[hpc-announce] CFP Special Issue: "High Performance Computing Solutions for Complex Problems" (deadline December 23rd)

Pedro Valero Lara pedro.valero.lara at gmail.com
Thu Nov 3 03:24:32 CDT 2016


(apologies for cross-postings)

Special Issue "High Performance Computing Solutions for Complex Problems"
http://www.scpe.org/index.php/scpe/pages/view/Call-issue-2-2017

Journal Scalable Computing: Practise and Experience
http://www.scpe.org/index.php/scpe/index

This journal is included in Thompson Reuters Emerging Sources Citation
Index, Google Scholar, DBLP, SNIP, SJR, ...
http://science.thomsonreuters.com/cgi-bin/jrnlst/jlresults.
cgi?PC=EX&ISSN=1895-1767
*High Performance Computing Solutions for Complex Problems*

In the last decades, the complexity of the current and upcoming
scientific/engineering problems has increased considerably. Computations
involved in numerical and physical simulations, molecular dynamics, fluid
dynamics, bio-informatics, image processing, deep-learning, information
retrieval, linear algebra or big-data computing are just a few examples of
such problems.

At the same time, improvements in high performance computing (HPC) systems
are mainly associated with an increased complexity of computer
architectures, resulting in increasing challenges in code optimization.
This results in an increasing gap between the general scientific /
engineering user community (in need of easy access to efficient high
performance computations) and the HPC programmers community (who design
codes for narrow sub-classes of problems). As a result, the development of
user-friendly codes for non-HPC-trained user community becomes a big
challenge. As a matter of fact, effective use of HPC centers requires
specialized / individual training for each user group. The aim of this
special issue report on efforts to reduce this gap by means of bringing new
ways to face the growing complexity of problems that are to be solved.
Specifically, we would like to address challenges involved in implementing
large and complex problems on current and upcoming platforms, composed of a
high number of computational cores. Thus, the issues to be addressed should
deal with communication, programming, heterogeneous architectures, load
balancing, benchmarking, etc. However, the overall goal should be
development of solutions that are going to be usable by non-HPC-trained
domain specialists.



*Topics:*

Authors are invited to submit manuscripts which present original and
unpublished research in all areas related with complex problems solving via
parallel and distributed processing, i.e., works focused on emerging
solutions to face with big computing challenges on HPC systems are
specially welcome. Relevant topics include, but are not limited to:

   - Benchmarking, performance and scalability of algorithms, data
   structures, tools.
   - Code adapting to take advantages of latest computational features.
   - New strategies to improve performance.
   - Auto-tuning computing systems.
   - New transparent, portable, and hardware diagnostic programming
   paradigms.
   - Advances in current or upcoming HPC platforms.
   - Communication, synchronization, load balancing.



*Important dates:*

   - *Submission: December 23, 2016*
   - Author notification: April 28, 2017
   - Revised papers: May 19, 2017
   - Author notification: June 2, 2017
   - Camera Ready papers due: June 16, 2017
   - Publication: June 30, 2017



*Submission guidelines:*

Original and unpublished works on any of the topics aforementioned or
related are welcome. The SCPE journal has a rigorous peer-reviewing process
and papers will be reviewed by at least three S.C. referees. All submitted
papers must be formatted according to the journal's instructions, which can
be found at:

http://www.scpe.org/index.php/scpe/about/submissions#authorGuidelines



*Special issue editors:*

   - Dr. Pedro Valero Lara, University of Manchester, UK.
   - Prof. Dr. Fernando L. Pelayo, University of Castilla-La Mancha, Spain.
   - Dr. Mawussi Zounon, University of Manchester, UK.
   - Dr. Maxim Abalenkov, University of Manchester, UK.
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