Helping Experimental Scientists Take Supercomputers to the Max

By Doug Black, Contributing Writer

December 30, 2014

Doug Baxter is a capability lead for the Molecular Science Computing Facility in the Environmental Molecular Sciences Laboratory (EMSL) at Pacific Northwest National Laboratory. He and his team are responsible for the software side of the operation, and they help experimental scientists get the most out of EMSL’s supercomputing resources. The facility is the home of Cascade, which is ranked number 18 on the current TOP500 list of the world’s most powerful supercomputers.

In this interview, we talk with Doug about EMSL’s Cascade supercomputer, the NWChem software package, and code modernization.

doug-baxter-headshotHPCwire: Can you give me a sense of what your daily role is at EMSL?

Baxter: I mainly manage the allocation of resources on our Cascade supercomputer and help our users get up and running on it successfully. Once they get their application running, I have a team of computational biologists, computational chemists and other computer scientists to help them address performance and efficiency.

We’re a national scientific user facility, and we help users from all over the nation working on scientific applications relevant to DOE’s Office of Biological and Environmental Research (BER). They are focused on predictive understanding for biological processes, subsurface flow, contaminants and clean-up, climate modeling, and aerosols.

One thing that makes EMSL special is the combination of our experimental instruments and our high performance computing that provides a theory side to the experimental aspect of science.

HPCwire: Is the work that you’re doing primarily in support of applications running on Cascade?

Baxter: It is primarily in support of Cascade and the corresponding archive system, which is shared with our institutional computing facility. We devote part of our time to outside research projects, including other supercomputing efforts here at the laboratory. We also have an institutional supercomputer, Olympus, and its successor, Constance.

HPCwire: How much of the workload on Cascade is NWChem?

Baxter: NWChem comprises 30 to 40 percent of the workload on Cascade. We keep statistics on what we run on the machine and we’re starting to see an increase in our climate modeling codes, our subsurface flow modeling codes as well as some of our computational biology codes as we have new projects in BER’s areas of interest. But as we support BER’s mission we expect that the computational chemistry pieces will continue to remain a large player.

HPCwire: You commented on climate modeling. Is this a lot of proprietary code?

Baxter: These are mostly codes that come out of NOAA and so they are publicly available codes, including the Weather Research and Forecasting (WRF) model. DOE research heavily utilizes the Community Earth System Model (CESM) and its land model component, the Community Land Model (CLM), both also publicly available. We do a lot of aerosol modeling and that gets down into molecular chemistry level and we’re back into computational chemistry again.

HPCwire: Are many of those codes you’re referring to, other than NWChem, developed for parallel systems?

Baxter: The climate codes and the subsurface flow codes are developed for parallel systems. Parallel systems have been developing over the years as well, so a lot of them have a long history of parallel computing. But that doesn’t necessarily mean they’re set to move on to the next stage of computing, to move on to grander scales of parallelism. That requires some rethinking of the way we’ve traditionally done things in the past.

PNNL Cascade-specsHPCwire: On that topic of code modernization or code optimization, what does that mean for you and your team as you prepare some of the codes for the many core architectures?

Baxter: Before you get to the parallel stage, you have to start with a good serial code. So some of what we do in preparing code to run at scale is going back to the mathematics and saying, ‘what exactly do we want to solve here?’ Sometimes we have to think differently, in a fundamental way about various things. Traditionally, in parallel computing, the assumption has been, ‘I have a fixed number of resources for the duration of the task that I’m executing and I have a lot of things to do. I partition the tasks that I want done among the players that I have, but my expectation is that all those players play for the full duration of the job and they synchronize with each other.’

One of the difficulties moving forward into exa-scale is global synchronization. As we increase to hundreds of thousands of processes or possibly millions of processes, synchronization becomes untenable. So we must think about things in a non-global-participation way. That requires a fair amount of effort because you need to think differently algorithmically about traditional computing. It used to be that FLOPS were very expensive and memory accesses were relatively inexpensive. So people spent a lot of time saving the results of computation so that they didn’t have to recompute them, doing the expensive part again. Now we’ve got a lot more FLOPS than we have memory accesses, so you can compute much faster than you can move data. That shifts the emphasis – it is sometimes cheaper to recompute a result than have it stored and read back in. So as we move toward greater predictive understanding of the processes that our sponsor is interested in, that requires higher resolution in our models—that means more data points, that means bigger problems to solve. We need more processors to work on problems, but we also have to think about solving them in different ways.

