[hpc-announce] Submission Extension: Call for Papers: PAW17- The 2nd Annual PGAS Applications Workshop

Wed Jul 26 09:36:50 CDT 2017

CALL FOR PAPERS (Submission Extension)

PAW17: The 2nd Annual PGAS Applications Workshop
http://sourceryinstitute.github.io/PAW/

Held in conjunction with SC 17:  The International Conference for
High Performance Computing, Networking, Storage and Analysis
http://sc17.supercomputing.org<http://sc17.supercomputing.org/>

In cooperation with SIGHPC

SUMMARY

The race towards Exascale computing is on, and a lot of stress is put
on researchers to break the boundaries of productivity and efficiency
imposed by traditional programming models.
Partitioned Global Address Space (PGAS) languages are an
effective alternative, and the most promising path towards
sustainable programming environments for exascale machines.
Languages such as UPC, Fortran, Chapel, and X10 are now more widely
available than ever, thanks to increased support from vendors and
open-source communities. PGAS models also take the form of
meta-languages and libraries, such as Unified Parallel C++ (UPC++),
Co-Array C++, OpenSHMEM, MPI-3 and Global Arrays.
These have the benefit of being integrated with existing languages,
simplifying the learning curve for existing programmers.

Significant improvements in the availability of PGAS compilers and
support software have been achieved in the last few years; these
open up more opportunities than ever for researchers
and developers to test new strategies and port applications to more
demanding requirements.

Following on the success of PAW16, we invite you to take part
in the second PGAS Application Workshop, and to join
its vibrant and diverse community of researchers and developers.

SCOPE AND AIMS

The scope of the PAW workshop is to provide a forum for exhibiting
case studies of PGAS programming models in the context of real-world
applications as a means of better understanding practical
applications of PGAS technologies.  We encourage the submission of
papers and talks detailing practical PGAS applications, including
characterizations of scalability and performance, of expressiveness
and programmability, as well as any downsides or areas for
improvement in existing PGAS models. In addition to informing other
application programmers about the potential that is available through
PGAS programming, the workshop is designed to communicate these
experiences to compiler vendors, library developers, and system
architects in order to achieve broader support for PGAS programming
across the community.

We also specifically encourage submissions covering big data
analytics, deep learning, and other novel and emerging application
areas, beyond traditional scientific HPC domains.

Topics include, but are not limited to:

* Novel application development using the PGAS model.
* Real-world examples demonstrating performance, compiler
  optimization, error checking, and/or reduced software complexity.
* Applications from big data analytics, bioinformatics, and other
  novel areas.
* Performance evaluation of applications running under PGAS.
* Algorithmic models enabled by PGAS model.
* Compiler and runtime environments.
* Libraries using/supporting PGAS and applications.
* Benefits of hardware abstraction and data
  locality on algorithm implementation.

IMPORTANT DATES:
  * Submission Deadline: August 15, 2017 (Final submission extension)
  * Author Notification: September 15, 2017
  * Camera Ready:        October 1, 2017
  * Workshop Date:       November 13, 2017

SUBMISSIONS

Submissions are solicited in two categories:

  a) Full-length papers presenting novel research results:
    Full-length papers will be published in the workshop proceedings in
    cooperation with SIGHPC. Submitted papers must be original work that
    has not appeared in, and is not under consideration for, another
    conference or a journal. Papers shall not exceed eight (8) pages
    including text, appendices, and figures. References are not included.

  b) Extended abstracts summarizing published/preliminary results:

    Extended abstracts will be evaluated separately and will
    not be included in the published proceedings; they are intended
    for timely communications of novel work that is going to be
    formally submitted elsewhere at a later stage, and/or of already
    published work that is nonetheless deemed appropriate for
    dissemination in this venue.
    Extended abstracts shall not exceed four (4) pages.
Submissions shall be submitted through EasyChair
(https://easychair.org/conferences/?conf=paw17); they must conform to
ACM Guidelines (http://www.acm.org/publications/proceedings-template).
Accepted full-length papers will be given longer presentation
slots at the workshop; extended abstracts will be given shorter time
slots.

WORKSHOP CHAIR
Karla Morris - Sandia National Laboratories

ORGANIZING COMMITTEE
  * Bradford L. Chamberlain - Cray Inc.
  * Salvatore Filippone - Cranfield University
  * Costin Iancu - Lawrence Berkeley National Laboratory
  * Bill Long - Cray Inc.

PROGRAM COMMITTEE CHAIR and CO-CHAIR
  * Francesco Rizzi - Sandia National Laboratories (Chair)
  * Bill Long - Cray Inc. (Co-Chair)

PROGRAM COMMITTEE
  * Bradford L. Chamberlain - Cray Inc.
  * Bert de Jong - Lawrence Berkeley National Laboratory
  * James Dinan - Intel
  * Salvatore Filippone - Cranfield University, UK
  * Jeff Hammond - Intel
  * Costin Iancu - Lawrence Berkeley National Laboratory
  * Karla Morris - Sandia National Laboratories
  * Nicholas Park - U.S. Department of Defense
  * Anton Shterenlikht - University of Bristol
  * Min Si - Argonne National Laboratory
  * Lauren L. Smith - U.S. Department of Defense
  * Yili Zheng - Google

ADVISORY COMMITTEE
  * Katherine A. Yelick - Lawrence Berkeley National Laboratory
  * Damian W. I. Rouson - Sourcery Institute

In case of questions please email us at: paw17 at cranfield.ac.uk<mailto:paw17 at cranfield.ac.uk>

-----------------------

Lauren L. Smith

HPC Computer Science Researcher

lauren.l.smith18.civ at mail.mil<mailto:lauren.l.smith18.civ at mail.mil>

240-373-6489

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