[hpc-announce] Call For Papers: ACM HPDC 2017

Song, Shuaiwen Shuaiwen.Song at pnnl.gov
Mon Sep 26 19:38:43 CDT 2016

     **** CALL FOR PAPERS ****

                The 26th International ACM Symposium on

         High-Performance Parallel and Distributed Computing


              Washington D.C., United States - June 26-30, 2017


       Sponsored by ACM SIGARCH

The ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC) is the premier annual conference for presenting the latest research on the design, implementation, evaluation, and the use of parallel and distributed systems for high-end computing.

The 26th HPDC will take place in Washington DC on June 26-30, 2017 (workshops on June 26th and 27th, main conference on June 28th-30th.). Washington DC, the center of a metropolitan area (population 6,097,684) extending into Maryland and Virginia,  is the legislative, administrative, and judicial center of the United States.. The city is also a major tourist attraction and a cultural center, home of the National Gallery and the Smithsonian Institute.


Abstracts (required) due:  January 10, 2017

Full Papers due:              January 17, 2017 (no extensions)

Author notifications:         March 29th, 2017

Camera Ready:               April 12th, 2017

Conference:                    June 26~30, 2017


Submissions are welcomed on high-performance parallel and distributed computing (HPDC) topics including but not limited to: clouds, clusters, grids, big data, massively multicore, and extreme-scale computing systems. Submissions that focus on the operating systems, runtime environments, architectures, and networks of high end computing systems are particularly encouraged. Experience reports of operational deployments that provide significantly novel insights for future research on HPDC applications and systems will also receive special consideration. All papers will be evaluated for their originality, technical depth and correctness, potential impact, relevance to the conference, and quality of presentation. Research papers must clearly demonstrate research contributions and novelty, while experience reports must clearly describe lessons learned and demonstrate impact.

In the context of high-performance parallel and distributed computing, the topics of interest include, but are not limited to:

  *   Operating systems, networks, and architectures
  *   High performance runtime environments
  *   Massively multicore systems, including heterogeneous systems
  *   Datacenter technology, resource virtualization
  *   Programming languages, APIs, and system interoperation approaches
  *   File and storage systems, I/O, and data management
  *   Big data stacks and big data ecosystems
  *   Resource management and scheduling, including energy-aware techniques
  *   Performance modeling, analysis, and engineering
  *   Fault tolerance, reliability, and availability
  *   Operational guarantees, and risk assessment and management
  *   Traditional and emerging applications and services that depend upon high- end computing


Authors are invited to submit technical papers of at most 12 pages in PDF format, including figures and references. Papers should be formatted in the ACM Proceedings Style<http://www.acm.org/publications/article-templates/proceedings-template.html> and submitted via the conference web site. Accepted papers will appear in the conference proceedings, and will be incorporated into the ACM Digital Library. A limited number of papers will be accepted as posters.

Papers must be self-contained and provide the technical substance required for the program committee to evaluate their contributions. Submitted papers must be original work that has not appeared in and is not under consideration for another conference or a journal. See the ACM Prior Publication Policy for more details. Papers can be submitted at https://ssl.linklings.net/conferences/hpdc/.


Howie Huang,      George Washington University, USA

Jon Weissman,    University of Minnesota, USA

**** HPDC'17 Technical PROGRAM CO-CHAIRS ****

Adriana Iamnitchi,          University of South Florida, USA

Alexandru Iosup,             Delft University of Technology, The Netherlands


Rich Wolski,                            University of California at Santa Barbara, USA

Ana Lucia Varbanescu,           University of Amsterdam, The Netherlands


Radu Prodan,             University of Innsbruck, Austria


Shuaiwen Leon Song,     Pacific Northwest National Laboratory, USA

Torsten Hoefler,               Eidgenössische Technische Hochschule Zürich, Switzerland

Kenjiro Taura,               The University of Tokyo, Japan


Dean Hildebrand,     IBM Almaden Research Center


Patrick Bridges,        University of New Mexico, USA

**** HPDC'17 WEBMASTER ****

Zach Leidall,     University of Minnesota, USA

**** Program Committee (As of September 26th) ****

Sameer Al-Kiswany,     University of Waterloo, Canada

Gabriel Antoniu,           INRIA, Rennes, France

Henri Bal,                    Vrije University, The Netherlands

Michela Becchi,            University of Missouri, USA

Patrick Bridges,            University of New Mexico, USA

Ali Butt,                       Virginia Tech, USA

Franck Cappello,           Argonne National Laboratory, USA

Abhishek Chandra,       University of Minnesota, USA

Andrew A. Chien,          University of Chicago and Argonne National Laboratory, USA

Frederic Desprez,          INRIA, France

Peter Dinda,                 Northwestern University, USA

Dick Epema,                 Delft University of Technology, The Netherlands

Gilles Fedak,                 INRIA/ENS Lyon, France

Renato Figueiredo,        University of Florida, USA

Liana Fong,                   IBM, USA

Ada Gavrilovska,           Georgia Tech

Haryadi Gunawi,            University of Chicago, USA

Salim Hariri,                  University of Arizona, USA

David Irwin,                  University of Massachusetts at Amherst, USA

John (Jack) Lange,         University of Pittsburgh, USA

Adrien Lebre,                 French Institute for Research in Computer Science, France

Jay Lofstead,                 Sandia National Laboratories, USA

Arthur Maccabe,            Oak Ridge National Laboratory, USA

Satoshi Matsuoka,         Tokyo Inst. Technology, Japan

Alberto Montresor,         University of Trento, Italy

Christine Morin,             INRIA, France

Radu Prodan,                 University of Innsbruck, Austria

Matei Ripeanu,               University of British Columbia, Canada

Martin Schulz,                Lawrence Livermore National Lab, USA

Yogesh Simmhan,          Indian Institute of Science, India

Evgenia Smirni,              College of William and Mary, USA

Shuaiwen Leon Song,     Pacific Northwest National Lab, USA

Michela Taufer,               University of Delaware, USA

Kenjiro Taura,                 The University of Tokyo, Japan

Douglas Thain,                University of Notre Dame, USA

Ana Lucia Varbanescu,    University of Amsterdam, The Netherlands

Rich Wolski,                   University of California at Santa Barbara, USA

Ming Zhao,                    Arizona State University, USA

**** Steering Committee ****

Franck Cappello,        Argonne National Lab, USA and INRIA, France

Peter Dinda,               Northwestern University

Salim Hariri,               University of Arizona

Dean Hildebrand,       IBM Research Almaden

Jack Lange,                University of Pittsburgh

Arthur “Barney”  Maccabe     Oak Ridge National Lab

Manish Parashar,        Rutgers University

Kenjiro Taura,             The University of Tokyo, Japan

Michela Taufer,           University of Delaware

Douglas Thain,            University of Notre Dame

Jon Weissman,           University of Minnesota (Chair)

Dongyan Xu,             Purdue University

Shuaiwen Leon Song
Staff Research Scientist
High Performance Computing Group,
Advanced Computing, Mathematics, and Data Division,
Pacific Northwest National Lab (PNNL),
Washington, US,
Email: Shuaiwen.Song at pnnl.gov
HomePage: https://sites.google.com/site/shuaiwensongsresearch/

It is never too late to become what you might have been.       ^
-- George Elliot                                                                    ^
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