HPCwire: How important is the role of coprocessors moving forward?

Baxter: They are important. They’re really fast at computing but keeping them busy is a challenge, and one of the requirements is the ability to move data to them asynchronously, in a one-sided fashion, which is becoming more prevalent. The Message Passing Interface (MPI) standard is the distributed memory programming paradigm. The MPI 2, and the MPI 3 standards have included one-sided communication protocols where a processor can move data into another processor’s memory space without taxing that processor.

HPCwire: How do you train your staff to get into the right frame of mind?

Baxter: A good starting place is the Jeffers/Reinders book (Intel Xeon Phi Coprocessor High Performance Programming by Jim Jeffers and James Reinders).
Fundamentally, it’s about starting with good serial code and then managing message passing in general. We also spend some effort developing methodologies that work with MPI. One thing I find useful is experimenting with the Intel Symmetric Communication Interface. It’s used to support MPI on the coprocessor. One of the basic ways to use the Xeon Phi coprocessors is to run MPI ranks on each of them so you use the same standard model programming. The difficulty with that is you can’t use all the Xeon Phi coprocessors on more than a handful of nodes because the MPI implementation layer is too memory-intensive. But the SCIF API exposes the communication calls, which allows us to go in and play with that in different ways.

Aside from assisting our various supercomputer users we also have some outside research interests that help improve our ability to help our users. And so part of what I do is work on ways to use those accelerators generically and then push that out to people doing development.

HPCwire: The theme for SC14 this year was “HPC Matters” and for 2015, the theme is “HPC Transforms.” In your own words, why do you think HPC matters?

Baxter: HPC does matter and it continually matters more. We use HPC to solve larger modeling problems, which are designed to help us get what we call predictive understanding of models and processes – such as the flow of radioactive ions leaking from waste tanks. We want to understand how to remediate that problem. Some of what we do is simulate the bacteria that can actually reduce those ions so they precipitate out of solution, making them non-mobile. Some of it is flow analysis of the water table and the surrounding elements to understand if there is a risk of radiation reaching a water source. Some of the modeling that we do is climate modeling to understand aerosols and effects of man-generated pollution on the radiated energy balance and what that is doing to our environment. And those models require lots of data and lots of computation, but they help us understand processes. For something like energy storage, understanding the process helps us control, modify, and improve efficiency.

The other important part of HPC is the predictive modeling. It’s frequently much less expensive to model things and arrive at possibilities for testing experimentally than it is to build many different physical test models. If our model is accurate and looks like a promising way to go, it helps narrow down the breadth of possible solutions in terms of exploring and developing mechanistic, chemical, and biological solutions to technical problems.

HPCwire: Of the work that your team does here, what are you most proud of?

Baxter: At any given time we have about 60 different proposals using our supercomputer. We are proud of the impact we have on science, on our ability to provide a production environment for our users, and our more recent success of transitioning from our previous supercomputer to a new one. It involved planning and experimenting with the old system before we moved it to the new one, and getting the software pieces in order and ready to run. It’s a challenging process, but that was perhaps one of the smoothest transitions we’ve had. We opened the machine to first users on the 6th of December (2013). By the 1st of January all of our users had been ported, all of their codes were running on the new system. So our most significant broad achievement is the transition from a five-year-old supercomputer to the 18th fastest in the world. Migrating users in less than a month is pretty impressive.

HPCwire: Do you have examples of some of the things you can do with Cascade that you couldn’t do with your previous supercomputer, Chinook?

Baxter: One example is in the NWChem arena where we have been able to increase the scale of the problems our users are able to tackle with Cascade.

In terms of peak performance of the machine, our previous machine, Chinook was a 160 teraflop machine; our current machine Cascade is a 3.5 petaflop machine. So that’s more than a 20X improvement in terms of peak performance. Without the accelerators, our expectation was that Cascade would run about three times faster than Chinook. What we found was that it ran four to six times faster.

Getting into the accelerators is a challenge. It takes some effort and rewriting of some code. That’s one of the things we need to get the community to understand is that it’s more than just plunking a machine down on the floor. It takes a software development effort to make these things go. But the return can be worth it. We’ve had some success in getting improved speedups with the accelerators. Linpack-wise, we measure a 2.5 petaflop performance out of the peak 3.4 petaflops. That’s an achievement. We don’t run Linpack on the machine but it’s a measure of the machine’s capacity. Then we work on bringing that kind of improvement to our other codes.

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: Wind Farms, Gravitational Lenses, Web Portals & More

February 19, 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

Insights from Optimized Codes on Cineca’s Marconi

February 15, 2019

What can you do with 381,392 CPU cores? For Cineca, it means enabling computational scientists to expand a large part of the world’s body of knowledge from the nanoscale to the astronomic, from calculating quantum effe Read more…

By Ken Strandberg

What Will IBM’s AI Debater Learn from Its Loss?

February 14, 2019

The utility of IBM’s latest man-versus-machine gambit is debatable. At the very least its Project Debater got us thinking about the potential uses of artificial intelligence as a way of helping humans sift through al Read more…

By George Leopold

HPE Extreme Performance Solutions

HPE Systems With Intel Omni-Path: Architected for Value and Accessible High-Performance Computing

Today’s high-performance computing (HPC) and artificial intelligence (AI) users value high performing clusters. And the higher the performance that their system can deliver, the better. Read more…

IBM Accelerated Insights

Medical Research Powered by Data

“We’re all the same, but we’re unique as well. In that uniqueness lies all of the answers….”

  • Mark Tykocinski, MD, Provost, Executive Vice President for Academic Affairs, Thomas Jefferson University

Getting the answers to what causes some people to develop diseases and not others is driving the groundbreaking medical research being conducted by the Computational Medicine Center at Thomas Jefferson University in Philadelphia. Read more…

ClusterVision in Bankruptcy, Fate Uncertain

February 13, 2019

ClusterVision, European HPC specialists that have built and installed over 20 Top500-ranked systems in their nearly 17-year history, appear to be in the midst of bankruptcy proceedings. According to Dutch news site Drimb Read more…

By Tiffany Trader

Insights from Optimized Codes on Cineca’s Marconi

February 15, 2019

What can you do with 381,392 CPU cores? For Cineca, it means enabling computational scientists to expand a large part of the world’s body of knowledge from th Read more…

By Ken Strandberg

ClusterVision in Bankruptcy, Fate Uncertain

February 13, 2019

ClusterVision, European HPC specialists that have built and installed over 20 Top500-ranked systems in their nearly 17-year history, appear to be in the midst o Read more…

By Tiffany Trader

UC Berkeley Paper Heralds Rise of Serverless Computing in the Cloud – Do You Agree?

February 13, 2019

Almost exactly ten years to the day from publishing of their widely-read, seminal paper on cloud computing, UC Berkeley researchers have issued another ambitious examination of cloud computing - Cloud Programming Simplified: A Berkeley View on Serverless Computing. The new work heralds the rise of ‘serverless computing’ as the next dominant phase of cloud computing. Read more…

By John Russell

Iowa ‘Grows Its Own’ to Fill the HPC Workforce Pipeline

February 13, 2019

The global workforce that supports advanced computing, scientific software and high-speed research networks is relatively small when you stop to consider the magnitude of the transformative discoveries it empowers. Technical conferences provide a forum where specialists convene to learn about the latest innovations and schedule face-time with colleagues from other institutions. Read more…

By Elizabeth Leake, STEM-Trek

Trump Signs Executive Order Launching U.S. AI Initiative

February 11, 2019

U.S. President Donald Trump issued an Executive Order (EO) today launching a U.S Artificial Intelligence Initiative. The new initiative - Maintaining American L Read more…

By John Russell

Celebrating Women in Science: Meet Four Women Leading the Way in HPC

February 11, 2019

One only needs to look around at virtually any CS/tech conference to realize that women are underrepresented, and that holds true of HPC. SC hosts over 13,000 H Read more…

By AJ Lauer

IBM Bets $2B Seeking 1000X AI Hardware Performance Boost

February 7, 2019

For now, AI systems are mostly machine learning-based and “narrow” – powerful as they are by today's standards, they're limited to performing a few, narro Read more…

By Doug Black

Assessing Government Shutdown’s Impact on HPC

February 6, 2019

After a 35-day federal government shutdown, the longest in U.S. history, government agencies are taking stock of the damage -- and girding for a potential secon Read more…

By Tiffany Trader

Quantum Computing Will Never Work

November 27, 2018

Amid the gush of money and enthusiastic predictions being thrown at quantum computing comes a proposed cold shower in the form of an essay by physicist Mikhail Read more…

By John Russell

Cray Unveils Shasta, Lands NERSC-9 Contract

October 30, 2018

Cray revealed today the details of its next-gen supercomputing architecture, Shasta, selected to be the next flagship system at NERSC. We've known of the code-name "Shasta" since the Argonne slice of the CORAL project was announced in 2015 and although the details of that plan have changed considerably, Cray didn't slow down its timeline for Shasta. Read more…

