PPT-Daniel Udwary NERSC Data Science Engagement Group

June 22 2016 TaskFarmer Task arrays Task array a scheduled job with multiple independent steps or tasks Why use a task array Often especially in bioinformatics you need to do repetitive analysis on many inputs. Common Defenses. Sometimes you accepted that treatments were placebos, but argued that that was OK:. Sometimes there are no other treatments.. If it works, it doesn’t matter how.. There are no side-effects, so taking a placebo can be better than taking “real” medicine.. Customer Data. July . 26, . 2016. Presentation material does not represent the views of the Joint Utilities Engagement Group as the group is still in discussion on these topics. Please do not redistribute this deck. . Group. June 2, . 2016. Presentation material does not represent the views of the Joint Utilities Engagement Group as the group is still in discussion on these topics. Please do not redistribute this deck. . . Tony Wildish. NERSC Data Science Engagement Group. Where is . Genepool. headed, and why do containers matter?. Why are we here?. Didn’t we just do a container training?. Yes! . Link to . Docker. NERSC Data Science Engagement Group. Anaconda. What is Anaconda and why will it make . your computing life better. ?. Anaconda . started as an . open-source distribution of Python. . But it’s also expanded to cover other languages and software. Peter Nugent (LBNL/UCB). Level 3 Manager. 1. Outline. Overview of. Deliverables, Input Data & Processing . Description of NERSC. Targeting . Database. Scalability. Traceability. Development Plan, Schedule & Milestones. Berkeley Lab (LBNL). HEP workflows at NERSC. pre-GDB on HPC Utilization. February 2. nd. 2018. Outline. Introduction to NERSC. HEP challenges running on NERSC HPC and NERSC attempts to address. Software . , Shane Cannon, Lisa Gerhardt. , . Rei. Lee, Mustafa . Mustafa and others . @. NERSC. . Berkeley Lab (LBNL). Enabling production HEP workflows on Supercomputers at . NERSC. CHEP . 2018. July 9. th. April 29, 2014. NERSC Data Services . Update and Plans (Draft). - . 1. -. Data intensive science is a growing part of the NERSC workload. - . 2. -. Data intensive science requires different services and expertise.. Putting the people who use our courts and tribunals at the heart of reform is fundamental for redesigned services. . One of the ways that we are doing this, as well as listening directly to people who use our services, is through a network of organisations. Jason Zurawski – . zurawski@es.net. . Science Engagement Engineer, . ESnet. Lawrence Berkeley National Laboratory. Southern Partnership in Advanced . Networking. April . 9. th. , . 2015. 2. – ESnet Science Engagement (. JUNE 9 - 12, 2020. BERKELEY, CALIFORNIA. Hosted by LBNL/NERSC,. UC Berkeley Research IT,. and OpenSFS. NERSC's Perlmutter System:. Deploying 30 PB of all-. NVMe. Lustre at scale. Glenn K. . Lockwood. Sample Agenda. Session. Agenda Item. Time. Point Person. Session 1. Welcome, Agenda, Purpose, Objectives. 5 min. Facilitator. Why is Member Engagement Important?. Theory of Engagement Presentation. 15 min. Customer Data. Recap of the July 26, 2016 Meeting. Presentation material does not represent the views of the Joint Utilities Engagement Group as the group is still in discussion on these topics. Please do not redistribute this deck. .