Network-Attached Memory: Virtualization for Java Environments

By Dennis Barker

October 6, 2008

Easing application scalability across a cluster is a problem being solved in a variety of ways. Terracotta does it with memory – network-attached memory, to be exact. The company’s infrastructure software creates an expandable/retractable pool of shared memory that Java applications can tap to meet increasing demand.

Network-attached memory is analogous to network-attached storage (NAS) in that it provides a service to thousands of connected clients transparently. As NAS is transparent underneath the file system, network-attached memory is transparent underneath the Java language, says Jeff Hartley, vice president of products and marketing at Terracotta. Objects are manipulated and kept consistent in memory like files are in NAS, but in Terracotta’s memory pool, everything can be massively scaled out.

“Another way to think of it is virtualization for the Java environment,” Hartley says. “In the same way that a hypervisor slices a machine into several logical machines, Terracotta takes many physical machines and connects them as one logical machine.”

Developers don’t have to change their applications to get clustering behavior. A clustered application looks the same as a Java application. Essentially, users tell Terracotta what to do in a config file, Hartley says, and the software injects that behavior into the application at runtime. Not only is there is no API required to send messages across the cluster, he says, there is no API at all. Terracotta uses plain old Java objects and plugs into familiar frameworks like Spring, Hibernate and EHcache so developers can continue using the same tools, stacks and development models.

“We provide an open source clustering solution,” says Hartley. “It’s scalability and high availability for Java enterprise apps, without having to change your application code. We actually hook into the Java virtual machine and share data.”

Beside simplified clustering and scalable performance, Hartley says Terracotta’s approach also brings “high availability without tradeoffs” — the possibility of reducing database bottlenecks, achieving better use of hardware, lowering maintenance costs, and, because the software takes care of adding clustering capabilities, focusing on developing new applications rather than retooling old ones.

“We’re working at the level of memory. If you have App Server A die, the user gets sent to App Server B, and all the data is sent from memory to B without the user even realizing it,” Hartley says. “We provide high availability by putting everything in memory instead of adding racks of servers.”

When you add a server to handle demand, it just “joins the group.” “You don’t have to implement anything. Just add the servers and we make them members of the cluster’s shared memory pool. Our server keeps your server’s data in sync,” Hartley says. Changes to one virtual machine are instantaneously reflected to every virtual machine throughout the cluster that needs to know.

Because data can be shared between Java virtual machines and processed at in-memory speeds, some customers use the software to take load off their databases. Transient data like user session info or shopping cart info can be kept and processed in memory, while only critical results are sent to and kept in the database. “Terracotta is used to handle the work-in-progress data while a process is running, and only the completed data goes to the database. As a result, some of our users have been able to reduce database utilization by as much as 70 percent and not have to buy more database licenses to meet increased workload,” Hartley says.

Eliminating mundane work for IT staff is one of Terracotta’s other major selling points. As company CTO Ari Zilka explains in a video tour of the software, “You don’t have to write the plumbing or maintain the plumbing. …You can run an app on two servers at midnight and on 20 servers at noon. It frees IT to run apps the way they need to run them” and focus development time on more important business issues, he says.

Deployment involves two primary components: client nodes and the Terracotta server array. Nodes run on standard Java VMs, and each node corresponds to a Java process in the cluster (e.g., the application server). Terracotta is loaded into each VM at startup. The Terracotta server array provides the intelligence to orchestrate all the nodes in the cluster, synchronize activity between them, replicate data and handle storing data to disk. The array can run in an active-passive pair configuration for high availability that can achieve tens of thousands of requests per second, the company says. Running multiple instances of the Terracotta server in active-passive mode guarantees that a failure won’t compromise the cluster, the company says.

“We’re a very fundamental technology. We’re not a grid solution, we’re not a cloud solution. We’re network-attached memory — distributed memory that sits right below the applications, and can be used for all kinds of things,” explains Hartley.

Demanding Customers

Customers running Terracotta typically have one thing in common: unpredictable numbers of demanding users. (Zilka used to be chief architect at Walmart.com, so he knows about building infrastructure to deal with traffic spikes of epic proportion.) The user list includes big names familiar with meeting heavy online demand, including Adobe, MapQuest, BBC, Electronic Arts, PartyGaming (PartyPoker.com, etc.), and financial services companies like JP Morgan and Mizuho Securities.

An online multiplayer gaming company that wishes to remain unnamed uses clustered servers to not just host games, but also to coordinate and track player activities. It chose Terracotta as its scale-out solution for several reasons: (1) it works behind the scenes, at byte-code level, providing distributed heap memory across multiple Java VMs; (2) developers can use the standard Java semantics for synchronizing access to shared objects; (3) “impressive horizontal scalability” enabled by just adding more server nodes and not having to use databases and caches to manage shared data; (4) no single point of failure; and (5) it’s open source, so the software doesn’t add to the bottom line and doesn’t require a purchase order to get started.

Mark Turansky, a software architect currently working in the health care industry, has written about his experience using Terracotta. “With enabling software like Terracotta, clustering becomes easy. You’ve still got to design your software to take advantage of parallelism, but the act of running programs in parallel is no longer difficult. …It invisibly and magically clusters your Java classes via configuration,” Turansky writes. “Distributing code and running massively parallel programs used to be difficult. It required complex architectures and expensive application servers. This is accidental complexity. Advances in software development — like Terracotta, GridGain, Spring, and other FOSS [free open source] programs — dramatically reduce if not eliminate the accidental complexity of distributing your programs to a cluster of machines.”

Gnip, a service that aggregates and distributes feeds from sites like Twitter and Digg to users of services like Plaxo, runs its system on Amazon’s EC2 but chose Terracotta for node replication at the memory level. A post on the company’s site explains: “The prospect of just writing our app and thinking of it as a single thing, rather than ‘how does all this state get replicated across n number of nodes’ was soooo appealing.”

Terracotta has just released version 2.7, which the company says is more tightly integrated with frameworks like Spring and Glassfish, and adds better management and visualization features, improved garbage collection and the ability to apply hot patches. It is available for download at www. terracotta.org.

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!

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

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

HPE Extreme Performance Solutions

Quants Achieving Maximum Compute Power without the Learning Curve

The financial services industry is a fast-paced and data-intensive environment, and financial firms are realizing that they must modernize their IT infrastructures and invest in high performance computing (HPC) tools in order to survive. Read more…

‘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

Swiss Researchers Peer Inside Chips with Improved X-Ray Imaging

March 22, 2017

Peering inside semiconductor chips using x-ray imaging isn’t new, but the technique hasn’t been especially good or easy to accomplish. Read more…

By John Russell

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

Nvidia Debuts HGX-1 for Cloud; Announces Fujitsu AI Deal

March 9, 2017

On Monday Nvidia announced a major deal with Fujitsu to help build an AI supercomputer for RIKEN using 24 DGX-1 servers. Read more…

By John Russell

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