Argonne Summer Program Students Tackle Research Projects at Forefront of High-Performance Computing

September 4, 2019

September 4, 2019 — For many students, the summer break means a chance to sleep in, enjoy the sun, and spend a few days at the beach. For the 29 students who spent their summers at the Argonne Leadership Computing Facility (ALCF), a U.S. Department of Energy (DOE) Office of Science User Facility, break meant a chance to embark on scientific endeavors, gaining valuable experience at a world-class research center.

Each summer, students ranging from undergraduates to PhD candidates come to the ALCF to work alongside a mentor—they take on research projects that address important computing problems, from system administration and data analytics to computational science and performance engineering.

“Our summer student program offers a unique experience on multiple levels,” said ALCF Director Michael Papka. “Students are able to see what it is like to work in a national laboratory, but they also get to work closely with experts in their field to solve real-world problems. It’s the kind of experience that can’t be taught in a classroom.”

The summer culminated in special symposiums that allowed students the chance to present on their research projects. Below are brief overviews of how five students spent their summers at the ALCF.

Implementing real-time auto tuning for program optimization

Application performance tuning can be an uphill battle, often requiring repeated program testing runs that take precious time away from completing actual computations.

This is just what Kavon Farvardin, PhD student from the University of Chicago, is working to combat. Farvardin returned to the ALCF for a second summer this year, following up on his previous work with real-time auto tuning. Auto tuning usually takes place prior to deployment, but Farvardin is interested in optimizing a program while it is running.

This year, Farvardin has extended his previous work into a new project called HALO (Wholly Adaptive LLVM Optimizer). “The idea is we can try and leverage recent techniques, such as available machine learning libraries, to help model the performance of a system and converge on an optimal configuration in as few steps as possible, yet also do it while the program is running live,” Farvardin said. “Because we’re doing this tuning as the actual production run data is being processed, our tuning is more specific to that workload.”

This concept could benefit a facility like the ALCF, as Farvardin hopes his optimization tool will yield notable performance improvements for high-performance computing applications. ALCF resources were beneficial for Farvardin as well, particularly the community of researchers he was able to work with. “I don’t have heavy-duty computations to work on,” he said, “but I care about making those computations that exist work faster—it was pretty awesome having connections to people who were interested in testing my project on their work.”

Processing neuroscientific X-ray and microscopy data on future exascale systems

After several years with a remote graduate appointment at Argonne, Jeffery Kinnison spent his first summer on site as an ALCF summer student this year. The PhD candidate from the University of Notre Dame continued to participate in an ongoing effort to make scalable computer vision and deep learning tools for processing neuroscientific data.

The team has been utilizing neuroscientific electron microscopy and X-ray data from Argonne’s Advanced Photon Source, a DOE Office of Science User Facility, working to understand how the brain is wired, potentially resulting in enhanced artificial intelligence capabilities or advanced treatment for neurological conditions.

Kinnison has been focusing on preparing tools for processing this X-ray data on future exascale computers, including the ALCF’s Aurora system. “We’re rolling out a couple of different pieces of software on Theta right now as a sort of first pass to prepare for Aurora later down the road,” he said. “I’ve been working specifically on scaling up some software that I wrote for my PhD to be able to process hundreds of gigabytes or petabytes of data in a reasonable amount of time.”

The team has also been exploring the usage of deep learning for neuroscience, looking at how it can help analyze and understand the X-ray and microscopy data available.

Kinnison’s time at the ALCF has helped him realized the scope and applicability of his work. “It seems like every other day my mentor is coming to me and asking ‘Hey, do you think this would be a good fit for a project that this person I know is doing?’” said Kinnison. “The projects often seem unrelated to ours, but we’re starting to find ways that we might be able to contribute a little bit to some of the other work that’s going on. It’s been surprising in a good way to see that what I’m doing is so applicable across different domains.”

Using Kubernetes for data-intensive workloads

Alessandro Buy is a senior data science major at the University of California, Berkeley. He found his time at the ALCF invaluable in helping him gain both experience and direction as he neared graduation; notable was the hands-on and problem-solving experience his ALCF internship allowed—two things not always present in the classroom.

