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!

Advancing Modular Supercomputing with DEEP and DEEP-ER Architectures

February 24, 2017

Knowing that the jump to exascale will require novel architectural approaches capable of delivering dramatic efficiency and performance gains, researchers around the world are hard at work on next-generation HPC systems. Read more…

By Sean Thielen

Weekly Twitter Roundup (Feb. 23, 2017)

February 23, 2017

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

HPE Server Shows Low Latency on STAC-N1 Test

February 22, 2017

The performance of trade and match servers can be a critical differentiator for financial trading houses. Read more…

By John Russell

HPC Financial Update (Feb. 2017)

February 22, 2017

In this recurring feature, we’ll provide you with financial highlights from companies in the HPC industry. Check back in regularly for an updated list with the most pertinent fiscal information. Read more…

By Thomas Ayres

HPE Extreme Performance Solutions

Manufacturers Reaping the Benefits of Remote Visualization

Today’s manufacturers are operating in an ever-changing atmosphere, and finding new ways to boost productivity has never been more vital.

This is why manufacturers are ramping up their investments in high performance computing (HPC), a trend which has helped give rise to the “connected factory” and Industrial Internet of Things (IIoT) concepts that are proliferating throughout the industry today. Read more…

Rethinking HPC Platforms for ‘Second Gen’ Applications

February 22, 2017

Just what constitutes HPC and how best to support it is a keen topic currently. Read more…

By John Russell

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

IDC: Will the Real Exascale Race Please Stand Up?

February 21, 2017

So the exascale race is on. And lots of organizations are in the pack. Government announcements from the US, China, India, Japan, and the EU indicate that they are working hard to make it happen – some sooner, some later. Read more…

By Bob Sorensen, IDC

ExxonMobil, NCSA, Cray Scale Reservoir Simulation to 700,000+ Processors

February 17, 2017

In a scaling breakthrough for oil and gas discovery, ExxonMobil geoscientists report they have harnessed the power of 717,000 processors – the equivalent of 22,000 32-processor computers – to run complex oil and gas reservoir simulation models. Read more…

By Doug Black

Advancing Modular Supercomputing with DEEP and DEEP-ER Architectures

February 24, 2017

Knowing that the jump to exascale will require novel architectural approaches capable of delivering dramatic efficiency and performance gains, researchers around the world are hard at work on next-generation HPC systems. Read more…

By Sean Thielen

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

IDC: Will the Real Exascale Race Please Stand Up?

February 21, 2017

So the exascale race is on. And lots of organizations are in the pack. Government announcements from the US, China, India, Japan, and the EU indicate that they are working hard to make it happen – some sooner, some later. Read more…

By Bob Sorensen, IDC

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

Drug Developers Use Google Cloud HPC in the Fight Against ALS

February 16, 2017

Within the haystack of a lethal disease such as ALS (amyotrophic lateral sclerosis / Lou Gehrig’s Disease) there exists, somewhere, the needle that will pierce this therapy-resistant affliction. Read more…

By Doug Black

Azure Edges AWS in Linpack Benchmark Study

February 15, 2017

The “when will clouds be ready for HPC” question has ebbed and flowed for years. Read more…

By John Russell

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

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

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

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

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

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

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

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

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

Leading Solution Providers

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

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

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

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

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

Dell Knights Landing Machine Sets New STAC Records

November 2, 2016

The Securities Technology Analysis Center, commonly known as STAC, has released a new report characterizing the performance of the Knight Landing-based Dell PowerEdge C6320p server on the STAC-A2 benchmarking suite, widely used by the financial services industry to test and evaluate computing platforms. The Dell machine has set new records for both the baseline Greeks benchmark and the large Greeks benchmark. 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 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

What Knights Landing Is Not

June 18, 2016

As we get ready to launch the newest member of the Intel Xeon Phi family, code named Knights Landing, it is natural that there be some questions and potentially some confusion. Read more…

By James Reinders, Intel

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