Doug Kothe on the Race to Build Exascale Applications

By John Russell

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. No pressure. He’s not alone, of course. The U.S. Exascale Computing Project (ECP) is a complicated effort with many interrelated parts and contributors, all necessary for success. Yet Kothe’s job as director of application development is one of the more visible and daunting and perhaps best described by his boss, Paul Messina, ECP director.

“We think of 50 times [current] performance on applications [as the exascale measure of merit], unfortunately there’s a kink in this,” said Messina. “The kink is people won’t be running today’s jobs in these exascale systems. We want exascale systems to do things we can’t do today and we need to figure out a way to quantify that. In some cases it will be relatively easy – just achieving much greater resolutions – but in many cases it will be enabling additional physics to more faithfully represent the phenomena. We want to focus on measuring every capable exascale system based on full applications tackling real problems compared to what they can do today.”

Doug Kothe, ECP

In this wide-ranging discussion with HPCwire, Kothe touches on ECP application development goals and processes; several technical issues such as efforts to combine data analytics with mod/sim and the need for expanded software frameworks to accommodate exascale applications; and early thoughts for incorporating neuromorphic and quantum computing not currently part of the formal ECP plan. Interestingly, his biggest worry isn’t reaching the goal on schedule – he believes the application teams will get there – but post-ECP staff retention when industry comes calling.

By way of review, ECP is a collaborative effort of two Department of Energy organizations—the Office of Science and the National Nuclear Security Administration. Six applications areas have been singled out: national security; energy security, economic security, scientific discovery; earth science; and health care. In terms of app-dev, that’s translated into 21 Science & Energy application projects, 3 NNSA application projects, and 1 DOE / NIH application project (precision medicine for cancer).

It’s not yet clear what the just released FY2018 U.S. Budget proposed by the Trump Administration portends. Funding for science programs were cut nearly across the board although ECP escaped. Kothe says simply, “It is the beginning of the process for the FY18 budget, and while the overall budget is determined, we will continue working on the applications that are already part of the ECP.”

In keeping with ECP’s broad ambitions, Kothe says, “All of our applications teams are focused on very specific challenge problems and by our definition a challenge problem is one that is intractable today, needs exascale resources, and is a strategic high priority for one of the DOE program offices. We aren’t claiming we are going to solve all the problems but we are claiming is simulation technology that can address the problem. The point is we have the applications vectored in rather specific directions.” (Summary list below, click to enlarge)

 

RISE OF DATA ANALYTICS
One of the more exciting and new-to-HPC areas is incorporation of data analytics into the HPC environment overall and ECP in particular. Indeed, harmonizing or at least integrating the big data and modelling and simulation is a goal specified by the National Strategic Computing Initiative. Data-driven science isn’t new nor is researcher familiarity with underlying statistics. But the sudden rise machine/deep learning techniques and including many that rely on lower precision calculations is somewhat new to the scientific computing community and an area where the commercial world has perhaps taken the lead. Kothe labels the topic “white hot”.

“Not being trained in the data analytics area I’ve been doing a lot of reading and talking [to others]. A large fraction of the area I feel like I know, but I didn’t appreciate the other 20 or 30 percent. The point is by exposing our applications teams to the data analytics community, even just calling libraries, we are going to see some interesting in situ and computational steering use cases. As an example of in situ, think of turbulence. It could be an LES (large eddy simulation) whose parameters could have been tuned a priori by machine learning or chosen on the fly by machine learning. That kind of work is already going on at some universities,” Kothe says.

Climate modeling is a case point. “A big challenge is subgrid models for clouds. Right now and even at exascale we probably cannot do one km or less resolution everywhere. We may be able to do regional coupled simulations that way, but if we try to do five or ten kilometers everywhere – of course it will vary whether over ocean or land ice, sea ice, or atmosphere – you will still have many clouds lost in one cell. You need a subgrid model. Maybe machine learning could be used to select the parameters. Think of a bunch of little LES models running in a 10km x10km cell holding lots of clouds that are then scaled into the higher level physics. I think subgrid models are potentially a poster child for machine learning.”