Much of Buy’s research this summer was spent looking into the open-source, cloud computing software Kubernetes, specifically evaluating its capabilities for data-intensive workloads. This software would allow machine learning applications and databases to be deployed across computing resources. “If we could deploy all of these databases with Kubernetes, it would increase the ability to manage and access them from different computers. It would help behind the scenes, as opposed to the straight up coding,” Buy said.

“I think the things I learned can be applied anywhere: you have this problem that you don’t know how to solve and then research and figure out how to get around it and get through it. That’s something that Argonne has taught me a lot so far,” said Buy. He also noted the various skills he was able to develop, ranging from new software to different data science tools. While Buy has yet to decide on his post-graduation plans, his time at the ALCF left him considering a future research position.

Applying quantum circuits to various physics problems

Nouman Butt, a PhD student at Syracuse University, spent his summer working to build a quantum computation-based algorithm. This would evaluate tensor networks associated with various statistical and field theoretical models and map them onto quantum mechanical problems. Many of these systems have been studied using classical simulations, but when studied as quantum computations, different information emerges.

Much of Butt’s previous research was on lattice field theories, but in recent months, he shifted to focus on tensor networks, helping inform the work he did at the ALCF. Butt’s tensor network research, specifically on quantum computation, “is the next step toward more generic algorithms that can help realize the novel structure of classical and quantum systems,” he said.

“Many physics problems studied using classical computers are now at a bottleneck. This is an alternative line of research where you can take the same problems and recast them into quantum computation problems, eventually leading us to algorithmic and physics development,” said Butt.

The collaborative environment at the ALCF has proved to be instrumental for the development of this new algorithm. “Sometimes you might not be looking at the problem the correct way, but someone from a totally different field has another perspective,” said Butt. “This combination of people working together can lead to new solutions.”

Studying quantum circuits in low dimensions

University of Colorado PhD student Michael Perlin spent some of his time working alongside Butt, studying the use of quantum computers to compute quantities represented by tensor networks. “I focused more specifically on the use of quantum computers for tensor-network-based methods to diagnose phase transitions in condensed matter systems. Things like the transition between magnetized and non-magnetized phases of a magnetic material,” Perlin said. These quantum computers are well suited for handling quantities associated with tensor networks due to their large intermediate memory capacity.

Perlin also participated in a second collaborative project, studying ways to cut large circuits into smaller circuit fragments that could then be run on classical computers or small quantum devices. He worked with his mentor and three other research students to explore strategies to recombine fragment simulation results in a way that mimics the circuit’s pre-dissected state.

This study should help to identify effective ways of simulating large quantum circuits that don’t rely on large-scale quantum computers, helping render quantum computations more accessible.

Perlin chose to do research at the ALCF because of the career-refining experience it offered him. “As a graduate student with an open-ended career path, I am on the lookout for research areas and work environments that I may want to pursue in the future,” said Perlin. “ALCF offered an opportunity to study a subject I am interested in (quantum computing) at a type of institution where I haven’t worked before.

