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Suggested Citation: "Appendix A Workshop Agenda and Participants List." National Academies of Sciences, Engineering, and Medicine. 2018. Opportunities from the Integration of Simulation Science and Data Science: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25199.

A

Workshop Agenda and Participants List

AGENDA

May 10, 2018
Keck Center, Washington, DC

8:30 a.m.	Breakfast available
9:15	Context Setting
9:15	William D. Gropp, University of Illinois at Urbana-Champaign Robert J. Harrison, Stony Brook University
9:30	Science Drivers and Needs
	What are the trends and frontiers in simulation and data-driven science? What is the distribution of needs for both across the science community? How well are current needs being met by today’s facilities and services? What new workloads are emerging (e.g., deep learning or graph analytics) and what architectures can (best) support them?
	Speakers: Alexander Szalay, The Johns Hopkins University Eliu Huerta, University of Illinois at Urbana-Champaign (remotely) Saurabh Sinha, University of Illinois at Urbana-Champaign (remotely) Shyue Ping Ong, University of California, San Diego (remotely)
11:00	Envisioning a Cyberinfrastructure Ecosystem for an Era of Extreme Compute and Big Data
11:00	Manish Parashar, National Science Foundation

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11:30	Architectural Landscape and Trends
	How have high-performance computing and data-centric computing architectures been evolving and where are they heading? What science and nonscience applications are driving their evolution? Where are the points of convergence and divergence? Specific issues Memory. Currently seeing wide array of memory approaches (vs. past parameters of cache size and division). Not clear whether the memory needs are the same for simulation and data-intensive workloads. File Systems. For example, traditional HPC file systems are widely considered inappropriate even for HPC applications. What are more appropriate file systems that could support both? Lessons from HPC side for data side? How are the associated software stacks evolving? To what extent are they converging?
	Speakers: Pete Beckman, Argonne National Laboratory David Konerding, Google, Inc. Sanjay Padhi, Amazon Web Services
12:30 p.m.	Lunch
1:30	Service, Usage Models, and Economics
	What are the characteristics and implications of different usage models—such as remote access, colocated computing, and community compute over data as a whole? What are the characteristics and implications of different service models—such as allocated time on HPC systems and purchased on-demand cloud services? What metrics should be used to evaluate different usage and service models? For example, cost/performance, throughput, and researcher productivity.
	Speakers: Roger Barga, Amazon Web Services Thomas Furlani, University at Buffalo Vani Mandava, Microsoft Research (remotely)
2:30	Data Storage and Retention What are characteristics and implications of different data usage models, e.g., continuous online vs. episodic access and local vs. remote storage? In particular, what are the implications in terms of cost and future infrastructure needs? How should researchers, funders, and institutions make decisions about what data to retain? What are the implications for future platforms?
2:30	Speakers: Robert Ross, Argonne National Laboratory (remotely) Robert Grossman, University of Chicago

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3:30	Convergence Opportunities and Limits
	What are the tradeoffs between more specialized and more general architectures? How can lessons learned in the HPC and data-centric world be used to inform development in the other? How much leverage does the scientific community have to drive future architectures? To what extent do architectural decisions affect what science you can do? What are some plausible directions for convergence?
	Speakers: Daniel Reed, University of Utah Michela Taufer, University of Tennessee, Knoxville Douglas Kothe, Oak Ridge National Laboratory
4:30	Wrap-Up Panel
5:30	Adjourn

PARTICIPANTS LIST

Roger Barga, Amazon Web Services

Chaitanya Baru, National Science Foundation

Pete Beckman, Argonne National Laboratory

Vipin Chaudhary, National Science Foundation

Thomas Furlani, University at Buffalo

William Gropp, University of Illinois at Urbana-Champaign

Robert Grossman, University of Chicago

Robert Harrison, Stony Brook University

Thuc Hoang, National Nuclear Security Administration

Eliu Huerta, University of Illinois at Urbana-Champaign

David Konerding, Google, Inc.

Douglas Kothe, Oak Ridge National Laboratory

Ji Hyun Lee, Networking and Information Technology Research and Development

Vani Mandava, Microsoft Research

Sanjay Padhi, Amazon Web Services

Manish Parashar, National Science Foundation

Shyue Ping Ong, University of California, San Diego

Daniel Reed, University of Utah

Robert Ross, Argonne National Laboratory

Barry Schneider, National Institute of Standards and Technology

Saurabh Sinha, University of Illinois at Urbana-Champaign

Alexandar Szalay, Johns Hopkins University

Michela Taufer, University of Tennessee, Knoxville

Ed Walker, National Science Foundation

Wendy Wigen, Networking and Information Technology Research and Development

Opportunities from the Integration of Simulation Science and Data Science: Proceedings of a Workshop (2018)

Chapter: Appendix A Workshop Agenda and Participants List

A

Workshop Agenda and Participants List

AGENDA

PARTICIPANTS LIST

My Academies

Opportunities from the Integration of Simulation Science and Data Science: Proceedings of a Workshop (2018)

Chapter: Appendix A Workshop Agenda and Participants List

A Workshop Agenda and Participants List

AGENDA

PARTICIPANTS LIST

A

Workshop Agenda and Participants List