Schedule
A full schedule will be released in early May.
For now, here is a preview of some of the talks and tutorials that will be featured at HTCondor Week 2018.
- Improving the Scheduling Efficiency and Scalability of a Global Multi-core HTCondor Pool in CMS
James Letts, Compact Muon Solenoid (CMS) - Integration of HTCondor and Google Cloud Platform
Karan Bhatia, Google - Using Condor and Singularity to run GPU jobs on the Titan Supercomputer
Vladimir Brik, Wisconsin IceCube Particle Astrophysics Center (WIPAC) - HTCondor in your backyard: modeling urban residential hydrology
Carolyn Voter, University of Wisconsin-Madison - HTCondor at CERN: Status and outlook
Helge Meinhard, CERN - What's new in HTCondor? What is upcoming?
Todd Tannenbaum, Center for High Throughput Computing - Using HTCondor to Calibrate and Archive HST and JWST Data
Matthew Burger, Space Telescope Science Institute - Changing Compute Landscape at Brookhaven
William Strecker-Kellogg, Brookhaven National Laboratory - Docker with HTCondor and FNAL
Anthony Tiradani, Fermilab National Accelerator Laboratory - Toil on HTCondor
Lon Blauvelt, University of California, Santa Cruz - Managing Caffe Machine Learning Jobs with HTCondor
Michael Pelletier, Raytheon - HTCondor with KRB/AFS at DESY interactive batch farm
Christoph Beyer, Deutsches Elektronen-Synchrotron (DESY) - Secure flocking with Docker
Kevin Hrpcek, UW-Madison Space Science and Engineering Center - Prioritizing vanille and grid jobs from local users on a Tier-3 condor cluster
Kenyi Hurtado Anampa, University of Notre Dame - HTCondor for machine learning in biology
Tony Gitter, UW-Madison, Morgridge Institute for Research - Automatic for the People: Containers for LIGO software development on the Open Science Grid and other diverse computing resources
Thomas Downes, University of Wisconsin-Milwaukee - HTCondor and VC3: Virtual Clusters for Community Computation
John Hover, Brookhaven National Laboratory - Credential Management in HTCondor
Zach Miller, Center for High Throughput Computing - High-Throughput Machine Learning from Electronic Health Records
Ross Kleiman, University of Wisconsin-Madison
