries — does x resemble y? This might not lead to a definitive answer, but will provide a range of possibilities, which can then be further refined. So, for example, one of the YarcData’s early customers is a government agency that is interested in finding “persons of interest.” They maintain profiles of terrorists, criminals or other ne’er-do-wells, and are using uRiKA to search for patterns of specific behaviors and activities. A credit card company could use the same basic algorithms to search for fraudulent transactions.

YarcData uses the term “relationship analytics” to describe this approach. While that might sound a bit Oprah-ish, it certainly emphasizes the importance of extracting knowledge from how the objects are connected rather than just their content. This is not to be confused with relational databases, which are organized in tabular form and use simpler forms of querying.

In fact, according to YarcData’s Parthasarathi, relational databases are not well suited to the kinds of large-scale, real-time data analysis uRiKA is designed for. He says it’s possible to shoehorn these applications into a relational databases using more traditional RDBMS tools, but the model just doesn’t scale very well as the data and relationship complexities grow. Especially if you’re looking to interact with the data in real time, it just takes too long, says Parthasarathi.

Parthasarathi also argues that traditional in-memory database platforms just don’t have enough memory to do graph problems. A single server might be able to be outfitted with a few terabytes, but once the data size grows beyond that, you have to start fetching bytes from external storage. And since graph analytics is non-deterministic, there’s no way to figure out which data should be pre-fetched or cached for a given query.

Being able to swallow an entire graph into memory is uRiKA’s biggest advantage over other architectures, but the system is also capable of ingesting data from secondary storage. Many of applications require this since their data is often very dynamic in nature (think of a financial trading system where asset values are constantly in motion). To deal meet that need, uRiKA offers a high performance storage subsystem that can deliver transfer rates of up to 350 TB/hour.

After data is ingested, it needs to be converted to an internal format called RDF, or Resource Description Framework (in case you were wondering, uRiKA stands for Universal RDF Integration Knowledge Appliance), an industry standard graph format for representing information in the Web. According to Mufti, they are providing tools for RDF data conversion and are also laying the groundwork for a standards-based software that allows for third-party conversion tools.

Industry standard is a common theme here. uRiKA’s software internals include SUSE Linux, Java, Apache, WS02, Google Gadgets, and Relfinder. That stack of interfaces allows users to write or port analytics applications to the platform without having to come up with a uRiKA-specific implementation. So Java, J2EE, SPARQL, and Gadget apps are all fair game. YarcData thinks this will be key to encouraging third-party developers to build applications on top of the system, since it doesn’t require them to use a whole new programming language or API.

The announcement this week pointed to five initial uRiKA customers. Besides the unnamed government agency mentioned previously, early adopters include the Institute of Systems Biology, which is targeting it for drug discovery; Noblis, which is engaged with various US government agencies to help develop a range graph database applications on the platform; the Swiss National Supercomputing Center (CSCS), which is using the system for scientific data analysis; and the Mayo Clinic, which intends to use uRiKA to pattern-match patients in order to optimize treatment regimes.

The latter application is reminiscent of IBM Watson’s work at Wellpoint, where the goal is to use the DeepQA expert system technology to suggest patient diagnosis and treatment options for doctors. In Watson’s case, the hardware and software architecture are completely different from that of uRiKA, but the level of analytics is of the same order. Like IBM, Cray is looking to establish its analytics technology across multiple verticals. In the future, YarcData intends to offer appliances with integrated software that targets specific application domains, like drug discovery, patient matching, and event-based trading.

Pricing on uRiKA configurations has not be made public, but according to Parthasarathi, a low-end setup will cost in the low hundreds of thousands of dollars. That probably corresponds to their baseline configuration of 16 Threadstorm processors and half a terabyte of memory. Additional memory and/or processors could easily push that into the million-dollar range, but considering there are no other systems on the market that sport terascale graph-based analytics, that could end up being a bargain.

Related articles

Cray Opens Doors on Big Data Developments

Can Supercomputing Help Cure Health Care?

Cray Pushes XMT Supercomputer Into the Limelight

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industry updates delivered to you every week!

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion XL — were added to the benchmark suite as MLPerf continues Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing power it brings to artificial intelligence.  Nvidia's DGX Read more…

Call for Participation in Workshop on Potential NSF CISE Quantum Initiative

March 26, 2024

Editor’s Note: Next month there will be a workshop to discuss what a quantum initiative led by NSF’s Computer, Information Science and Engineering (CISE) directorate could entail. The details are posted below in a Ca Read more…

Waseda U. Researchers Reports New Quantum Algorithm for Speeding Optimization

March 25, 2024

Optimization problems cover a wide range of applications and are often cited as good candidates for quantum computing. However, the execution time for constrained combinatorial optimization applications on quantum device Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at the network layer threatens to make bigger and brawnier pro Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HBM3E memory as well as the the ability to train 1 trillion pa Read more…

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing po Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HB Read more…