Steering simulations is another emerging use case. “There’s a couple of labs, Lawrence Livermore in particular, that are already using machine learning to make decisions, to automate decisions about mesh quality for fluid and structure simulations where the mesh is just flowing with the moving material and the mesh may start to contort in a way that will cause the numerical solution to break down or errors to increase. You could do quality checks on the fly and correct the mesh with machine learning.”

One interesting use is being explored as part of the Exascale CANcer Distributed Learning Environment (CANDLE) project (see HPCwire article, Enlisting Deep Learning in the War on Cancer). Part of the project is clarifying the RAS (gene) network activity. The RAS network is implicated very many cancers. “You have machine learning orchestrating ensembles of molecular dynamics simulations [looking at docking scenarios with the RAS protein] and examining factors that are involved in docking,” says Kothe. Machine learning can recognize already known areas and reduce need for computationally intensive simulation in those areas while zeroing in on lesser known areas for intense quantum chemistry simulations. Think of it as zooming in and out as needed.

 

FRAMEWORKS REVISITED
Clearly there’s no shortage of challenges for ECP application development. Kothe cites optimizing node performance and memory management among the especially thorny ones, “We’ve now have many levels of memory exposed to us. We don’t really quite know how best to use it.” Data structure choices can also be problematic and Kothe suggests frameworks may undergo a revival,

One of the application teams (astrophysics), recalls Kothe, came to him and said, “I am afraid to make a choice for a data structure that would be pervasive in my whole code because it might be the wrong one and I’m stuck with it.'” The point is I think what we are seeing with the applications a kind of ‘going back to the future’ in late 80s when you saw lots of heavyweight frameworks where an application would call out to a black box and say register this array for me and hand me back the pointer.

“That’s good and it’s bad. The bad part is you’re losing control and now you have to schlep around this black box and you don’t know if it is going to do what you want it to do. The good part is if you are on a KNL system or an NVIDIA system, you are on different nodes, and that block box memory manager would have been tuned for that hardware. [In] dealing with memory hierarchy risks, I think we are probably seeing applications move more towards frameworks which I find think is a good idea. We’ve learned kind of what I call the big F or little f frameworks. I think we’re learning how to balance the two so applications can be portable and not have to rely on an army of people but still do something that’s more agile than just choose one data structure and hope it works.”

Performance portability is naturally a major consideration. Historically, says Kothe, application developers and he includes himself in the category, “We chose portability over performance because we want to make sure our science can be done anywhere. Performance can’t be an afterthought but it often is. Portability in my mind has several dimensions. So the new system shows up and it is probably not something out of left field, you know something about it, but what’s a reasonable amount of effort that you think should be required to port your code? How much of the code base do you think should change? What is correctness in terms of the problem and getting the answer.

“I would claim that a 64-bit comparison is probably not realistic. I mean it’s probably not even appropriate. What set of problems would you run? You need to run real problems. We’re asking each app team to define what they think portability means and hope that collectively we’ll move towards a good definition and a good target for all the apps but I think it will end up being fairly app specific.”

THE CO-DESIGN IMPERATIVE
The necessity of co-design has become a given throughout HPC as well as with the ECP. Advancing hardware and new systems architectures must be taken into account not merely to push application performance but to get them to run at all. However coupling software too tightly to a specific machine or architecture is limiting. Currently ECP has established six co-design centers to help deal with specific challenges. Kothe believes use of motifs may help.

“Every application team at some level will be doing some vertically integrated co-design and there is probably more software co-design going on – the interplay with the compilers and runtime systems and that kind of thing – than anything else. By having the co-design centers identify a small number of motifs that applications are using, I think we can leverage a deep dive co-design on the motifs as opposed to doing kind of an extensive co-design vertically integrated within every application. This is new and there are some risks. But long term, my dream would be we [develop] community libraries that are co-designed around motifs that are used broadly among the applications.

“The poster child is probably [handling] particles. Almost every application has a discrete particle model for something and that’s good and it’s a challenge. So how do you encapsulate the particle [model] in a way that it can be co-designed not as a separate activity that’s not thinking about the [specific] consumer of that motif, but just thinking about making that motif rock and roll. That’s the challenge, to co-design motifs so they can be broadly used and I have high hopes there.”

