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!

What’s New in HPC Research: September (Part 1)

September 18, 2018

In this new bimonthly feature, HPCwire will highlight newly published research in the high-performance computing community and related domains. From exascale to quantum computing, the details are here. Check back every Read more…

By Oliver Peckham

House Passes $1.275B National Quantum Initiative

September 17, 2018

Last Thursday the U.S. House of Representatives passed the National Quantum Initiative Act (NQIA) intended to accelerate quantum computing research and development. Among other things it would establish a National Quantu Read more…

By John Russell

Nvidia Accelerates AI Inference in the Datacenter with T4 GPU

September 14, 2018

Nvidia is upping its game for AI inference in the datacenter with a new platform consisting of an inference accelerator chip--the new Turing-based Tesla T4 GPU--and a refresh of its inference server software packaged as Read more…

By George Leopold

HPE Extreme Performance Solutions

Introducing the First Integrated System Management Software for HPC Clusters from HPE

How do you manage your complex, growing cluster environments? Answer that big challenge with the new HPC cluster management solution: HPE Performance Cluster Manager. Read more…

IBM Accelerated Insights

A Crystal Ball for HPC

People are notoriously bad at predicting the future.  This very much includes experts. In the Forbes article “Why Most Predictions Are So Bad” Philip Tetlock discusses the largest and best-known test of the accuracy of expert predictions which show that any experts would do better if they make random guesses. Read more…

NSF Highlights Expanded Efforts for Broadening Participation in Computing

September 13, 2018

Today, the Directorate of Computer and Information Science and Engineering (CISE) of the NSF released a letter highlighting the expansion of its broadening participation in computing efforts. The letter was penned by Jam Read more…

By Staff

House Passes $1.275B National Quantum Initiative

September 17, 2018

Last Thursday the U.S. House of Representatives passed the National Quantum Initiative Act (NQIA) intended to accelerate quantum computing research and developm Read more…

By John Russell

Nvidia Accelerates AI Inference in the Datacenter with T4 GPU

September 14, 2018

Nvidia is upping its game for AI inference in the datacenter with a new platform consisting of an inference accelerator chip--the new Turing-based Tesla T4 GPU- Read more…

By George Leopold

DeepSense Combines HPC and AI to Bolster Canada’s Ocean Economy

September 13, 2018

We often hear scientists say that we know less than 10 percent of the life of the oceans. This week, IBM and a group of Canadian industry and government partner Read more…

By Tiffany Trader

Rigetti (and Others) Pursuit of Quantum Advantage

September 11, 2018

Remember ‘quantum supremacy’, the much-touted but little-loved idea that the age of quantum computing would be signaled when quantum computers could tackle Read more…

By John Russell

How FPGAs Accelerate Financial Services Workloads

September 11, 2018

While FSI companies are unlikely, for competitive reasons, to disclose their FPGA strategies, James Reinders offers insights into the case for FPGAs as accelerators for FSI by discussing performance, power, size, latency, jitter and inline processing. Read more…

By James Reinders

Update from Gregory Kurtzer on Singularity’s Push into FS and the Enterprise

September 11, 2018

Container technology is hardly new but it has undergone rapid evolution in the HPC space in recent years to accommodate traditional science workloads and HPC systems requirements. While Docker containers continue to dominate in the enterprise, other variants are becoming important and one alternative with distinctly HPC roots – Singularity – is making an enterprise push targeting advanced scale workload inclusive of HPC. Read more…

By John Russell

At HPC on Wall Street: AI-as-a-Service Accelerates AI Journeys

September 10, 2018

AIaaS – artificial intelligence-as-a-service – is the technology discipline that eases enterprise entry into the mysteries of the AI journey while lowering Read more…

By Doug Black

No Go for GloFo at 7nm; and the Fujitsu A64FX post-K CPU

September 5, 2018

It’s been a news worthy couple of weeks in the semiconductor and HPC industry. There were several HPC relevant disclosures at Hot Chips 2018 to whet appetites Read more…

By Dairsie Latimer

TACC Wins Next NSF-funded Major Supercomputer

July 30, 2018

The Texas Advanced Computing Center (TACC) has won the next NSF-funded big supercomputer beating out rivals including the National Center for Supercomputing Ap Read more…

By John Russell

IBM at Hot Chips: What’s Next for Power

August 23, 2018

With processor, memory and networking technologies all racing to fill in for an ailing Moore’s law, the era of the heterogeneous datacenter is well underway, Read more…

By Tiffany Trader

Requiem for a Phi: Knights Landing Discontinued

July 25, 2018

On Monday, Intel made public its end of life strategy for the Knights Landing "KNL" Phi product set. The announcement makes official what has already been wide Read more…

By Tiffany Trader

CERN Project Sees Orders-of-Magnitude Speedup with AI Approach

August 14, 2018

An award-winning effort at CERN has demonstrated potential to significantly change how the physics based modeling and simulation communities view machine learni Read more…

