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!

AWS Embraces FPGAs, ‘Elastic’ GPUs

December 2, 2016

A new instance type rolled out this week by Amazon Web Services is based on customizable field programmable gate arrays that promise to strike a balance between performance and cost as emerging workloads create requirements often unmet by general-purpose processors. Read more…

By George Leopold

AWS Launches Massive 100 Petabyte ‘Sneakernet’

December 1, 2016

Amazon Web Services now offers a way to move data into its cloud by the truckload. Read more…

By Tiffany Trader

Weekly Twitter Roundup (Dec. 1, 2016)

December 1, 2016

Here at HPCwire, we aim to keep the HPC community apprised of the most relevant and interesting news items that get tweeted throughout the week. Read more…

By Thomas Ayres

HPC Career Notes (Dec. 2016)

December 1, 2016

In this monthly feature, we’ll keep you up-to-date on the latest career developments for individuals in the high performance computing community. Read more…

By Thomas Ayres

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

IBM and NSF Computing Pioneer Erich Bloch Dies at 91

November 30, 2016

Erich Bloch, a computational pioneer whose competitive zeal and commercial bent helped transform the National Science Foundation while he was its director, died last Friday at age 91. Bloch was a productive force to be reckoned. During his long stint at IBM prior to joining NSF Bloch spearheaded development of the “Stretch” supercomputer and IBM’s phenomenally successful System/360. Read more…

By John Russell

Pioneering Programmers Awarded Presidential Medal of Freedom

November 30, 2016

In an awards ceremony on November 22, President Barack Obama recognized 21 recipients with the Presidential Medal of Freedom, the Nation’s highest civilian honor. Read more…

By Tiffany Trader

Seagate-led SAGE Project Delivers Update on Exascale Goals

November 29, 2016

Roughly a year and a half after its launch, the SAGE exascale storage project led by Seagate has delivered a substantive interim report – Data Storage for Extreme Scale. Read more…

By John Russell

AWS Launches Massive 100 Petabyte ‘Sneakernet’

December 1, 2016

Amazon Web Services now offers a way to move data into its cloud by the truckload. Read more…

By Tiffany Trader

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

Seagate-led SAGE Project Delivers Update on Exascale Goals

November 29, 2016

Roughly a year and a half after its launch, the SAGE exascale storage project led by Seagate has delivered a substantive interim report – Data Storage for Extreme Scale. Read more…

By John Russell

Nvidia Sees Bright Future for AI Supercomputing

November 23, 2016

Graphics chipmaker Nvidia made a strong showing at SC16 in Salt Lake City last week. Read more…

By Tiffany Trader

HPE-SGI to Tackle Exascale and Enterprise Targets

November 22, 2016

At first blush, and maybe second blush too, Hewlett Packard Enterprise’s (HPE) purchase of SGI seems like an unambiguous win-win. SGI’s advanced shared memory technology, its popular UV product line (Hanna), deep vertical market expertise, and services-led go-to-market capability all give HPE a leg up in its drive to remake itself. Bear in mind HPE came into existence just a year ago with the split of Hewlett-Packard. The computer landscape, including HPC, is shifting with still unclear consequences. One wonders who’s next on the deal block following Dell’s recent merger with EMC. Read more…

By John Russell

Intel Details AI Hardware Strategy for Post-GPU Age

November 21, 2016

Last week at SC16, Intel revealed its product roadmap for embedding its processors with key capabilities and attributes needed to take artificial intelligence (AI) to the next level. Read more…

By Alex Woodie

SC Says Farewell to Salt Lake City, See You in Denver

November 18, 2016

After an intense four-day flurry of activity (and a cold snap that brought some actual snow flurries), the SC16 show floor closed yesterday (Thursday) and the always-extensive technical program wound down today. Read more…

By Tiffany Trader

D-Wave SC16 Update: What’s Bo Ewald Saying These Days

November 18, 2016

Tucked in a back section of the SC16 exhibit hall, quantum computing pioneer D-Wave has been talking up its new 2000-qubit processor announced in September. Forget for a moment the criticism sometimes aimed at D-Wave. This small Canadian company has sold several machines including, for example, ones to Lockheed and NASA, and has worked with Google on mapping machine learning problems to quantum computing. In July Los Alamos National Laboratory took possession of a 1000-quibit D-Wave 2X system that LANL ordered a year ago around the time of SC15. Read more…

By John Russell

Why 2016 Is the Most Important Year in HPC in Over Two Decades

August 23, 2016

In 1994, two NASA employees connected 16 commodity workstations together using a standard Ethernet LAN and installed open-source message passing software that allowed their number-crunching scientific application to run on the whole “cluster” of machines as if it were a single entity. Read more…

By Vincent Natoli, Stone Ridge Technology

IBM Advances Against x86 with Power9

August 30, 2016

After offering OpenPower Summit attendees a limited preview in April, IBM is unveiling further details of its next-gen CPU, Power9, which the tech mainstay is counting on to regain market share ceded to rival Intel. Read more…

By Tiffany Trader

AWS Beats Azure to K80 General Availability

September 30, 2016

Amazon Web Services has seeded its cloud with Nvidia Tesla K80 GPUs to meet the growing demand for accelerated computing across an increasingly-diverse range of workloads. The P2 instance family is a welcome addition for compute- and data-focused users who were growing frustrated with the performance limitations of Amazon's G2 instances, which are backed by three-year-old Nvidia GRID K520 graphics cards. Read more…

By Tiffany Trader

Think Fast – Is Neuromorphic Computing Set to Leap Forward?