Nvidia Looks to Accelerate GenAI Adoption with NIM

March 19, 2024

Today at the GPU Technology Conference, Nvidia launched a new offering aimed at helping customers quickly deploy their generative AI applications in a secure, s Read more…

The Generative AI Future Is Now, Nvidia’s Huang Says

March 19, 2024

We are in the early days of a transformative shift in how business gets done thanks to the advent of generative AI, according to Nvidia CEO and cofounder Jensen Read more…

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, codenamed Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from Read more…

Nvidia Showcases Quantum Cloud, Expanding Quantum Portfolio at GTC24

March 18, 2024

Nvidia’s barrage of quantum news at GTC24 this week includes new products, signature collaborations, and a new Nvidia Quantum Cloud for quantum developers. Wh Read more…

Alibaba Shuts Down its Quantum Computing Effort

November 30, 2023

In case you missed it, China’s e-commerce giant Alibaba has shut down its quantum computing research effort. It’s not entirely clear what drove the change. Read more…

Nvidia H100: Are 550,000 GPUs Enough for This Year?

August 17, 2023

The GPU Squeeze continues to place a premium on Nvidia H100 GPUs. In a recent Financial Times article, Nvidia reports that it expects to ship 550,000 of its lat Read more…

Shutterstock 1285747942

AMD’s Horsepower-packed MI300X GPU Beats Nvidia’s Upcoming H200

December 7, 2023

AMD and Nvidia are locked in an AI performance battle – much like the gaming GPU performance clash the companies have waged for decades. AMD has claimed it Read more…

DoD Takes a Long View of Quantum Computing

December 19, 2023

Given the large sums tied to expensive weapon systems – think $100-million-plus per F-35 fighter – it’s easy to forget the U.S. Department of Defense is a Read more…

Synopsys Eats Ansys: Does HPC Get Indigestion?

February 8, 2024

Recently, it was announced that Synopsys is buying HPC tool developer Ansys. Started in Pittsburgh, Pa., in 1970 as Swanson Analysis Systems, Inc. (SASI) by John Swanson (and eventually renamed), Ansys serves the CAE (Computer Aided Engineering)/multiphysics engineering simulation market. Read more…

Choosing the Right GPU for LLM Inference and Training

December 11, 2023

Accelerating the training and inference processes of deep learning models is crucial for unleashing their true potential and NVIDIA GPUs have emerged as a game- Read more…

Intel’s Server and PC Chip Development Will Blur After 2025

January 15, 2024

Intel's dealing with much more than chip rivals breathing down its neck; it is simultaneously integrating a bevy of new technologies such as chiplets, artificia Read more…

Baidu Exits Quantum, Closely Following Alibaba’s Earlier Move

January 5, 2024

Reuters reported this week that Baidu, China’s giant e-commerce and services provider, is exiting the quantum computing development arena. Reuters reported � Read more…

Leading Solution Providers

Contributors

Comparing NVIDIA A100 and NVIDIA L40S: Which GPU is Ideal for AI and Graphics-Intensive Workloads?

October 30, 2023

With long lead times for the NVIDIA H100 and A100 GPUs, many organizations are looking at the new NVIDIA L40S GPU, which it’s a new GPU optimized for AI and g Read more…

Shutterstock 1179408610

Google Addresses the Mysteries of Its Hypercomputer 

December 28, 2023

When Google launched its Hypercomputer earlier this month (December 2023), the first reaction was, "Say what?" It turns out that the Hypercomputer is Google's t Read more…

AMD MI3000A

How AMD May Get Across the CUDA Moat

October 5, 2023

When discussing GenAI, the term "GPU" almost always enters the conversation and the topic often moves toward performance and access. Interestingly, the word "GPU" is assumed to mean "Nvidia" products. (As an aside, the popular Nvidia hardware used in GenAI are not technically... Read more…

Shutterstock 1606064203

Meta’s Zuckerberg Puts Its AI Future in the Hands of 600,000 GPUs

January 25, 2024

In under two minutes, Meta's CEO, Mark Zuckerberg, laid out the company's AI plans, which included a plan to build an artificial intelligence system with the eq Read more…

Google Introduces ‘Hypercomputer’ to Its AI Infrastructure

December 11, 2023

Google ran out of monikers to describe its new AI system released on December 7. Supercomputer perhaps wasn't an apt description, so it settled on Hypercomputer Read more…

China Is All In on a RISC-V Future

January 8, 2024

The state of RISC-V in China was discussed in a recent report released by the Jamestown Foundation, a Washington, D.C.-based think tank. The report, entitled "E Read more…

Intel Won’t Have a Xeon Max Chip with New Emerald Rapids CPU

December 14, 2023

As expected, Intel officially announced its 5th generation Xeon server chips codenamed Emerald Rapids at an event in New York City, where the focus was really o Read more…

IBM Quantum Summit: Two New QPUs, Upgraded Qiskit, 10-year Roadmap and More

December 4, 2023

IBM kicks off its annual Quantum Summit today and will announce a broad range of advances including its much-anticipated 1121-qubit Condor QPU, a smaller 133-qu Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire