XPRESS Route to Exascale

By Nicole Hemsoth

February 28, 2013

In the world of supercomputers, where the top machines can cost upwards of $100 million, $1.1 million may not sound like much. To Thomas Sterling, chief scientist at the Center for Research in Extreme Scale Computing (CREST) at Indiana University, it’s a sum that will go a long way toward funding his favorite project.

The Department of Energy awarded that amount to Indiana University’s CREST last week to fund three years of work on the XPRESS (eXascale Programming Environment and System Software) project. CREST is collaborating with work going on simultaneously at Sandia National Laboratories and several other universities and research labs. The overall goal is to enable the creation of exascale computers.

HPCwire caught up with Sterling to discuss his role in the endeavor and what it means. As usual, he has some bold and controversial opinions on the future of supercomputing.

XPRESS, based on the ParalleX parallel computation model, is being designed to enable highly parallel processing. Collectively, the work being coordinated at Sandia, according to Sterling, represents “the single most important program in high performance computing that there is.”

CREST Team
From left to right: Executive Associate Director Thomas Sterling, Director Andrew Lumsdaine, and Associate Director of Strategy Craig Stewart

Sterling’s team at CREST, which is not yet 18 months old, is working on a unique new type of runtime environment, a dynamic system that will enable the software to automatically reallocate compute tasks over time. It will be self-correcting; detecting when processor cores are sitting idle and assigning them new tasks on the fly rather than sticking to the routines established by the human programmer, the compiler and the load time system. When it detects idle processors, it should be able to make adjustments on the order of a millisecond, or even a microsecond.

Sterling believes such a system could provide a dramatic improvement in the efficiency of supercomputers. Benchmarks such as Linpack or Highly Parallel Linpack don’t always represent the real world. When even the most powerful supercomputers are running real and very complex applications, such as multi-scale, multi-physics applications, the efficiency may be as high as 70% or as low as 3%, he says. “You’ll see that the efficiencies are often well below 10%,” he adds. “You find yourself throwing away 90% of the computer.”

Sterling acknowledges that others disagree with his approach. While other prominent research teams are working on improving the popular MPI (Message Passing Interface) to create a parallel processing system, Sterling has doubts about how far that kind of work can go. While a programmer can divide tasks among many different cores with MPI, the gains are limited because each task takes a different amount of time to complete. That requires setting up global barriers that keep each core from moving on to the next task until all the other cores have completed their tasks. A lot of cores, therefore, are sitting idle at any one time.

That works fine for many HPC programs; those in which the tasks are regular, even and coarse-grained, he says. There are plenty of such tasks in HPC, and MPI has been a big success as a result. But Sterling believes it is no longer sufficient to usher in the era of exascale. Complex scientific calculations are usually highly non-linear and the processing time of different cores can vary dramatically.

Next >>

Sterling believes that it’s not always necessary to use global barriers. Not every core needs to wait for all the tasks on all the other cores to finish. The cores that finish last are the ones that need the data from all the other cores before finishing their own tasks Every other core sits idly by to wait for them to catch up and release the barrier. The idle cores could be working on new tasks if they weren’t held back by the barrier, waiting for the last cores, whose data they don’t need, to catch up.

An example comes from climate modeling, where the researcher is studying changes in temperature over the ocean. The model has to take into account a lot of different variables, such as energy and mass transfer, different chemistries in the ocean, solar radiation, and the transfer of energy from the boundary areas of the water. It also has to take into account highly irregular coastlines, islands, or the distribution of ice. But when studying a cross-section of a grid over the ocean, it’s not necessary to wait for all the calculations to be completed for every section of the grid before moving on to the next task. One grid in the middle of the Atlantic is only going to be affected by areas within tens or hundreds of kilometers, not by sections in the Pacific. Some of the calculations do not need to wait for the entire set of processors to finish.

The problem is that it’s virtually impossible for the programmer to figure out in advance all the permutations of tasks and cores that would move things along more rapidly. That’s where the ParalleX execution model comes in. Dynamic modeling means that the system can automatically detect when tasks are finished and cores are sitting idle. It can then assign new tasks to those cores. Everything still needs to be synchronized at certain points, but ParalleX sets up many smaller barriers rather than one global barrier.

Sterling has a lot of confidence in the work at CREST, which is devising a new software stack that will insert an XPRESS layer into the X-Stack system. But just creating a plug-in stack layer is not sufficient. CREST’s work is being done in conjunction with Sandia’s light weight kernel operating system, integrating them tightly together. “We’re able to redefine the OS and the runtime system jointly, which creates a whole new protocol, a whole new relationship between those two pieces of software,” he says.

Where his work goes further than other efforts as parallelism, he adds, is moving beyond an ad-hoc approach to an integrated system.

“We redefine the execution model so these things stop being hacks, stop being patches and they start being something of a comprehensive or a coherent, complete paradigm,” he says. “We feel it’s very important that everything be designed within the context and scope of everything else so it all makes sense. That will create a whole new ability to dialog between the two software layers.”