By Rob Farber

ORNL Summit Supercomputer Is Officially Here

June 8, 2018

Oak Ridge National Laboratory (ORNL) together with IBM and Nvidia celebrated the official unveiling of the Department of Energy (DOE) Summit supercomputer toda Read more…

By Tiffany Trader

New Deep Learning Algorithm Solves Rubik’s Cube

July 25, 2018

Solving (and attempting to solve) Rubik’s Cube has delighted millions of puzzle lovers since 1974 when the cube was invented by Hungarian sculptor and archite Read more…

By John Russell

AMD’s EPYC Road to Redemption in Six Slides

June 21, 2018

A year ago AMD returned to the server market with its EPYC processor line. The earth didn’t tremble but folks took notice. People remember the Opteron fondly Read more…

By John Russell

MLPerf – Will New Machine Learning Benchmark Help Propel AI Forward?

May 2, 2018

Let the AI benchmarking wars begin. Today, a diverse group from academia and industry – Google, Baidu, Intel, AMD, Harvard, and Stanford among them – releas Read more…

By John Russell

Leading Solution Providers

SC17 Booth Video Tours Playlist

Altair @ SC17

Altair

AMD @ SC17

AMD

ASRock Rack @ SC17

ASRock Rack

CEJN @ SC17

CEJN

DDN Storage @ SC17

DDN Storage

Huawei @ SC17

Huawei

IBM @ SC17

IBM

IBM Power Systems @ SC17

IBM Power Systems

Intel @ SC17

Intel

Lenovo @ SC17

Lenovo

Mellanox Technologies @ SC17

Mellanox Technologies

Microsoft @ SC17

Microsoft

Penguin Computing @ SC17

Penguin Computing

Pure Storage @ SC17

Pure Storage

Supericro @ SC17

Supericro

Tyan @ SC17

Tyan

Univa @ SC17

Univa

Pattern Computer – Startup Claims Breakthrough in ‘Pattern Discovery’ Technology

May 23, 2018

If it weren’t for the heavy-hitter technology team behind start-up Pattern Computer, which emerged from stealth today in a live-streamed event from San Franci Read more…

By John Russell

Sandia to Take Delivery of World’s Largest Arm System

June 18, 2018

While the enterprise remains circumspect on prospects for Arm servers in the datacenter, the leadership HPC community is taking a bolder, brighter view of the x86 server CPU alternative. Amongst current and planned Arm HPC installations – i.e., the innovative Mont-Blanc project, led by Bull/Atos, the 'Isambard’ Cray XC50 going into the University of Bristol, and commitments from both Japan and France among others -- HPE is announcing that it will be supply the United States National Nuclear Security Administration (NNSA) with a 2.3 petaflops peak Arm-based system, named Astra. Read more…

By Tiffany Trader

D-Wave Breaks New Ground in Quantum Simulation

July 16, 2018

Last Friday D-Wave scientists and colleagues published work in Science which they say represents the first fulfillment of Richard Feynman’s 1982 notion that Read more…

By John Russell

Intel Pledges First Commercial Nervana Product ‘Spring Crest’ in 2019

May 24, 2018

At its AI developer conference in San Francisco yesterday, Intel embraced a holistic approach to AI and showed off a broad AI portfolio that includes Xeon processors, Movidius technologies, FPGAs and Intel’s Nervana Neural Network Processors (NNPs), based on the technology it acquired in 2016. Read more…

By Tiffany Trader

Intel Announces Cooper Lake, Advances AI Strategy

August 9, 2018

Intel's chief datacenter exec Navin Shenoy kicked off the company's Data-Centric Innovation Summit Wednesday, the day-long program devoted to Intel's datacenter Read more…

By Tiffany Trader

TACC’s ‘Frontera’ Supercomputer Expands Horizon for Extreme-Scale Science

August 29, 2018

The National Science Foundation and the Texas Advanced Computing Center announced today that a new system, called Frontera, will overtake Stampede 2 as the fast Read more…

By Tiffany Trader

GPUs Power Five of World’s Top Seven Supercomputers

June 25, 2018

The top 10 echelon of the newly minted Top500 list boasts three powerful new systems with one common engine: the Nvidia Volta V100 general-purpose graphics proc Read more…

By Tiffany Trader

The Machine Learning Hype Cycle and HPC

June 14, 2018

Like many other HPC professionals I’m following the hype cycle around machine learning/deep learning with interest. I subscribe to the view that we’re probably approaching the ‘peak of inflated expectation’ but not quite yet starting the descent into the ‘trough of disillusionment. This still raises the probability that... Read more…

By Dairsie Latimer

  • arrow
  • Click Here for More Headlines
  • arrow
Do NOT follow this link or you will be banned from the site!
Share This