2019 ALCF Summer Students

Alberto Acevedo, University of Arizona
Mentor: Yuri Alexeev

Joseph Adamo, University of Illinois at Urbana-Champaign
Mentor: JD Emberson

Nouman Butt, Syracuse University
Mentor: James Osborn

Alessandro Buy, University of California, Berkley
Mentor: Corey Adams

Ram Sharan Chaulgain, Florida State University
Mentor: Scott Parker

Blake Ehrenbeck, Illinois Institute of Technology
Mentor: Bill Allcock

Kavon Farvardin, The University of Chicago
Mentor: Hal Finkle

Abigail Herwaldt, Loras College
Mentor: Beth Cerny

Ryan Kabrick, University of Delaware
Mentor: Thomas Applencourt

Yao “Raine” Kang, Illinois Institute of Technology
Mentor: Sudheer Chunduri

Mrinal Kanti, Northern Illinois University
Mentor: Taylor Childers

Sami Khairy, Illinois Institute of Technology
Mentor: Prasanna Balaprakash

Vennela Kilaru, Northern Illinois University
Mentor: Bill Allcock

Hayley Kim, Valparaiso University
Mentor: Jim Collins

Jeffery Kinnison, University of Notre Dame
Mentor: Tom Uram

Arthur Kraus, Loyola University Chicago
Mentor: Doug Waldron

Vishvak Kumaran, University of Illinois at Chicago
Mentor: Kevin Harms

Ryan Lewis, Northern Illinois University
Mentor: Janet Knowles

Boyang Li, Illinois Institute of Technology
Mentor: Kevin Harms

Michael Perlin, University of Colorado Boulder
Mentor: Xiao-Yong Jin

Ivan Sanchez, Northern Illinois University
Mentor: Silvio Rizzi

Sergio Servantez, Illinois Institute of Technology
Mentor: Huihou Zheng

Ruslan Shaydulin, Clemson University
Mentor: Yuri Alexeev

Shilpika, University of California, Davis
Mentor: Bethany Lusch

Michael Simon, Northern Illinois University
Mentor: JaeHyuk Kwack

Xin Wang, Illinois Institute of Technology
Mentor: Sudheer Chundri

Xin-Chuan Wu, The University of Chicago
Mentor: Hal Finkle

Yuliana Zamora, The University of Chicago
Mentor: Murali Emani

Ning Zhang, Illinois Institute of Technology
Mentor: Bill Allcock


About Argonne National Laboratory 

Argonne National Laboratory seeks solutions to pressing national problems in science and technology. The nation’s first national laboratory, Argonne conducts leading-edge basic and applied scientific research in virtually every scientific discipline. Argonne researchers work closely with researchers from hundreds of companies, universities, and federal, state and municipal agencies to help them solve their specific problems, advance America’s scientific leadership and prepare the nation for a better future. With employees from more than 60 nations, Argonne is managed by UChicago Argonne, LLC for the U.S. Department of Energy’s Office of Science.

About the U.S. Department of Energy’s Office of Science 

The U.S. Department of Energy’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of the most pressing challenges of our time. For more information, visit

SOurce: Hayley Kim, Argonne National Laboratory 

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!

Dell’s AMD-Powered Server Line Targets High-End Jobs

September 17, 2019

Dell Technologies rolled out five new servers this week based on AMD’s latest Epyc processor that are geared toward data-driven workloads running on increasingly popular multi-cloud platforms as well as in the HPC data Read more…

By George Leopold

Cerebras to Supply DOE with Wafer-Scale AI Supercomputing Technology

September 17, 2019

Cerebras Systems, which debuted its wafer-scale AI silicon at Hot Chips last month, has entered into a multi-year partnership with Argonne National Laboratory and Lawrence Livermore National Laboratory as part of a larger collaboration with the U.S. Department of Energy... Read more…

By Tiffany Trader

Better Scientific Software: Turn Your Passion into Cash

September 13, 2019

Do you know your way around scientific software and programming? You think you can contribute to the community by making scientific software better? If so, then the Better Scientific Software (BSSW) organization wants yo Read more…

By Dan Olds

AWS Solution Channel

A Guide to Discovering the Best AWS Instances and Configurations for Your HPC Workload

The flexibility and heterogeneity of HPC cloud services provide a welcome contrast to the constraints of on-premises HPC. Every HPC configuration is potentially accessible to any given workload in a well-resourced cloud HPC deployment, with vast scalability to spin up as much compute as that workload demands in any given moment. 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

Rumors of My Death Are Still Exaggerated: The Mainframe

[Connect with Spectrum users and learn new skills in the IBM Spectrum LSF User Community.]

As of 2017, 92 of the world’s top 100 banks used mainframes. Read more…

Google’s ML Compiler Initiative Advances

September 12, 2019

Machine learning models running on everything from cloud platforms to mobile phones are posing new challenges for developers faced with growing tool complexity. Google’s TensorFlow team unveiled an open-source machine Read more…

By George Leopold

Cerebras to Supply DOE with Wafer-Scale AI Supercomputing Technology

September 17, 2019

Cerebras Systems, which debuted its wafer-scale AI silicon at Hot Chips last month, has entered into a multi-year partnership with Argonne National Laboratory and Lawrence Livermore National Laboratory as part of a larger collaboration with the U.S. Department of Energy... Read more…