 

 

STAY ON TARGET
“A big challenge with application developers, is everything sounds cool and looks good, so we want to keep them focused. Year by year the applications have laid out a number of milestones and for the most parts the milestones are step by step progression towards that challenge program. The progression has many dimensions: is the science capability improving, better physics, better algorithms; is the team utilizing the hardware efficiently [such as] state of the art test beds, the latest systems on the floor; are they integrating software technologies and probably one of the most important is they are using co-design efforts,” says Kothe

One ECP-wide tool is a comprehensive project database where “all the R&D projects and applications and software technology, all their plans and milestones are in one place.” A key aspect of ECP, says Kothe, is that everyone can see what everyone else is doing and how they are progressing.

Think of a milestone as a handful of things, says Kothe, that are generally tangible such as software release or a demonstration simulation. “It could be a report or a presentation. It can even be a small write up that says I tried this algorithm and it didn’t work. A milestone is a decision point.

“It’s not always a huge success. Failure can be just as valuable. Sometimes we can force a sense of urgency. We can review this seven-year plan and say, alright you can’t bring in a technology that doesn’t have a line of sight in this timeframe, or you’ve got algorithm A and B going along [and] at this point you have make a decision and choose one and go with it. I like that. I think it imparts a sense of urgency,” Kothe.

Kothe, of course, has his own milestones. One is an annual application assessment report due every September.

“I am hearing I am a slave driver and I didn’t really think had that personality,” says Kothe. One area where he is inflexible is on scheduled releases. “We want you to release on the scheduled date, that date is gospel. What’s in the release may float. So the team and budget, we like to be pretty rigid, but what’s in the release floats based on what you have learned. You have this bag of tasks and try to get as many tasks done as you can but you still must have the release.”

Currently, the comprehensive database of projects isn’t publicly available (would be interesting reading) but Kothe says individual PIs are encouraged to share information widely.

SOFTWARE TECHNOLOGY SHARING
Not surprisingly, close collaboration with the software technology team is emphasized. “Right now what we have this incredible opportunity because applications teams are exposed to a lot of software technologies they’ve never seen or heard of.” It’s a bit like kids in a candy store says Kothe, “They are looking at this technology and saying I want to do that, to do that, to do that, and so the challenge for integration is on managing the interfaces and doing it in a scalable way.”

There a couple of technology projects that everyone wants to integrate, he says, and that’s big bandwidth worry when you have 20-plus application projects lined up saying “let me try your stuff because chances are there will be new APIs and new functionalities and bugs and features [too]. The software technology people are saying, ‘Doug be careful. let’s come up with a scalable process.’” Conversely, says Kothe, it is also true there’s a fair amount of great “software technology the application teams are not exploring which they should be.”

“We have defined a number of integration milestones which are basically milestones that require deliverables from two or three areas. We call that shared fate. [I know] it sounds like we are jumping off a cliff together. A good example is an application project looks at a linear solver and says ‘you don’t have the functionality I need, lets negotiate requirements.’ So the solver negotiates a new API, a new functionality, and the application team will have a milestone that says it will have integrated and tested and the new technology [by a given date] and the software technology team has to have its release say two or three months before. These things tend to be daisy chained like that. You have a release, then an integration assessment, and we might have another release to basically deal with any issues.

“Right now, early on in ECP, we’re having a lot of point-to-point interaction where there’s lots of aps that want to do lots of same or different things with lots of software projects. I think once we settle down on the requirements the software technologies will be kind of one to all [having] settled on a base functionality and a base API. An obvious example is MPI but even with MPI there’s new features and functionalities that certain aspects. We can’t take it for granted that some of these tremendous technologies like MPI are going to be there working the way we need for exascale,” says Kothe.

 

ECP FUTURE WATCH
Even as ECP pushes forward it remains rooted in CMOS technology yet there are several newer technologies – not least neuromorphic and quantum computing – which have made great strides recently and seem on the cusp of practical application.