By Tiffany Trader

The Case Against ‘The Case Against Quantum Computing’

January 9, 2019

It’s not easy to be a physicist. Richard Feynman (basically the Jimi Hendrix of physicists) once said: “The first principle is that you must not fool yourse Read more…

By Ben Criger

AMD Sets Up for Epyc Epoch

November 16, 2018

It’s been a good two weeks, AMD’s Gary Silcott and Andy Parma told me on the last day of SC18 in Dallas at the restaurant where we met to discuss their show news and recent successes. Heck, it’s been a good year. Read more…

By Tiffany Trader

Intel Reportedly in $6B Bid for Mellanox

January 30, 2019

The latest rumors and reports around an acquisition of Mellanox focus on Intel, which has reportedly offered a $6 billion bid for the high performance interconn Read more…

By Doug Black

US Leads Supercomputing with #1, #2 Systems & Petascale Arm

November 12, 2018

The 31st Supercomputing Conference (SC) - commemorating 30 years since the first Supercomputing in 1988 - kicked off in Dallas yesterday, taking over the Kay Ba Read more…

By Tiffany Trader

Looking for Light Reading? NSF-backed ‘Comic Books’ Tackle Quantum Computing

January 28, 2019

Still baffled by quantum computing? How about turning to comic books (graphic novels for the well-read among you) for some clarity and a little humor on QC. The Read more…

By John Russell

ClusterVision in Bankruptcy, Fate Uncertain

February 13, 2019

ClusterVision, European HPC specialists that have built and installed over 20 Top500-ranked systems in their nearly 17-year history, appear to be in the midst o Read more…

By Tiffany Trader

Leading Solution Providers

SC 18 Virtual Booth Video Tour

Advania @ SC18 AMD @ SC18
ASRock Rack @ SC18
DDN Storage @ SC18
HPE @ SC18
IBM @ SC18
Lenovo @ SC18 Mellanox Technologies @ SC18
One Stop Systems @ SC18
Oracle @ SC18 Panasas @ SC18
Supermicro @ SC18 SUSE @ SC18 TYAN @ SC18
Verne Global @ SC18

Contract Signed for New Finnish Supercomputer

December 13, 2018

After the official contract signing yesterday, configuration details were made public for the new BullSequana system that the Finnish IT Center for Science (CSC Read more…

By Tiffany Trader

Deep500: ETH Researchers Introduce New Deep Learning Benchmark for HPC

February 5, 2019

ETH researchers have developed a new deep learning benchmarking environment – Deep500 – they say is “the first distributed and reproducible benchmarking s Read more…

By John Russell

IBM Quantum Update: Q System One Launch, New Collaborators, and QC Center Plans

January 10, 2019

IBM made three significant quantum computing announcements at CES this week. One was introduction of IBM Q System One; it’s really the integration of IBM’s Read more…

By John Russell

HPC Reflections and (Mostly Hopeful) Predictions

December 19, 2018

So much ‘spaghetti’ gets tossed on walls by the technology community (vendors and researchers) to see what sticks that it is often difficult to peer through Read more…

By John Russell

IBM Bets $2B Seeking 1000X AI Hardware Performance Boost

February 7, 2019

For now, AI systems are mostly machine learning-based and “narrow” – powerful as they are by today's standards, they're limited to performing a few, narro Read more…

By Doug Black

Nvidia’s Jensen Huang Delivers Vision for the New HPC

November 14, 2018

For nearly two hours on Monday at SC18, Jensen Huang, CEO of Nvidia, presented his expansive view of the future of HPC (and computing in general) as only he can do. Animated. Backstopped by a stream of data charts, product photos, and even a beautiful image of supernovae... Read more…

By John Russell

The Deep500 – Researchers Tackle an HPC Benchmark for Deep Learning

January 7, 2019

How do you know if an HPC system, particularly a larger-scale system, is well-suited for deep learning workloads? Today, that’s not an easy question to answer Read more…

By John Russell

Intel Confirms 48-Core Cascade Lake-AP for 2019

November 4, 2018

As part of the run-up to SC18, taking place in Dallas next week (Nov. 11-16), Intel is doling out info on its next-gen Cascade Lake family of Xeon processors, specifically the “Advanced Processor” version (Cascade Lake-AP), architected for high-performance computing, artificial intelligence and infrastructure-as-a-service workloads. Read more…

By Tiffany Trader

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