SC10 Disruptive Technology Preview: The First Cloud Portal to “R” and Beyond

By Nicole Hemsoth

October 26, 2010

At each annual Supercomputing Conference a handful of innovations are selected as the year’s “disruptive technologies” that are most likely to revolutionize high-performance computing. These are described as “drastic innovations in current practices…that have the potential to completely transform” the landscape. 

At this year’s event in New Orleans, the focus will be on “new computing architectures and interfaces that will significantly impact the high-performance computing field throughout the next five to 15 years,” a focus that is reflected in the list of disruptive exhibitors who were selected by an SC committee. 

Another “qualification” of those selected innovations is that they cannot have already emerged into the landscape in any meaningful way—that they sit on the bleeding edge waiting for impetus to burst forth and cause a paradigm shift.

At the edge of this potential sea-change in HPC—and included on that SC10 list of innovations this year is a one-man show run by Karim Chine of his newly-minted company, Cloud Era, Ltd.

Chine’s opportunity to showcase his “Google Docs-like portal for scientific computing in the cloud” could mean that his three-year effort, which he bootstrapped after he was unable to secure the funding needed for his research and development process, could garner some significant interest and make what this self-described “social entrepreneur” calls a real, universal impact in the broad field of large-scale data analysis.

Chine’s goal when he began the project after leaving academia was to bring the R language to the cloud and deliver it seamlessly to users who can share infrastructure and collaborate in real-time with a wide range of documents and computational tools. Or at least that’s the Reader’s Digest version–the actual technology and processes that create the experience for technical users goes far beyond these elements in terms of complexity and what is possible.

From the outset, Chine saw the inherent value of R as a ubiquitous tool but also recognized that there are a number of embedded challenges to using the language in terms of memory and compute capabilities being stretched to the limit. On the other end of the spectrum, he also saw how he could carry over lessons from social networks. Chine notes that part of what makes his Elastic-R project innovative–disruptive, even–is that users can move beyond sharing static information as they would on social networking platform and instead have a scientific network where real-time information sharing would be at the core of the communities.

The R Language Coming to a Browser Near You

It’s far too simple to suggest that what makes the platform unique or disruptive is the capacity for real-time resource and information-sharing. At the core of this innovation is the enhanced ability for researchers to use R, Scilab, and other tools in a new way–on the “infinite” resources provided by the cloud.

Many will agree that the R language is the lingua franca of data analysis—it’s the standard for nearly all statistics students in every major university and has a user base that some estimate is well over one million. In Chine’s view, the beauty of the R language, which is an open source implementation of S, lies “not just in statistics, not just in open source, it’s become the environment where people share scientific artifacts—where people contribute and access powerful tools for working with data.”

Although Chine discussed at length some of the benefits of the R language for scientists and researchers, he noted that there are some significant limitations to the language, particularly in the arena of software architecture and the R’s distinct lack of ability to optimize memory usage. However, the memory and architecture problems can be addressed by delivering R via cloud-based resources like EC2—in an environment where a user is no longer constrained by compute or memory and where inexpensive machine instances with 70 GB of RAM can be called into action in a few moments.

The idea of a “few moments” to get an instance up and running might strike some newer EC2 users as a little far-fetched, which leads to another issue that Elastic-R might be able to solve. One of the goals Chine had in mind was not only to provide a resource that would make R available via a web browser on a machine like an iPad, for instance, which has limited compute capacity, but to deliver the resource in a way that is intuitive and takes away from potential complexity in accessing remote infrastructure.

Elastic-R enables scientists, educators and students to use cloud resources seamlessly, work with R engines and use their full capabilities from within any standard web browser. For example, they can collaborate in real time, create, share and reuse machines, sessions, data functions, spreadsheets, dashboards, etc.”

Elastic-R is also an applications platform that allows anyone to assemble statistical methods and data with interactive user interfaces for the end user. These interfaces and dashboards are created visually and are automatically published and delivered as simple web applications.”

For Chine, the revolutionary or disruptive nature of Elastic-R lies in its user-friendliness, something that few people might say about the static R language. He states that offering a platform on top of R that is easy to work with in any browser allows people to access infrastructure without being computer savvy or with any real specific training. In essence, in three minutes you can have simple access to machines on EC2 that will allow you to do anything you want with large-scale data.

Even more disruptive, however, is the fact that users can hook in other scientific computing tools like Scilab or MATLAB thus making it a universal platform that is open to change and adds the possibility of throwing in additional tools to enhance research. They can then eliminate the problems involved with having their data in disparate formats that can complicate sharing by porting their results directly into standard Microsoft Office tools that can be shared and edited in real time via the web interface.

