ECSS Symposium

ECSS staff share technical solutions to scientific computing challenges monthly in this open forum.

The ECSS Symposium allows the over 70 ECSS staff members to exchange on a monthly basis information about successful techniques used to address challenging science problems. Tutorials on new technologies may be featured. Two 30-minute, technically-focused talks are presented each month and include a brief question and answer period. This series is open to everyone.

Symposium coordinates

Day and Time: Third Tuesdays @ 1 pm Eastern / 12 pm Central / 10 am Pacific
Add this event to your calendar. 
Note – Symposium not held in July and November due to conflicts with PEARC and SC conferences.

Webinar (PC, Mac, Linux, iOS, Android): Launch Zoom webinar

Meeting ID: 892 8873 8446

Password: 398208

One tap mobile

+13462487799,,89288738446# US (Houston)

+16027530140,,89288738446# US (Phoenix)


Find your local number to join by phone: https://illinois.zoom.us/u/konD1P8cl
 

Upcoming events are also posted to the Training category of XSEDE News.

Due to the large number of attendees, only the presenters and host broadcast audio. Attendees may submit chat questions to the presenters through a moderator.

To better secure Zoom meetings all participants are now required to log in to their Zoom account (personal or university/institution) in order to access any XSEDE meeting. If you do not currently have an account, you can create one at https://zoom.us/signup

Key Points
Monthly technical exchange
ECSS community present
Open to everyone
Tutorials and talks with Q & A
Contact Information

 

Previous years' ECSS seminars may accessed through these links:

May 17, 2022

Reworking inefficient workflows for shared HPC resources

Presenter(s): Mitchell Dorrell (Pittsburgh Supercomputing Center)

Sometimes major scientific advances arrive as beautifully packaged open source software implementations, ready to be used on any computing system in the world. Sometimes they don't. The world of protein folding has been changed by the arrival of new AI-centric algorithms that use databases of known sequence-to-structure relationships to predict previously-unknown structures with unprecedented accuracy. One of the most accomplished such algorithms is DeepMind's AlphaFold2, which is now publicly available under an open source license. As a service provider, we sought to install AlphaFold2 on our systems to make it more accessible to our users. In the process, we discovered that the workflow that DeepMind ships with AlphaFold2 is extremely inefficient when used on typical HPC resources. In this discussion, I will explain the approaches we are taking to enable our users to run AlphaFold2 as easily, but also efficiently, as possible.

A Historical Big Data Analysis To Study The Social Construction Of Juvenile Delinquency - Latest Progress

Presenter(s): Yu Zhang (CSU Fresno) Sandeep Puthanveetil Satheesan (NCSA) Bhavya (University of Illinois Urbana-Champaign) Adam Davies (University of Illinois Urbana-Champaign)

Social construction is a theoretical position that social reality is created through the human's definition and interaction. As one type of social reality, juvenile delinquency is perceived as part of social problems, deeply contextualized and socially constructed in American society. The social construction of juvenile delinquency started far earlier than the first juvenile court in 1899 in the US. Scholars have tried traditional historical analysis to explore the timeline of the social construction of juvenile delinquency in the past, but it is inefficient to examine hundred years of documents using traditional paper-and-pencil methods. Our project combines "big data" image and text analysis modules, using these tools to analyze hundreds of years of scanned newspaper images to better understand the historical social construction of juvenile delinquency in American society. This ECSS Symposium will provide an update of progress on this project since our last symposium. In the prior symposium we focused on the issues involved in OCR and in segmentation of historical newspaper collections. We have since made great progress in this area and have also added additional newspaper collections. We will provide an update on the OCR and segmentation issues, but primarily address the analyses of the resultant text data. We have applied a number of text analysis techniques including topic modeling, lexical analysis, and human-in-the-loop document classification.


April 19, 2022

A case study on deep learning for classification with imbalanced finance data

Presenter(s): Paul Rodriguez (San Diego Supercomputer Center)

Presentation Slides

Deep learning neural networks have become very important in machine learning and artificial intelligence applications but it is not so obvious how much neural networks will improve classification performance in applications with tabular data or sequential data. In this study we compare neural network performance to several standard machine learning models for classification with an imbalanced data sets with low rate of positive cases. We explore several neural network architecture options and consider methods and trade-offs in searching through hyperparameter space, as well as sampling or loss-weighting options. We find that although neural networks have robust and interesting performance, more deep layers do not show a big improvement in this data set, and shallow networks or other models are competitive.