“One of the things I have been thinking about is even if we don’t have access to a neuromorphic chip what is its behavior like from a hardware simulator point of view. The same thing with quantum computing. Our mindset has to change with regards to the algorithms we lay out for neuromorphic or quantum. The applications teams need to start thinking about different types of algorithms. As Paul [Messina] has pointed out it’s possible quantum computing could fairly soon become an accelerator on traditional node. Making sure applications are compartmentalized is important to make that possible. It would allow us to be more flexible and extensible and perhaps exploit something like a quantum accelerator.”

Looking ahead, says Kothe, he worries most about the unknown unknowns – there will be surprises. “I feel like right now in apps space we kind of have known unknowns and we’ll hit some unknown unknowns, but I believe we are going to have a number of applications ready to go. We’ll have trips along the way and we may not do some things we plan now. I think we have an aggressive but not naive set of metrics. It’s really the people. We have some unbelievable people,” he says.

One can understand today’s attraction. Kothe points out this is likely to be a once-in-a-career opportunity and the mix of experience among the application team members significant. “What we see is millennials sitting at the table showing people new ways of doing software with gray-haired guys like me who have been to the school of hard knocks. There’s a tremendous cross fertilization. I’m confident. I saw it when we selected these teams. We had teams with rosters that looked like the all star team, but I am worried about retention. We are training people to be some of the best, especially the early career folks, so I am worried that they will be in high demand, very marketable.”

Kothe Bio from ECP website:
Douglas B. Kothe (Doug) has over three decades of experience in conducting and leading applied R&D in computational applications designed to simulate complex physical phenomena in the energy, defense, and manufacturing sectors. Kothe is currently the Deputy Associate Laboratory Director of the Computing and Computational Sciences Directorate (CCSD) at Oak Ridge National Laboratory (ORNL). Prior positions for Kothe at ORNL, where he has been since 2006, were Director of the Consortium for Advanced Simulation of Light Water Reactors, DOE’s first Energy Innovation Hub (2010-2015), and Director of Science at the National Center for Computational Sciences (2006-2010).

Feature Caption:
The Transforming Additive Manufacturing through Exascale Simulation project (ExaAM) is building a new multi-physics modeling and simulation platform for 3D printing of metals to provide an up-front assessment of the manufacturability and performance of additively manufactured parts. Pictured: simulation of laser melting of metal powder in a 3D printing process (LLNL) and a fully functional lightweight robotic hand (ORNL).

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!

Simulating Car Crashes with Supercomputers – and Lego

October 18, 2019

It’s an experiment many of us have carried out at home: crashing two Lego creations into each other, bricks flying everywhere. But for the researchers at the General German Automobile Club (ADAC) – which is comparabl Read more…

By Oliver Peckham

NASA Uses Deep Learning to Monitor Solar Weather

October 17, 2019

Solar flares may be best-known as sci-fi MacGuffins, but those flares – and other space weather – can have serious impacts on not only spacecraft and satellites, but also on Earth-based systems such as radio communic Read more…

By Oliver Peckham

Federated Learning Applied to Cancer Research

October 17, 2019

The ability to share and analyze data while protecting patient privacy is giving medical researchers a new tool in their efforts to use what one vendor calls “federated learning” to train models based on diverse data Read more…

By George Leopold

Using AI to Solve One of the Most Prevailing Problems in CFD

October 17, 2019

How can artificial intelligence (AI) and high-performance computing (HPC) solve mesh generation, one of the most commonly referenced problems in computational engineering? A new study has set out to answer this question and create an industry-first AI-mesh application... Read more…

By James Sharpe

NSB 2020 S&E Indicators Dig into Workforce and Education

October 16, 2019

Every two years the National Science Board is required by Congress to issue a report on the state of science and engineering in the U.S. This year, in a departure from past practice, the NSB has divided the 2020 S&E Read more…

By John Russell

AWS Solution Channel

Making High Performance Computing Affordable and Accessible for Small and Medium Businesses with HPC on AWS

High performance computing (HPC) brings a powerful set of tools to a broad range of industries, helping to drive innovation and boost revenue in finance, genomics, oil and gas extraction, and other fields. Read more…

HPE Extreme Performance Solutions

Intel FPGAs: More Than Just an Accelerator Card

FPGA (Field Programmable Gate Array) acceleration cards are not new, as they’ve been commercially available since 1984. Typically, the emphasis around FPGAs has centered on the fact that they’re programmable accelerators, and that they can truly offer workload specific hardware acceleration solutions without requiring custom silicon. Read more…

IBM Accelerated Insights

How Do We Power the New Industrial Revolution?