Taking R Beyond the Public Cloud

At the moment the resource can only be deployed using Amazon EC2 but this is simply a matter of how far Chine has traveled with his experiences—in theory, this can run on any resource. For instance, when he first began rolling out the prototype version of Elastic-R, he did so on the National Grid Services in the U.K. using a standard cluster, which would be possible on any other resource he might have selected.

The point is that what Chine has created is agnostic to the hardware and operating system, so users can connect to computational engines via their browsers, thus enabling to work with large-scale data that you don’t move, but can share with others for collaboration in real-time.

As Chine stated, “What’s wonderful about Amazon is that they already deliver the most significant public cloud of the moment, but also that they’ve blurred the frontier between normal computing and HPC…For the end user or interaction design perspective there’s no borderline between general computing and high-performance computing now.”

There are a range of capabilities that Elastic-R that are almost too numerous to mention in a relatively short article. In fact, this seems to be one of the reasons why this is such a disruptive technology; it’s multi-layered in its potential usefulness. Scientists and researchers can open mainstream computing environments beyond R (Scilab, SciPy, Sage, etc.) can issue commands to the remote R engne, install and deploy new packages, and easily run computationally-intensive algorithms virtually that are managed through the simple interface, then share all of it, including the computational resources themselves.

The following is from a slide out of the following deck (the presentation, which is the pptx file provides a more in-depth overview of the layers of the Elastic-R portal and what it provides) showing the onion-like way users can visualize their access to resources and tools.

During an interview with Karim Chine, I was granted access to the interface to watch how collaboration happens and how resources are secured. Without much experience at all, it was possible to understand intuitively understand exactly what was needed to get my job running, to indentify where the results were, who I could share them with and how at the exact same moment I updated a spreadsheet, my partner on the other side of the ocean could see my changes in real time. Real-time. There was no delay. The moment he replaced a “5” with a “6” on his end I saw it on my own browser screen.

This is big news for the future of scientific collaboration and computation using remote resources. 

A Business Model Still in the Making

Chine’s goals are multi-layered and go far beyond making R more accessible to greater numbers of researchers via the cloud—he hopes to create a “Facebook” for scientists and statisticians where they can share and collaborate with big data in real time using a simple interface that they can build applications on top of and add or shed layers of computational tools and resources seamlessly.

 As a social entrepreneur, Chine notes that this interface, as it develops, means that researchers in developing countries without access to high-performance computing resources can now easily create machine instances for small sums and even if those prices are too high, they can also share infrastructure with collaborating participants.

In essence, what this means is that there is not only an economy of information sharing involved with this disruptive innovation—there is an economic angle that allows researchers to extend their infrastructure to those across the world easily and in only a few moments.

As a business model, however, there are some issues that Chine admits he is still working to resolve. On the one hand, he sees the possibility of involving those who make scientific tools available, including The MathWorks, partnering in a revenue-sharing sense once those tools are integrated. He also sees value for supercomputing centers that might want to provide a simpler and more streamlined way to access and use high-performance computing infrastructure.

For now, however, he admits that he is just waiting to see how useful this will be as he extends his user base, which is currently only at 140 members—all of whom he knows personally. He will be announcing the technology just before SC10 as publicly available.

While the cloud can open the doors to enhanced collaboration and resource sharing as well as providing the tools researchers need, there is a remaining need for software that creates a sturdy bridge between the tools for scientific computation and the cloud, which is where Elastic-R fits into the picture.

Coupled with the open, collaborative nature of the project, which is driven by its social entrepreneur founder and creator, it will be thrilling indeed to watch how the community receives, uses, then builds on this disruptive innovation.

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!