August 15, 2016

Steadily advancing neuromorphic computing technology has created high expectations for this fundamentally different approach to computing. Read more…

By John Russell

The Exascale Computing Project Awards $39.8M to 22 Projects

September 7, 2016

The Department of Energy’s Exascale Computing Project (ECP) hit an important milestone today with the announcement of its first round of funding, moving the nation closer to its goal of reaching capable exascale computing by 2023. Read more…

By Tiffany Trader

HPE Gobbles SGI for Larger Slice of $11B HPC Pie

August 11, 2016

Hewlett Packard Enterprise (HPE) announced today that it will acquire rival HPC server maker SGI for $7.75 per share, or about $275 million, inclusive of cash and debt. The deal ends the seven-year reprieve that kept the SGI banner flying after Rackable Systems purchased the bankrupt Silicon Graphics Inc. for $25 million in 2009 and assumed the SGI brand. Bringing SGI into its fold bolsters HPE's high-performance computing and data analytics capabilities and expands its position... Read more…

By Tiffany Trader

ARM Unveils Scalable Vector Extension for HPC at Hot Chips

August 22, 2016

ARM and Fujitsu today announced a scalable vector extension (SVE) to the ARMv8-A architecture intended to enhance ARM capabilities in HPC workloads. Fujitsu is the lead silicon partner in the effort (so far) and will use ARM with SVE technology in its post K computer, Japan’s next flagship supercomputer planned for the 2020 timeframe. This is an important incremental step for ARM, which seeks to push more aggressively into mainstream and HPC server markets. Read more…

By John Russell

IBM Debuts Power8 Chip with NVLink and Three New Systems

September 8, 2016

Not long after revealing more details about its next-gen Power9 chip due in 2017, IBM today rolled out three new Power8-based Linux servers and a new version of its Power8 chip featuring Nvidia’s NVLink interconnect. Read more…

By John Russell

Leading Solution Providers

Vectors: How the Old Became New Again in Supercomputing

September 26, 2016

Vector instructions, once a powerful performance innovation of supercomputing in the 1970s and 1980s became an obsolete technology in the 1990s. But like the mythical phoenix bird, vector instructions have arisen from the ashes. Here is the history of a technology that went from new to old then back to new. Read more…

By Lynd Stringer

US, China Vie for Supercomputing Supremacy

November 14, 2016

The 48th edition of the TOP500 list is fresh off the presses and while there is no new number one system, as previously teased by China, there are a number of notable entrants from the US and around the world and significant trends to report on. Read more…

By Tiffany Trader

Intel Launches Silicon Photonics Chip, Previews Next-Gen Phi for AI

August 18, 2016

At the Intel Developer Forum, held in San Francisco this week, Intel Senior Vice President and General Manager Diane Bryant announced the launch of Intel's Silicon Photonics product line and teased a brand-new Phi product, codenamed "Knights Mill," aimed at machine learning workloads. 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

Beyond von Neumann, Neuromorphic Computing Steadily Advances

March 21, 2016

Neuromorphic computing – brain inspired computing – has long been a tantalizing goal. The human brain does with around 20 watts what supercomputers do with megawatts. And power consumption isn’t the only difference. Fundamentally, brains ‘think differently’ than the von Neumann architecture-based computers. While neuromorphic computing progress has been intriguing, it has still not proven very practical. Read more…

By John Russell

Dell EMC Engineers Strategy to Democratize HPC

September 29, 2016

The freshly minted Dell EMC division of Dell Technologies is on a mission to take HPC mainstream with a strategy that hinges on engineered solutions, beginning with a focus on three industry verticals: manufacturing, research and life sciences. "Unlike traditional HPC where everybody bought parts, assembled parts and ran the workloads and did iterative engineering, we want folks to focus on time to innovation and let us worry about the infrastructure," said Jim Ganthier, senior vice president, validated solutions organization at Dell EMC Converged Platforms Solution Division. Read more…

By Tiffany Trader

Container App ‘Singularity’ Eases Scientific Computing

October 20, 2016

HPC container platform Singularity is just six months out from its 1.0 release but already is making inroads across the HPC research landscape. It's in use at Lawrence Berkeley National Laboratory (LBNL), where Singularity founder Gregory Kurtzer has worked in the High Performance Computing Services (HPCS) group for 16 years. Read more…

By Tiffany Trader

Micron, Intel Prepare to Launch 3D XPoint Memory

August 16, 2016

Micron Technology used last week’s Flash Memory Summit to roll out its new line of 3D XPoint memory technology jointly developed with Intel while demonstrating the technology in solid-state drives. Micron claimed its Quantx line delivers PCI Express (PCIe) SSD performance with read latencies at less than 10 microseconds and writes at less than 20 microseconds. Read more…

By George Leopold

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