Enabling Research with MATLAB on the TeraGrid

By Nicole Hemsoth

October 18, 2010

Rajesh Bhaskaran at Cornell’s Space Systems Design Studio CUSat Satellite Project is leading a multi-year effort to create and deploy an autonomous in-orbit inspection satellite system using a MATLAB-based simulation.

Meanwhile, Ricky Harjanto at UC San Diego’s Cartilage Tissue Engineering Lab is also using MATLAB to examine changes in the shape of mice femurs during postnatal development via statistical shape modeling techniques to determine variations in mouse development at different stages of growth.

At the same time, Harshal Mahajan at the University of Pittsburgh’s NSF Quality of Life Technology Center is modeling power wheelchair driving to determine different techniques to improve and enhance mobility for the many thousands who rely on safe, effective wheelchairs. Mahajan’s code uses the MATLAB system identification toolbox to build models from the wealth of driving data collected.

Outside of using MATLAB as their primary tool, these and other researchers have something else in common; they are all using Cornell University’s MATLAB on the TeraGrid experimental computing resource, which is helping them achieve fast results delivered to their desktop — and doing so in an operating environment they are already comfortable with.

High-Level Programming for the Non-Programmer

MATLAB is ubiquitous in scientific and large-scale computing with estimates closing in on over one million researchers who use the tool for a wide variety of technical computing applications. Outside of its use in technical applications, it is also being deployed to manipulate data gathered from a range of scientific instruments, including satellites, telescopes and sensors.

There are clear incentives to deliver easily-accessible software and computational resources to a large number of scientific users in general. This has been the goal of any number of universities and national labs from the era of grid until the present. This has been an aim of the National Science Foundation, which is one of a handful of funding sources for these types of projects and accordingly, it is not difficult to see how their interest was engaged when Cornell stated it would be capable of delivering MATLAB and high-performance computing to more researchers.

As Robert Buhrman, Senior Vice Provost for Research at Cornell, stated, “MATLAB on the TeraGrid will help enable a broader class of researchers who are well-versed in MATLAB to reduce the time to solution in a scalable manner without having to become parallel programming experts.” It is this reduced time to results and mitigation of programming challenges that makes this an attractive option — and one that has some direct results, judging from Cornell’s long list of research projects both pending and underway on the MATLAB and TeraGrid resource.

Part of the appeal for researchers is that the computational learning curve is diminished. Access to the 512-core resource does not require understanding of any particular operating system, MPI library, or batch scheduler. By utilizing the Parallel Computing Toolbox and the MATLAB Distributed Computing Server to access the resource via desktops and the TeraGrid science gateways, users who are part of TeraGrid are granted high-performance equipment without some of the common hassles on the programming front they used to encounter on a regular basis. In other words, it is allowing researchers to focus distinctly on their research problems, rather than forcing them to become, by proxy, experts in parallel programming.

The Partnership to Bring MATLAB to TeraGrid

Cornell University, in partnership with Purdue University, received an NSF grant to deploy MATLAB on the TeraGrid for what is currently deemed an experimental resource. Since MATLAB is such an important data tool for complex data analysis for many TeraGrid users, as a parallel resource it could provide an even greater opportunity to expand access to high-performance computing for researchers.

The goal of the partnership between the universities and the NSF is to provide “seamless parallel MATLAB computational services running on Windows HPC Server 2008 to remote desktop and Science Gateway users with complex analytic and fast simulation requirements.”

In a recent interview, David Lifka, director at the Cornell Center for Advanced Computing, noted that the funding from the NSF was in part to provide staff at Cornell that would develop software to allow MATLAB clients from any platform (Windows, Linux, Mac) to seamlessly connect to the experimental resource at Cornell and run jobs in parallel. This would mean that users would get their results back on their desktop via the Web interface without needing to learn a new batch system or new programming model. As Lifka explained, “Basically, once the users know MATLAB, they can use parallel MATLAB directly from their host client.”