How much improvement can this approach offer? Theoretically the combined project could increase efficiency by a factor of 20. So far, his tests have managed to increase efficiency by a factor of two.

Might it be better to just figure out how to evolve MPI to do the same kind of thing? Sterling acknowledges that it might, but ultimately he doubts if that approach will be able to make the leap forward in parallelism that’s needed. He compares it to punctuated equilibrium in evolutionary biology. Evolution is not always gradual change; sometimes it encounters a rapidly changing environment and must adapt quickly.

Sterling believes we’re at such a point today. “It’s not just because of big data, although that’s the big thing right now,” he says. More importantly, he says, the big need is for dynamic graph structures. Climate modeling, for example, is a hugely complex problem that requires more than a two-dimensional approach. Accounting for hurricanes and other phenomena in oceans requires a z-axis. Industrial design, microbiology, and controlled fusion are also deep, highly non-linear problems that need solving with dynamic graphs. This kind of parallelism is key to the future of HPC, not just for number crunching, he says, but for “HPC symbolic information, which means knowledge management and understanding by machines.”

While the overall program is officially dedicated to creating exascale computing, Sterling believes it could prove its importance much sooner than that. He refers to the need for “extreme scale” computing, not exascale, which is an arbitrary benchmark. A lot of progress can be made along the way. Getting to exascale represents in increase in compute power of two orders of magnitude from today’s best supercomputers. But one order of magnitude or less would go a long way to improving materials science, industrial design, microbiology and what he sees as the most important need for the 21st century, controlled fusion. Supercomputers are already showing limitations for some of the kinds of scientific programming people want to do.

“You don’t have to wait until the end of the decade to worry about exascale,” he says. “The challenge is today, not some far future challenge. We are losing today and we need new methods today.”

He believes he has a good chance of meeting that challenge. And that makes him very happy. “There will be nothing like it,” he says. “I find it very exciting.”

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!

Long Flights to Cluster Fights: Meet the Asian Student Cluster Teams

November 22, 2017

Five teams from Asia traveled thousands of miles to compete at the SC17 Student Cluster Competition in Denver. Our cameras were there to meet ‘em, greet ‘em, and grill ‘em about their clusters and how they’re doi Read more…

By Dan Olds

Japan Unveils Quantum Neural Network

November 22, 2017

The U.S. and China are leading the race toward productive quantum computing, but it's early enough that ultimate leadership is still something of an open question. The latest geo-region to throw its hat in the quantum co Read more…

By Tiffany Trader

Perspective: What Really Happened at SC17?

November 22, 2017

SC is over. Now comes the myriad of follow-ups. Inboxes are filled with templated emails from vendors and other exhibitors hoping to win a place in the post-SC thinking of booth visitors. Attendees of tutorials, workshop Read more…

By Andrew Jones

HPE Extreme Performance Solutions

HPE Wins “Best HPC Server” for the Apollo 6000 Gen10 System

Hewlett Packard Enterprise (HPE) was nominated for 14 HPCwire Readers’ and Editors’ Choice Awards—including “Best High Performance Computing (HPC) Server Product or Technology” and “Top Supercomputing Achievement.” The HPE Apollo 6000 Gen10 was named “Best HPC Server” of 2017. Read more…

Turnaround Complete, HPE’s Whitman Departs

November 22, 2017

Having turned around the aircraft carrier the Silicon Valley icon had become, Meg Whitman is leaving the helm of a restructured Hewlett Packard. Her successor, technologist Antonio Neri will now guide what Whitman assert Read more…

By George Leopold

Long Flights to Cluster Fights: Meet the Asian Student Cluster Teams

November 22, 2017

Five teams from Asia traveled thousands of miles to compete at the SC17 Student Cluster Competition in Denver. Our cameras were there to meet ‘em, greet ‘em Read more…

By Dan Olds

Perspective: What Really Happened at SC17?

November 22, 2017

SC is over. Now comes the myriad of follow-ups. Inboxes are filled with templated emails from vendors and other exhibitors hoping to win a place in the post-SC Read more…

By Andrew Jones

SC Bids Farewell to Denver, Heads to Dallas for 30th Anniversary

November 17, 2017

After a jam-packed four-day expo and intensive six-day technical program, SC17 has wrapped up another successful event that brought together nearly 13,000 visit Read more…

By Tiffany Trader

SC17 Keynote – HPC Powers SKA Efforts to Peer Deep into the Cosmos

November 17, 2017

This week’s SC17 keynote – Life, the Universe and Computing: The Story of the SKA Telescope – was a powerful pitch for the potential of Big Science projects that also showcased the foundational role of high performance computing in modern science. It was also visually stunning. Read more…

By John Russell

How Cities Use HPC at the Edge to Get Smarter

November 17, 2017

Cities are sensoring up, collecting vast troves of data that they’re running through predictive models and using the insights to solve problems that, in some Read more…

By Doug Black

Student Cluster LINPACK Record Shattered! More LINs Packed Than Ever before!

November 16, 2017

Nanyang Technological University, the pride of Singapore, utterly destroyed the Student Cluster Competition LINPACK record by posting a score of 51.77 TFlop/s a Read more…

By Dan Olds

Hyperion Market Update: ‘Decent’ Growth Led by HPE; AI Transparency a Risk Issue

November 15, 2017

The HPC market update from Hyperion Research (formerly IDC) at the annual SC conference is a business and social “must,” and this year’s presentation at S Read more…

By Doug Black

Nvidia Focuses Its Cloud Containers on HPC Applications

November 14, 2017

Having migrated its top-of-the-line datacenter GPU to the largest cloud vendors, Nvidia is touting its Volta architecture for a range of scientific computing ta Read more…

By George Leopold

US Coalesces Plans for First Exascale Supercomputer: Aurora in 2021

September 27, 2017

At the Advanced Scientific Computing Advisory Committee (ASCAC) meeting, in Arlington, Va., yesterday (Sept. 26), it was revealed that the "Aurora" supercompute Read more…

By Tiffany Trader

NERSC Scales Scientific Deep Learning to 15 Petaflops

August 28, 2017

A collaborative effort between Intel, NERSC and Stanford has delivered the first 15-petaflops deep learning software running on HPC platforms and is, according Read more…

By Rob Farber

Oracle Layoffs Reportedly Hit SPARC and Solaris Hard

September 7, 2017

Oracle’s latest layoffs have many wondering if this is the end of the line for the SPARC processor and Solaris OS development. As reported by multiple sources Read more…

By John Russell

AMD Showcases Growing Portfolio of EPYC and Radeon-based Systems at SC17

November 13, 2017

AMD’s charge back into HPC and the datacenter is on full display at SC17. Having launched the EPYC processor line in June along with its MI25 GPU the focus he Read more…

By John Russell

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

Google Releases Deeplearn.js to Further Democratize Machine Learning

August 17, 2017

Spreading the use of machine learning tools is one of the goals of Google’s PAIR (People + AI Research) initiative, which was introduced in early July. Last w Read more…

By John Russell

GlobalFoundries Puts Wind in AMD’s Sails with 12nm FinFET

September 24, 2017

From its annual tech conference last week (Sept. 20), where GlobalFoundries welcomed more than 600 semiconductor professionals (reaching the Santa Clara venue Read more…

By Tiffany Trader

Amazon Debuts New AMD-based GPU Instances for Graphics Acceleration

September 12, 2017

Last week Amazon Web Services (AWS) streaming service, AppStream 2.0, introduced a new GPU instance called Graphics Design intended to accelerate graphics. The Read more…

By John Russell

Leading Solution Providers

SC17 Booth Video Tours

EU Funds 20 Million Euro ARM+FPGA Exascale Project

September 7, 2017

At the Barcelona Supercomputer Centre on Wednesday (Sept. 6), 16 partners gathered to launch the EuroEXA project, which invests €20 million over three-and-a-half years into exascale-focused research and development. Led by the Horizon 2020 program, EuroEXA picks up the banner of a triad of partner projects — ExaNeSt, EcoScale and ExaNoDe — building on their work... Read more…

By Tiffany Trader

Delays, Smoke, Records & Markets – A Candid Conversation with Cray CEO Peter Ungaro

October 5, 2017

Earlier this month, Tom Tabor, publisher of HPCwire and I had a very personal conversation with Cray CEO Peter Ungaro. Cray has been on something of a Cinderell Read more…

By Tiffany Trader & Tom Tabor

Cray Moves to Acquire the Seagate ClusterStor Line

July 28, 2017

This week Cray announced that it is picking up Seagate's ClusterStor HPC storage array business for an undisclosed sum. "In short we're effectively transitioning the bulk of the ClusterStor product line to Cray," said CEO Peter Ungaro. 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

Intel Launches Software Tools to Ease FPGA Programming

September 5, 2017

Field Programmable Gate Arrays (FPGAs) have a reputation for being difficult to program, requiring expertise in specialty languages, like Verilog or VHDL. Easin Read more…

By Tiffany Trader

HPC Chips – A Veritable Smorgasbord?

October 10, 2017

For the first time since AMD's ill-fated launch of Bulldozer the answer to the question, 'Which CPU will be in my next HPC system?' doesn't have to be 'Whichever variety of Intel Xeon E5 they are selling when we procure'. Read more…

By Dairsie Latimer

Flipping the Flops and Reading the Top500 Tea Leaves

November 13, 2017

The 50th edition of the Top500 list, the biannual publication of the world’s fastest supercomputers based on public Linpack benchmarking results, was released Read more…

By Tiffany Trader

IBM Advances Web-based Quantum Programming

September 5, 2017

IBM Research is pairing its Jupyter-based Data Science Experience notebook environment with its cloud-based quantum computer, IBM Q, in hopes of encouraging a new class of entrepreneurial user to solve intractable problems that even exceed the capabilities of the best AI systems. Read more…

By Alex Woodie

Share This