Argonne researchers to share scientific computing advances at SC20 conference

Researchers from the U.S. Department of Energy’s (DOE) Argonne National Laboratory will share their latest insights and advances in high performance computing (HPC) at SC20, the International Conference for High Performance Computing, Networking, Storage, and Analysis. This year’s event will be held virtually Nov. 9–19.

Continuing the laboratory’s long history of participation in the SC conference series, more than 90 Argonne researchers will contribute to conference activities and studies on topics ranging from exascale computing and big data analysis to artificial intelligence (AI) and quantum computing.

“This year’s SC conference provides an opportunity to hear from experts about the array of new technologies being explored by researchers and the advances we are making in addressing the challenges raised by the emerging exascale era,” said Valerie Taylor, director of Argonne’s Mathematics and Computer Science division and Argonne Distinguished Fellow. “Through the many virtual panels, papers, posters and workshops, the scientific community can share their accomplishments and demonstrate how research is being used for scientific discovery and societal impact.”

The laboratory’s conference activities will include technical paper presentations, invited talks, workshops, “birds of a feather” sessions, panel discussions and tutorials. Some notable Argonne contributions are highlighted below. For the full schedule of the laboratory’s participation in the conference, visit Argonne’s SC20 webpage.

Tutorial: Lossy Compression for Scientific Data

• Presenters: Franck Cappello, Peter Lindstrom, Sheng Di
• Date: Monday, Nov. 9: 2:30 p.m.-6:30 p.m. (EST)

Researchers from Argonne and Lawrence Livermore National Laboratory will present a tutorial on using lossy data compression to reduce the size of increasingly large scientific datasets. They will use real-world examples to illustrate the capabilities and performance of different compression techniques.

Tutorial: Performance Tuning with the Roofline Model on GPUs and CPUs

• Presenters: Samuel Williams, Aleksandar Ilic, Zakhar Matveev, Max Katz, JaeHyuk Kwack, Charlene Yang, Colleen Bertoni, Khaled Ibrahim
• Dates: Monday Nov. 9-Tuesday, Nov. 10: 10 a.m.-2 p.m. (EST)

Argonne researchers will contribute to a two-day tutorial on how the Roofline performance model can be used to help optimize applications for central processing units (CPUs) and graphic processing units (GPUs). The tutorial will include hands-on instruction and discussions of real-world use cases.

First International Workshop on Quantum Computing Software

• Organizers: Travis Humble, Scott Pakin, Michael McGuigan, Yuri Alexeev, Jim Kowalski, Ojas Parekh, Bert de Jong, Nathan Wiebe, Jonathan Dubois
• Date: Wednesday, Nov. 11: 10 a.m.-6:30 p.m. (EST)

Argonne computational scientist Yuri Alexeev teamed up with researchers from eight other national laboratories to co-organize a new workshop focused on exploring the software tools and techniques needed to make quantum computing practical and accessible. The workshop will cover topics such as programming languages, quantum computing simulators and debuggers.

Scaffold-Induced Molecular Subgraphs (SIMSG): Effective Graph Sampling Methods for High-Throughput Computational Drug Discovery

• Authors: Austin Clyde, Ashka Shah, Max Zvyagin, Arvind Ramanathan, Rick Stevens
• Date: Friday, Nov. 13: 12:15–12:30 p.m. (EST)

At SC20‘s Computational Approaches for Cancer Workshop, Argonne and University of Chicago researchers will present a paper detailing a novel approach that can be used to efficiently navigate vast chemical libraries for promising drug candidates. By using a graph-based structure of the chemical space instead of a static library of compounds, their study demonstrates an enhanced sampling technique for ultra-high-throughput docking studies.

Deep Learning-Based Low-Dose Tomography Reconstruction with Hybrid-Dose Measurements

• Authors: Ziling Wu, Tekin Bicer, Zhengchun Liu, Vincent De Andrade, Yunhui Zhu, Ian T. Foster
• Date: Friday, Nov. 13: 12:15–12:40 p.m. (EST)

At the Workshop on Artificial Intelligence and Machine Learning for Scientific Applications, researchers from Argonne and Virginia Tech will present a paper on a deep learning framework that can be applied to tomography reconstruction and other X-ray imaging techniques for enhanced analysis of dose-sensitive samples.

More Than HPC Plenary: Advanced Computing and COVID-19

• Presenters: Ilkay Altintas, Rommie Amaro, Rick Stevens, Alessandro Vespignani
• Date: Monday, Nov. 16: 2–3:30 p.m. (EST)

Rick Stevens, Associate Laboratory Director for Argonne’s Computing, Environment and Life Sciences (CELS) Directorate, will take part in a panel discussion that highlights how the scientific computing community is using HPC to advance COVID-19 research.

Recurrent Neural Network Architecture Search for Geophysical Emulation

• Authors: Romit Maulik, Romain Egele, Bethany Lusch, Prasanna Balaprakash
• Date: Tuesday, Nov. 17: 10:30–11 a.m. (EST)

Argonne researchers will present a technical paper detailing the development of a scalable neural architecture search to forecast sea surface temperatures using a dataset from the National Oceanic and Atmospheric Administration. Their work to develop surrogate geophysical models has the potential to reduce the large computational cost involved in atmospheric and oceanic modeling.

Petascale XCT: 3D Image Reconstruction with Hierarchical Communications on Multi-GPU Nodes

• Authors: Mert Hidayetoglu, Tekin Bicer, Simon Garcia de Gonzalo, Bin Ren, Vincent De Andrade, Doga Gursoy, Rajkumar Kettimuthu, Ian T. Foster, Wen-mei W. Hwu
• Date: Tuesday, Nov. 17: 3–3:30 p.m. (EST)

A finalist for the conference’s Best Paper Award, this study, co-authored by Argonne researchers, introduces a novel method for recovering high-quality 3D volumetric images from 2D X-ray images generated at experimental synchrotron facilities. Their research could open the door to using iterative tomographic reconstruction algorithms on larger datasets than are currently possible.

HPC I/O Throughput Bottleneck Analysis with Explainable Local Models

• Authors: Mihailo Isakov, Eliakin del Rosario, Sandeep Madireddy, Prasanna Balaprakash, Phillip H. Carns, Robert Ross, Michel A. Kinsy
• Date: Tuesday, Nov. 17: 4–4:30 p.m. (EST)

Researchers from Argonne and Texas A&M University will present a technical paper on a new data-driven diagnostic tool called Gauge that can be used to explore the latent space of supercomputing job features, understand behaviors of clusters of jobs, and identify and assess I/O bottlenecks.

CAB-MPI: Exploring Interprocess Work-Stealing towards Balanced MPI Communication

• Authors: Kaiming Ouyang, Min Si, Atsushi Hori, Zizhong Chen, Pavan Balaji
• Date: Tuesday, Nov. 17: 4–4:30 p.m. (EST)

Argonne researchers co-authored a paper that introduces CAB-MPI, an implementation of Message Passing Interface (MPI), as a tool for designing communication-balanced applications. Leveraging a work-stealing scheme based on process-memory-sharing techniques, CAB-MPI can identify and use idle MPI processes to dynamically balance the communication workload of an application.

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.

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 https://energy.gov/science.

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