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 industry updates delivered to you every week!

Under The Wire: Nearly HPC News (June 13, 2024)

June 13, 2024

As managing editor of the major global HPC news source, the term "news fire hose" is often mentioned. The analogy is quite correct. In any given week, there are many interesting stories, and only a few ever become headli Read more…

Quantum Tech Sector Hiring Stays Soft

June 13, 2024

New job announcements in the quantum tech sector declined again last month, according to an Quantum Economic Development Consortium (QED-C) report issued last week. “Globally, the number of new, public postings for Qu Read more…

Labs Keep Supercomputers Alive for Ten Years as Vendors Pull Support Early

June 12, 2024

Laboratories are running supercomputers for much longer, beyond the typical lifespan, as vendors prematurely deprecate the hardware and stop providing support. A typical supercomputer lifecycle is about five to six years Read more…

MLPerf Training 4.0 – Nvidia Still King; Power and LLM Fine Tuning Added

June 12, 2024

There are really two stories packaged in the most recent MLPerf  Training 4.0 results, released today. The first, of course, is the results. Nvidia (currently king of accelerated computing) wins again, sweeping all nine Read more…

Highlights from GlobusWorld 2024: The Conference for Reimagining Research IT

June 11, 2024

The Globus user conference, now in its 22nd year, brought together over 180 researchers, system administrators, developers, and IT leaders from 55 top research computing centers, national labs, federal agencies, and univ Read more…

Nvidia Shipped 3.76 Million Data-center GPUs in 2023, According to Study

June 10, 2024

Nvidia had an explosive 2023 in data-center GPU shipments, which totaled roughly 3.76 million units, according to a study conducted by semiconductor analyst firm TechInsights. Nvidia's GPU shipments in 2023 grew by more Read more…

Under The Wire: Nearly HPC News (June 13, 2024)

June 13, 2024

As managing editor of the major global HPC news source, the term "news fire hose" is often mentioned. The analogy is quite correct. In any given week, there are Read more…

Labs Keep Supercomputers Alive for Ten Years as Vendors Pull Support Early

June 12, 2024

Laboratories are running supercomputers for much longer, beyond the typical lifespan, as vendors prematurely deprecate the hardware and stop providing support. Read more…

MLPerf Training 4.0 – Nvidia Still King; Power and LLM Fine Tuning Added

June 12, 2024

There are really two stories packaged in the most recent MLPerf  Training 4.0 results, released today. The first, of course, is the results. Nvidia (currently Read more…

Highlights from GlobusWorld 2024: The Conference for Reimagining Research IT

June 11, 2024

The Globus user conference, now in its 22nd year, brought together over 180 researchers, system administrators, developers, and IT leaders from 55 top research Read more…

Nvidia Shipped 3.76 Million Data-center GPUs in 2023, According to Study

June 10, 2024

Nvidia had an explosive 2023 in data-center GPU shipments, which totaled roughly 3.76 million units, according to a study conducted by semiconductor analyst fir Read more…

ASC24 Expert Perspective: Dongarra, Hoefler, Yong Lin

June 7, 2024

One of the great things about being at an ASC (Asia Supercomputer Community) cluster competition is getting the chance to interview various industry experts and Read more…

HPC and Climate: Coastal Hurricanes Around the World Are Intensifying Faster

June 6, 2024

Hurricanes are among the world's most destructive natural hazards. Their environment shapes their ability to deliver damage; conditions like warm ocean waters, Read more…

ASC24: The Battle, The Apps, and The Competitors

June 5, 2024

The ASC24 (Asia Supercomputer Community) Student Cluster Competition was one for the ages. More than 350 university teams worked for months in the preliminary competition to earn one of the 25 final competition slots. The winning teams... Read more…

Atos Outlines Plans to Get Acquired, and a Path Forward

May 21, 2024

Atos – via its subsidiary Eviden – is the second major supercomputer maker outside of HPE, while others have largely dropped out. The lack of integrators and Atos' financial turmoil have the HPC market worried. If Atos goes under, HPE will be the only major option for building large-scale systems. Read more…

Comparing NVIDIA A100 and NVIDIA L40S: Which GPU is Ideal for AI and Graphics-Intensive Workloads?

October 30, 2023

With long lead times for the NVIDIA H100 and A100 GPUs, many organizations are looking at the new NVIDIA L40S GPU, which it’s a new GPU optimized for AI and g Read more…

Nvidia H100: Are 550,000 GPUs Enough for This Year?

August 17, 2023

The GPU Squeeze continues to place a premium on Nvidia H100 GPUs. In a recent Financial Times article, Nvidia reports that it expects to ship 550,000 of its lat Read more…

Everyone Except Nvidia Forms Ultra Accelerator Link (UALink) Consortium

May 30, 2024

Consider the GPU. An island of SIMD greatness that makes light work of matrix math. Originally designed to rapidly paint dots on a computer monitor, it was then Read more…

Choosing the Right GPU for LLM Inference and Training

December 11, 2023

Accelerating the training and inference processes of deep learning models is crucial for unleashing their true potential and NVIDIA GPUs have emerged as a game- Read more…

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, codenamed Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from Read more…

Synopsys Eats Ansys: Does HPC Get Indigestion?

February 8, 2024

Recently, it was announced that Synopsys is buying HPC tool developer Ansys. Started in Pittsburgh, Pa., in 1970 as Swanson Analysis Systems, Inc. (SASI) by John Swanson (and eventually renamed), Ansys serves the CAE (Computer Aided Engineering)/multiphysics engineering simulation market. Read more…

Some Reasons Why Aurora Didn’t Take First Place in the Top500 List

May 15, 2024

The makers of the Aurora supercomputer, which is housed at the Argonne National Laboratory, gave some reasons why the system didn't make the top spot on the Top Read more…

Leading Solution Providers

Contributors

AMD MI3000A

How AMD May Get Across the CUDA Moat

October 5, 2023

When discussing GenAI, the term "GPU" almost always enters the conversation and the topic often moves toward performance and access. Interestingly, the word "GPU" is assumed to mean "Nvidia" products. (As an aside, the popular Nvidia hardware used in GenAI are not technically... Read more…

The NASA Black Hole Plunge

May 7, 2024

We have all thought about it. No one has done it, but now, thanks to HPC, we see what it looks like. Hold on to your feet because NASA has released videos of wh Read more…

Google Announces Sixth-generation AI Chip, a TPU Called Trillium

May 17, 2024

On Tuesday May 14th, Google announced its sixth-generation TPU (tensor processing unit) called Trillium.  The chip, essentially a TPU v6, is the company's l Read more…

Intel’s Next-gen Falcon Shores Coming Out in Late 2025 

April 30, 2024

It's a long wait for customers hanging on for Intel's next-generation GPU, Falcon Shores, which will be released in late 2025.  "Then we have a rich, a very Read more…

GenAI Having Major Impact on Data Culture, Survey Says

February 21, 2024

While 2023 was the year of GenAI, the adoption rates for GenAI did not match expectations. Most organizations are continuing to invest in GenAI but are yet to Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing po Read more…

Intel Plans Falcon Shores 2 GPU Supercomputing Chip for 2026  

August 8, 2023

Intel is planning to onboard a new version of the Falcon Shores chip in 2026, which is code-named Falcon Shores 2. The new product was announced by CEO Pat Gel Read more…

How the Chip Industry is Helping a Battery Company

May 8, 2024

Chip companies, once seen as engineering pure plays, are now at the center of geopolitical intrigue. Chip manufacturing firms, especially TSMC and Intel, have b Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire