SC 2019 : Conference on High Performance Computing (Supercomputing)

  in Conferences   Posted on February 11, 2019

Conference Information

Submission Deadline Wednesday 10 Apr 2019 Proceedings indexed by :
Conference Dates Nov 19, 2019 - Nov 21, 2019
Conference Address Denver, United States
Conference & Submission Link https://sc19.supercomputing.org/
Conference Organizers : ( Deadline extended ? Click here to edit )

Conference Ranking & Metrics (This is a TOP Conference)

Guide2Research Overall Ranking: 240
Category Rankings
Hardware, Robotics & Electronics 38
Computational Theory and Mathematics 28
Software Engineering & Programming 64
Databases & Information Systems 64

Google Scholar H5-index:
21

Conference Call for Papers

The SC Papers program is the leading venue for presenting high-quality original research, groundbreaking ideas, and compelling insights on future trends in high performance computing, networking, storage, and analysis. Technical papers are peer-reviewed and an Artifact Description (to aid in reproducibility) is now mandatory for all papers submitted to SC19. Submissions will be considered on any topic related to high performance computing within the ten tracks below. A new track on machine learning and HPC has been added this year.

Algorithms: The development, evaluation and optimization of scalable, general-purpose, high performance algorithms.

Applications: The development and enhancement of algorithms, parallel implementations, models, software and problem-solving environments for specific applications that require high performance resources.

Architecture and Networks: All aspects of high-performance hardware including the optimization and evaluation of processors and networks.

Clouds and Distributed Computing: All software aspects of clouds and distributed computing that are related to HPC systems, including software architecture, configuration, optimization and evaluation.

Data Analytics, Visualization, and Storage: All aspects of data analytics, visualization, storage, and storage I/O related to HPC systems. Submissions on work done at scale are highly favored.

Machine Learning and HPC: The development and enhancement of algorithms, systems, and software for scalable machine learning utilizing high-performance and cloud computing platforms.

Performance Measurement, Modeling, and Tools: Novel methods and tools for measuring, evaluating, and/or analyzing performance for large scale systems.

Programming Systems: Technologies that support parallel programming for large-scale systems as well as smaller-scale components that will plausibly serve as building blocks for next-generation HPC architectures.

State of the Practice: All R&D aspects of the pragmatic practices of HPC, including operational IT infrastructure, services, facilities, large-scale application executions and benchmarks.

System Software: Operating system (OS), runtime system and other low-level software research & development that enables allocation and management of hardware resources for HPC applications and services.

Technical Program Chairs

Chair: Pavan Balaji, Argonne National Laboratory

Deputy Chair: Irene Qualters, Los Alamos National Laboratory

Vice Chair: Antonio J. Pena, Barcelona Supercomputing Center (BSC), Polytechnic University of Catalonia

Technical Papers Chairs

Scott Pakin, Los Alamos National Laboratory

Michelle Mills Strout, University of Arizona, Computer Science

Track Chairs

Algorithms

X. Sherry Li, Lawrence Berkeley National Laboratory

Hatem Ltaief, King Abdullah University of Science and Technology

Applications

Michael Bader, Technical University of Munich

Suzanne Shontz, University of Kansas

Architectures & Networks

Jonathan Beard, ARM Ltd

Brian Towles, D.E. Shaw Research

Clouds & Distributed Computing

Ilkay Altintas, San Diego Supercomputer Center, UC San Diego; Data Science Institute, UC San Diego

Gabriel Antoniu, French Institute for Research in Computer Science and Automation (INRIA)

Data Analytics, Visualization & Storage

John Bent, DataDirect Networks

Suzanne McIntosh, New York University, Courant Institute of Mathematical Sciences

Machine Learning and HPC

Maryam Mehri Dehnavi, University of Toronto

Robert Patton, Oak Ridge National Laboratory

Performance

Lauren L. Smith, National Security Agency

Nathan Tallent, Pacific Northwest National Laboratory

Programming Systems

Sriram Krishnamoorthy, Pacific Northwest National Laboratory

Xipeng Shen, North Carolina State University

State of the Practice

Sadaf R. Alam, Swiss National Supercomputing Centre

Wu Feng, Virginia Tech

System Software

Patrick Bridges, University of New Mexico

Dilma Da Silva, Texas A&M University

Full committee at

https://sc19.supercomputing.org/planning-committee/#Technical%20Program

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