IEEE/ACM SC 2019 : IEEE/ACM International Conference on High Performance Computing, Networking, Storage and Analysis
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
X. Sherry Li, Lawrence Berkeley National Laboratory
Hatem Ltaief, King Abdullah University of Science and Technology
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; Halicioglu
Data Science Institute, UC San Diego
Gabriel Antoniu, French Institute for Research in Computer Science and
Data Analytics, Visualization & Storage
John Bent, DataDirect Networks
Suzanne McIntosh, New York University, Courant Institute of Mathematical
Machine Learning and HPC
Maryam Mehri Dehnavi, University of Toronto
Robert Patton, Oak Ridge National Laboratory
Lauren L. Smith, National Security Agency
Nathan Tallent, Pacific Northwest National Laboratory
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
Patrick Bridges, University of New Mexico
Dilma Da Silva, Texas A&M University
Full committee at