Folks, SC19 deadline is 4/10 (see the full announcement below). Important things to note: 1. PC members can submit any number of papers. There's no limit on submissions. 2. Single node papers are fine to submit. There's no special requirement for multi-node papers. It needs to be related to HPC, though, obviously. As you all already know, SC is one of the top conferences in our field and is highly valued at Argonne. Please forward this note to your students/postdocs as well, who might not be on this mailing list. -- Pavan Begin forwarded message: SC19: The International Conference for High Performance Computing, Networking, Storage, and Analysis November 17-22, 2019, Denver, Colorado, USA https://sc19.supercomputing.org/submit/paper-submissions/ March 1, 2019 - Submissions open April 2, 2019 - Deadline for abstracts April 10, 2019 - Full paper deadline (firm deadline, no extensions) 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; Halicioglu 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