By Tiffany Trader

IDAS: ‘Automagic’ HPC With Training Wheels

September 12, 2019

High-performance computing (HPC) for research is notorious for having steep barriers to entry. For this reason, high-tech disciplines were early adopters, have Read more…

By Elizabeth Leake

Univa Brings Cloud Automation to Slurm Users with Navops Launch 2.0

September 11, 2019

Univa, the company behind Grid Engine, announced today its HPC cloud-automation platform NavOps Launch will support the popular open-source workload scheduler Slurm. With the release of NavOps Launch 2.0, “Slurm users will have access to the same cloud automation capabilities... Read more…

By Tiffany Trader

When Dense Matrix Representations Beat Sparse

September 9, 2019

In our world filled with unintended consequences, it turns out that saving memory space to help deal with GPU limitations, knowing it introduces performance pen Read more…

By James Reinders

Eyes on the Prize: TACC’s Frontera Quickly Ramps up Science Agenda

September 9, 2019

Announced a year ago and officially launched a week ago, the Texas Advanced Computing Center’s Frontera – now the fastest academic supercomputer (~25 petefl Read more…

By John Russell

Quantum Roundup: IBM Goes to School, Delft Tackles Networking, Rigetti Updates

September 5, 2019

IBM today announced a new open source quantum ‘textbook’, a series of quantum education videos, and plans to expand its nascent quantum hackathon program. L Read more…

By John Russell

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

Fastest Academic Supercomputer Enters Full Production at TACC, Just in Time for Hurricane Season

September 3, 2019

Frontera, the NSF supercomputer installed at the Texas Advanced Computing Center (TACC) in June, passed its formal acceptance last week and is now officially la Read more…

By Tiffany Trader

High Performance (Potato) Chips

May 5, 2006

In this article, we focus on how Procter & Gamble is using high performance computing to create some common, everyday supermarket products. Tom Lange, a 27-year veteran of the company, tells us how P&G models products, processes and production systems for the betterment of consumer package goods. Read more…

By Michael Feldman

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

AMD Verifies Its Largest 7nm Chip Design in Ten Hours

June 5, 2019

AMD announced last week that its engineers had successfully executed the first physical verification of its largest 7nm chip design – in just ten hours. The AMD Radeon Instinct Vega20 – which boasts 13.2 billion transistors – was tested using a TSMC-certified Calibre nmDRC software platform from Mentor. Read more…

By Oliver Peckham

TSMC and Samsung Moving to 5nm; Whither Moore’s Law?

June 12, 2019

With reports that Taiwan Semiconductor Manufacturing Co. (TMSC) and Samsung are moving quickly to 5nm manufacturing, it’s a good time to again ponder whither goes the venerable Moore’s law. Shrinking feature size has of course been the primary hallmark of achieving Moore’s law... Read more…

By John Russell

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

Nvidia Embraces Arm, Declares Intent to Accelerate All CPU Architectures

June 17, 2019

As the Top500 list was being announced at ISC in Frankfurt today with an upgraded petascale Arm supercomputer in the top third of the list, Nvidia announced its Read more…

By Tiffany Trader

Leading Solution Providers

ISC 2019 Virtual Booth Video Tour


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

Top500 Purely Petaflops; US Maintains Performance Lead

June 17, 2019

With the kick-off of the International Supercomputing Conference (ISC) in Frankfurt this morning, the 53rd Top500 list made its debut, and this one's for petafl Read more…

By Tiffany Trader

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

Cray – and the Cray Brand – to Be Positioned at Tip of HPE’s HPC Spear

May 22, 2019

More so than with most acquisitions of this kind, HPE’s purchase of Cray for $1.3 billion, announced last week, seems to have elements of that overused, often Read more…

By Doug Black and Tiffany Trader

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

Qualcomm Invests in RISC-V Startup SiFive

June 7, 2019

Investors are zeroing in on the open standard RISC-V instruction set architecture and the processor intellectual property being developed by a batch of high-flying chip startups. Last fall, Esperanto Technologies announced a $58 million funding round. Read more…

By George Leopold

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

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

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