The NSF also set aside funding for staff at Purdue University who were tasked with enabling the same sort of connectivity via their science gateway. Purdue has a software framework for building scientific gateways called HubZero — a framework that has been rising in popularity as more disciplines create domain-specific gateways of their own to share and augment research projects.

On a hardware and software level, it should be noted that Cornell’s cluster is not a “tricked out” resource by any means. The Dell PowerEdge HPC cluster is not a gigantic system; there are no special interconnects and it is not running any specialized, customized software. One look at the specs reveals that it’s running everything off the shelf, including Microsoft Windows HPC scheduler and the standard version of the MathWorks software, for example.

Lifka stated that the only part that is customized is the software interface that the client installs on his or her MATLAB client that handles the secure communication with the cluster to submit jobs.

The resource itself is modest, although the team hopes that it will eventually grow after proven success with the MATLAB on TeraGrid project. Current wait times are still an issue; this is not the instant-run access that some HPC-as-a-service providers from the “outside world” can deliver. The team publishes the current wait times, which generally run between three and four days, give or take.

Opening Doors to Discovery

MATLAB is in such wide use across disciplines because it allows researchers to focus on their immediate discipline-specific questions without needing to become advanced programmers. It is generally perceived as being far more compact for scientific and mathematical uses than Fortran or C, and for this reason, it is has become the most comfortable environment for many in academia, engineering and beyond. By delivering it to a larger number of users, Cornell, Purdue and TeraGrid are helping to advance scientific discovery and aid in the ease of access to many researchers.

“One of the beauties of MATLAB is that it’s such a broad tool that can be used across disciplines and that was the key thing we felt was important — and why we wanted to do this project with the NSF,” said Lifka. “The MathWorks’ MATLAB is used across business, academia and in national labs because it works and because it doesn’t require a steep learning curve. If you know your science and you know your MATLAB, you can get a lot done very quickly.”

Encouraging Broader Impact

Delivering parallel MATLAB as a resource for a broader class of researchers was part of what made the deal attractive to the National Science Foundation (NSF) as it examined the benefits of funding such a partnership. David Lifka, director at the Cornell Center for Advanced Computing, stated, “What we wanted to do and what the NSF wants to encourage is broader impact — bringing new users into the fold who need large-scale computing without the learning curve. We want to get them scaling their science up and hopefully, along the way, they ask some questions so we can continue to improve.”

The funding came from a Strategic Technologies and Cyberinfrastructure grant, which is backed by the NSF’s stated aims to bring new resources to bear to encourage greater access to high-performance computing. The idea behind the project is to present this as a resource so that later it can be determined whether or not this project will belong in the TeraGrid resource provider collection in the future. As Lifka noted, “We’re hopeful that someday we will be part of this collection, but today we’re not.”

Additional support for the project came from Dell, Microsoft and The Mathworks, purveyors of MATLAB. According to Lifka, this backing was due to the interest these stakeholders had in watching how utility computing could be made available and how the experimental resource might enable seamless access from Web to the desktop.

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!

China’s Expanding Effort to Win in Microchips

July 27, 2017

The global battle for preeminence, or at least national independence, in semiconductor technology and manufacturing continues to heat up with Europe, China, Japan, and the U.S. all vying for sway. A fascinating article ( Read more…

By John Russell

Hyperion: Storage to Lead HPC Growth in 2016-2021

July 27, 2017

Global HPC external storage revenues will grow 7.8% over the 2016-2021 timeframe according to an updated forecast released by Hyperion Research this week. HPC server sales, by comparison, will grow a modest 5.8% to $14.8 Read more…

By John Russell

Exascale FY18 Budget – The Senate Provides Their Input

July 27, 2017

In the federal budgeting world, “regular order” is a meaningful term that is fondly remembered by members of both the Congress and the Executive Branch. Regular order is the established process whereby an Administrat Read more…