[Attend the IBM LSF, HPC & AI User Group Meeting at SC19 in Denver on November 19!]

Almost everyone is talking about artificial intelligence (AI). Read more…

What’s New in HPC Research: Rabies, Smog, Robots & More

October 14, 2019

In this bimonthly feature, HPCwire highlights newly published research in the high-performance computing community and related domains. From parallel programming to exascale to quantum computing, the details are here. Read more…

By Oliver Peckham

Using AI to Solve One of the Most Prevailing Problems in CFD

October 17, 2019

How can artificial intelligence (AI) and high-performance computing (HPC) solve mesh generation, one of the most commonly referenced problems in computational engineering? A new study has set out to answer this question and create an industry-first AI-mesh application... Read more…

By James Sharpe

NSB 2020 S&E Indicators Dig into Workforce and Education

October 16, 2019

Every two years the National Science Board is required by Congress to issue a report on the state of science and engineering in the U.S. This year, in a departu Read more…

By John Russell

Crystal Ball Gazing: IBM’s Vision for the Future of Computing

October 14, 2019

Dario Gil, IBM’s relatively new director of research, painted a intriguing portrait of the future of computing along with a rough idea of how IBM thinks we’ Read more…

By John Russell

Summit Simulates Braking – on Mars

October 14, 2019

NASA is planning to send humans to Mars by the 2030s – and landing on the surface will be considerably trickier than landing a rover like Curiosity. To solve Read more…

By Staff report

Trovares Drives Memory-Driven, Property Graph Analytics Strategy with HPE

October 10, 2019

Trovares, a high performance property graph analytics company, has partnered with HPE and its Superdome Flex memory-driven servers on a cybersecurity capability the companies say “routinely” runs near-time workloads on 24TB-capacity systems... Read more…

By Doug Black

Intel, Lenovo Join Forces on HPC Cluster for Flatiron

October 9, 2019

An HPC cluster with deep learning techniques will be used to process petabytes of scientific data as part of workload-intensive projects spanning astrophysics to genomics. AI partners Intel and Lenovo said they are providing... Read more…

By George Leopold

Optimizing Offshore Wind Farms with Supercomputer Simulations

October 9, 2019

Offshore wind farms offer a number of benefits; many of the areas with the strongest winds are located offshore, and siting wind farms offshore ameliorates many of the land use concerns associated with onshore wind farms. Some estimates say that, if leveraged, offshore wind power... Read more…

By Oliver Peckham

Harvard Deploys Cannon, New Lenovo Water-Cooled HPC Cluster

October 9, 2019

Harvard's Faculty of Arts & Sciences Research Computing (FASRC) center announced a refresh of their primary HPC resource. The new cluster, called Cannon after the pioneering American astronomer Annie Jump Cannon, is supplied by Lenovo... Read more…

By Tiffany Trader

Supercomputer-Powered AI Tackles a Key Fusion Energy Challenge

August 7, 2019

Fusion energy is the Holy Grail of the energy world: low-radioactivity, low-waste, zero-carbon, high-output nuclear power that can run on hydrogen or lithium. T Read more…

By Oliver Peckham

DARPA Looks to Propel Parallelism

September 4, 2019

As Moore’s law runs out of steam, new programming approaches are being pursued with the goal of greater hardware performance with less coding. The Defense Advanced Projects Research Agency is launching a new programming effort aimed at leveraging the benefits of massive distributed parallelism with less sweat. Read more…

By George Leopold

Cray Wins NNSA-Livermore ‘El Capitan’ Exascale Contract

August 13, 2019

Cray has won the bid to build the first exascale supercomputer for the National Nuclear Security Administration (NNSA) and Lawrence Livermore National Laborator Read more…

By Tiffany Trader

AMD Launches Epyc Rome, First 7nm CPU

August 8, 2019

From a gala event at the Palace of Fine Arts in San Francisco yesterday (Aug. 7), AMD launched its second-generation Epyc Rome x86 chips, based on its 7nm proce Read more…