Doug Kothe on the Race to Build Exascale Applications

May 29, 2017

Ensuring there are applications ready to churn out useful science when the first U.S. exascale computers arrive in the 2021-2023 timeframe is Doug Kothe’s job Read more…

By John Russell

PRACEdays Reflects Europe’s HPC Commitment

May 25, 2017

More than 250 attendees and participants came together for PRACEdays17 in Barcelona last week, part of the European HPC Summit Week 2017, held May 15-19 at t Read more…

By Tiffany Trader

Russian Researchers Claim First Quantum-Safe Blockchain

May 25, 2017

The Russian Quantum Center today announced it has overcome the threat of quantum cryptography by creating the first quantum-safe blockchain, securing cryptocurr Read more…

By Doug Black

Google Debuts TPU v2 and will Add to Google Cloud

May 25, 2017

Not long after stirring attention in the deep learning/AI community by revealing the details of its Tensor Processing Unit (TPU), Google last week announced the Read more…

By John Russell

HPE Extreme Performance Solutions

Exploring the Three Models of Remote Visualization

The explosion of data and advancement of digital technologies are dramatically changing the way many companies do business. With the help of high performance computing (HPC) solutions and data analytics platforms, manufacturers are developing products faster, healthcare providers are improving patient care, and energy companies are improving planning, exploration, and production. Read more…

Nvidia CEO Predicts AI ‘Cambrian Explosion’

May 25, 2017

The processing power and cloud access to developer tools used to train machine-learning models are making artificial intelligence ubiquitous across computing pl Read more…

By George Leopold

PGAS Use will Rise on New H/W Trends, Says Reinders

May 25, 2017

If you have not already tried using PGAS, it is time to consider adding PGAS to the programming techniques you know. Partitioned Global Array Space, commonly kn Read more…

By James Reinders

Exascale Escapes 2018 Budget Axe; Rest of Science Suffers

May 23, 2017

President Trump's proposed $4.1 trillion FY 2018 budget is good for U.S. exascale computing development, but grim for the rest of science and technology spend Read more…

By Tiffany Trader

Hedge Funds (with Supercomputing help) Rank First Among Investors

May 22, 2017

In case you didn’t know, The Quants Run Wall Street Now, or so says a headline in today’s Wall Street Journal. Quant-run hedge funds now control the largest Read more…

By John Russell

Doug Kothe on the Race to Build Exascale Applications

May 29, 2017

Ensuring there are applications ready to churn out useful science when the first U.S. exascale computers arrive in the 2021-2023 timeframe is Doug Kothe’s job Read more…

By John Russell

PRACEdays Reflects Europe’s HPC Commitment

May 25, 2017

More than 250 attendees and participants came together for PRACEdays17 in Barcelona last week, part of the European HPC Summit Week 2017, held May 15-19 at t Read more…

By Tiffany Trader

PGAS Use will Rise on New H/W Trends, Says Reinders

May 25, 2017

If you have not already tried using PGAS, it is time to consider adding PGAS to the programming techniques you know. Partitioned Global Array Space, commonly kn Read more…

By James Reinders

Exascale Escapes 2018 Budget Axe; Rest of Science Suffers

May 23, 2017

President Trump's proposed $4.1 trillion FY 2018 budget is good for U.S. exascale computing development, but grim for the rest of science and technology spend Read more…

By Tiffany Trader

Cray Offers Supercomputing as a Service, Targets Biotechs First

May 16, 2017

Leading supercomputer vendor Cray and datacenter/cloud provider the Markley Group today announced plans to jointly deliver supercomputing as a service. The init Read more…

By John Russell

HPE’s Memory-centric The Machine Coming into View, Opens ARMs to 3rd-party Developers

May 16, 2017

Announced three years ago, HPE’s The Machine is said to be the largest R&D program in the venerable company’s history, one that could be progressing tow Read more…

By Doug Black

What’s Up with Hyperion as It Transitions From IDC?

May 15, 2017

If you’re wondering what’s happening with Hyperion Research – formerly the IDC HPC group – apparently you are not alone, says Steve Conway, now senior V Read more…

By John Russell

Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a 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. Just how close real-wo 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 cam Read more…

By John Russell

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 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 a Read more…

By Tiffany Trader

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

Since our first formal product releases of OSPRay and OpenSWR libraries in 2016, CPU-based Software Defined Visualization (SDVis) has achieved wide-spread adopt Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Last week, Google reported that its custom ASIC Tensor Processing Unit (TPU) was 15-30x faster for inferencing workloads than Nvidia's K80 GPU (see our coverage Read more…

By Tiffany Trader

Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a 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 ne Read more…

By Tiffany Trader

Leading Solution Providers

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 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 w 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 Read more…

By Steve Campbell

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 Eng Read more…

By Tiffany Trader

US Supercomputing Leaders Tackle the China Question

March 15, 2017

As China continues to prove its supercomputing mettle via the Top500 list and the forward march of its ambitious plans to stand up an exascale machine by 2020, 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 networ Read more…

By Tiffany Trader

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 advance Read more…

By Tiffany Trader

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" process Read more…

By Tiffany Trader

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