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.
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.
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.
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.
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.
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.
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.
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.
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.