Reworking inefficient workflows for shared HPC resources (Rescheduled for May 17)

Presenter(s): Mitchell Dorrell (Pittsburgh Supercomputing Center)

Sometimes major scientific advances arrive as beautifully packaged open source software implementations, ready to be used on any computing system in the world. Sometimes they don't. The world of protein folding has been changed by the arrival of new AI-centric algorithms that use databases of known sequence-to-structure relationships to predict previously-unknown structures with unprecedented accuracy. One of the most accomplished such algorithms is DeepMind's AlphaFold2, which is now publicly available under an open source license. As a service provider, we sought to install AlphaFold2 on our systems to make it more accessible to our users. In the process, we discovered that the workflow that DeepMind ships with AlphaFold2 is extremely inefficient when used on typical HPC resources. In this discussion, I will explain the approaches we are taking to enable our users to run AlphaFold2 as easily, but also efficiently, as possible.


March 15, 2022

Supporting HPC Research and Education With Open OnDemand

Presenter(s): Richard Lawrence (Texas A&M University)

Presentation Slides

Researchers using HPC resources face a steep learning curve when faced with new tools, technologies, and languages. This barrier to entry slows adoption of HPC best practices. A robust system of graphical, interactive user interfaces lowers the barrier. The Open OnDemand framework enables HPC sites to provide web-based graphical user interfaces. We present here some improvements that are possible in the OOD framework developed and deployed at TAMU and argue for the necessity of these and additional developments. The focus is on practical utility for researchers and easy maintenance for administrators.

Anvil - A National Composable Advanced Computational Resource for the Future of Science and Engineering

Presenter(s): Rajesh Kalyanam (Purdue University)

Presentation Slides

Anvil is a new XSEDE advanced capacity computational resource funded by NSF. Designed to meet the ever increasing and diversifying needs for advanced computational capacity, Anvil integrates a large capacity HPC system with a comprehensive ecosystem of software, access interfaces, programming environments, and composable services. Comprising a 1000-node CPU cluster featuring the latest AMD EPYC 3rd generation (Milan) processors, along with a set of 1TB large memory and NVIDIA A100 GPU nodes, Anvil integrates a multi-tier storage system, a Kubernetes composable subsystem, and a pathway to Azure commercial cloud to support a variety of workflows and storage needs. Anvil entered production in February 2022 and will serve the nation's science and engineering research community for five years. We will describe the Anvil system, its user-facing interfaces, and services, and share data and feedback from the recently concluded early user access program.


December 21, 2021

TaRget Enablement to Accelerate Therapy Development for Alzheimer's Disease (TREAT-AD)

Presenter(s): Rob Quick (Indiana University)

The National Institute on Aging describes Alzheimer's Disease (AD) as "a brain disorder that slowly destroys memory and thinking skills, and, eventually, the ability to carry out the simplest tasks." It ranks as the 6th leading cause of death in the US. The TaRget Enablement to Accelerate Therapy Development for Alzheimer's Disease (TREAT-AD) is a joint effort leveraging drug discovery expertise from the Indiana University School of Medicine (IUSoM), Purdue University, Emory University, and Sage Bionetworks. The goal of these NIH funded projects is to improve, diversify, and invigorate the Alzheimer's disease drug discovery pipeline. The IUSoM is responsible for the Bioinformatics and Computational Biology Core (BCBCore) and will be the focus of this symposium. The BCBCore (bcbportal.medicine.iu.edu) is implemented as a series of developmental science gateways that will be consolidated into a single production portal for AD Tools and Data. We will discuss the motivation and goals of the overarching project, demo an important AD research tool under development (AD Explorer), and discuss other various aspects of engaging this important research group as an XSEDE ECSS collaborator.


October 19, 2021

Campus Champions Short Presentations

Presenter(s): Suxia Cu (Prairie View A&M University) Kurt Showmaker (University of Mississippi Medical Center,) Zhiyong Zhang (Stanford University) Sinclair Im (Yale University)

Presentation Slides Image analysis for digital surrogates

Presentation Slides A density functional theory study

Presentation Slides Optimal utilization of XSEDE resources

The October Symposium will feature a series of short presentations (≤ 15 minutes) by four of the XSEDE 2020-21 Campus Champion Fellows. Speakers and titles are listed below, with additional details for their projects available on the 2020-21 announcements page.

Suxia Cui, Prairie View A&M University, Image analysis for digital surrogates of historical motion picture film
Kurt Showmaker, University of Mississippi Medical Center, A Comprehensive Annotator and Web Viewer for scRNA-seq Data
Zhiyong Zhang, Stanford University, Optimal Utilization of XSEDE Computing Resources for the NWChem Computational Chemistry Software Package
Sinclair Im, Yale University, A density functional theory study: quantum materials


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