By Tiffany Trader

Ayar Labs to Demo Photonics Chiplet in FPGA Package at Hot Chips

August 19, 2019

Silicon startup Ayar Labs continues to gain momentum with its DARPA-backed optical chiplet technology that puts advanced electronics and optics on the same chip Read more…

By Tiffany Trader

Using AI to Solve One of the Most Prevailing Problems in CFD

October 17, 2019

How can artificial intelligence (AI) and high-performance computing (HPC) solve mesh generation, one of the most commonly referenced problems in computational engineering? A new study has set out to answer this question and create an industry-first AI-mesh application... Read more…

By James Sharpe

D-Wave’s Path to 5000 Qubits; Google’s Quantum Supremacy Claim

September 24, 2019

On the heels of IBM’s quantum news last week come two more quantum items. D-Wave Systems today announced the name of its forthcoming 5000-qubit system, Advantage (yes the name choice isn’t serendipity), at its user conference being held this week in Newport, RI. Read more…

By John Russell

Chinese Company Sugon Placed on US ‘Entity List’ After Strong Showing at International Supercomputing Conference

June 26, 2019

After more than a decade of advancing its supercomputing prowess, operating the world’s most powerful supercomputer from June 2013 to June 2018, China is keep Read more…

By Tiffany Trader

Leading Solution Providers

ISC 2019 Virtual Booth Video Tour

CRAY
CRAY
DDN
DDN
DELL EMC
DELL EMC
GOOGLE
GOOGLE
ONE STOP SYSTEMS
ONE STOP SYSTEMS
PANASAS
PANASAS
VERNE GLOBAL
VERNE GLOBAL

A Behind-the-Scenes Look at the Hardware That Powered the Black Hole Image

June 24, 2019

Two months ago, the first-ever image of a black hole took the internet by storm. A team of scientists took years to produce and verify the striking image – an Read more…

By Oliver Peckham

Intel Confirms Retreat on Omni-Path

August 1, 2019

Intel Corp.’s plans to make a big splash in the network fabric market for linking HPC and other workloads has apparently belly-flopped. The chipmaker confirmed to us the outlines of an earlier report by the website CRN that it has jettisoned plans for a second-generation version of its Omni-Path interconnect... Read more…

By Staff report

Crystal Ball Gazing: IBM’s Vision for the Future of Computing

October 14, 2019

Dario Gil, IBM’s relatively new director of research, painted a intriguing portrait of the future of computing along with a rough idea of how IBM thinks we’ Read more…

By John Russell

Kubernetes, Containers and HPC

September 19, 2019

Software containers and Kubernetes are important tools for building, deploying, running and managing modern enterprise applications at scale and delivering enterprise software faster and more reliably to the end user — while using resources more efficiently and reducing costs. Read more…

By Daniel Gruber, Burak Yenier and Wolfgang Gentzsch, UberCloud

Intel Debuts Pohoiki Beach, Its 8M Neuron Neuromorphic Development System

July 17, 2019

Neuromorphic computing has received less fanfare of late than quantum computing whose mystery has captured public attention and which seems to have generated mo Read more…

By John Russell

Rise of NIH’s Biowulf Mirrors the Rise of Computational Biology

July 29, 2019

The story of NIH’s supercomputer Biowulf is fascinating, important, and in many ways representative of the transformation of life sciences and biomedical res Read more…

By John Russell

Quantum Bits: Neven’s Law (Who Asked for That), D-Wave’s Steady Push, IBM’s Li-O2- Simulation

July 3, 2019

Quantum computing’s (QC) many-faceted R&D train keeps slogging ahead and recently Japan is taking a leading role. Yesterday D-Wave Systems announced it ha Read more…

By John Russell

With the Help of HPC, Astronomers Prepare to Deflect a Real Asteroid

September 26, 2019

For years, NASA has been running simulations of asteroid impacts to understand the risks (and likelihoods) of asteroids colliding with Earth. Now, NASA and the European Space Agency (ESA) are preparing for the next, crucial step in planetary defense against asteroid impacts: physically deflecting a real asteroid. Read more…

By Oliver Peckham

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