By Alex R. Larzelere

HPE Extreme Performance Solutions

HPE Servers Deliver High Performance Remote Visualization

Whether generating seismic simulations, locating new productive oil reservoirs, or constructing complex models of the earth’s subsurface, energy, oil, and gas (EO&G) is a highly data-driven industry. Read more…

India Plots Three-Phase Indigenous Supercomputing Strategy

July 26, 2017

Additional details on India's plans to stand up an indigenous supercomputer came to light earlier this week. As reported in the Indian press, the Rs 4,500-crore (~$675 million) supercomputing project, approved by the Ind Read more…

By Tiffany Trader

Exascale FY18 Budget – The Senate Provides Their Input

July 27, 2017

In the federal budgeting world, “regular order” is a meaningful term that is fondly remembered by members of both the Congress and the Executive Branch. Reg Read more…

By Alex R. Larzelere

India Plots Three-Phase Indigenous Supercomputing Strategy

July 26, 2017

Additional details on India's plans to stand up an indigenous supercomputer came to light earlier this week. As reported in the Indian press, the Rs 4,500-crore Read more…

By Tiffany Trader

Tuning InfiniBand Interconnects Using Congestion Control

July 26, 2017

InfiniBand is among the most common and well-known cluster interconnect technologies. However, the complexities of an InfiniBand (IB) network can frustrate the Read more…

By Adam Dorsey

NSF Project Sets Up First Machine Learning Cyberinfrastructure – CHASE-CI

July 25, 2017

Earlier this month, the National Science Foundation issued a $1 million grant to Larry Smarr, director of Calit2, and a group of his colleagues to create a comm Read more…

By John Russell

Graphcore Readies Launch of 16nm Colossus-IPU Chip

July 20, 2017

A second $30 million funding round for U.K. AI chip developer Graphcore sets up the company to go to market with its “intelligent processing unit” (IPU) in Read more…

By Tiffany Trader

Fujitsu Continues HPC, AI Push

July 19, 2017

Summer is well under way, but the so-called summertime slowdown, linked with hot temperatures and longer vacations, does not seem to have impacted Fujitsu's out Read more…

By Tiffany Trader

Researchers Use DNA to Store and Retrieve Digital Movie

July 18, 2017

From abacus to pencil and paper to semiconductor chips, the technology of computing has always been an ever-changing target. The human brain is probably the com Read more…

By John Russell

The Exascale FY18 Budget – The Next Step

July 17, 2017

On July 12, 2017, the U.S. federal budget for its Exascale Computing Initiative (ECI) took its next step forward. On that day, the full Appropriations Committee Read more…

By Alex R. Larzelere

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

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

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

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

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

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

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

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

Leading Solution Providers

How ‘Knights Mill’ Gets Its Deep Learning Flops

June 22, 2017

Intel, the subject of much speculation regarding the delayed, rewritten or potentially canceled “Aurora” contract (the Argonne Lab part of the CORAL “ Read more…

By Tiffany Trader

Reinders: “AVX-512 May Be a Hidden Gem” in Intel Xeon Scalable Processors

June 29, 2017

Imagine if we could use vector processing on something other than just floating point problems.  Today, GPUs and CPUs work tirelessly to accelerate algorithms Read more…

By James Reinders

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 cryptocurrencies like Bitcoin, along with classified government communications and other sensitive digital transfers. Read more…

By Doug Black

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

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

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 Read more…

By Alex Woodie

Six Exascale PathForward Vendors Selected; DoE Providing $258M

June 15, 2017

The much-anticipated PathForward awards for hardware R&D in support of the Exascale Computing Project were announced today with six vendors selected – AMD Read more…

By John Russell

Top500 Results: Latest List Trends and What’s in Store

June 19, 2017

Greetings from Frankfurt and the 2017 International Supercomputing Conference where the latest Top500 list has just been revealed. Although there were no major Read more…

By Tiffany Trader

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