Parallel Applications Workshop, Alternatives To MPI+X

Parallel Applications Workshop, Alternatives To MPI+X (PAW-ATM)
in conjunction with SC 2019
in cooperation with TCHPC
Denver, USA
November 17, 2019

IMPORTANT DATES:
Submission Deadline: August 15, 2019
Author Notification: September 15, 2019
Camera Ready: October 1, 2019

Supercomputers are becoming increasingly complex due to the prevalence of hierarchy and heterogeneity in emerging node and system architectures. As a result of these trends, users of conventional programming models for scalable high-performance applications increasingly find themselves writing applications using a mix of distinct programming models—such as Fortran90, C, C++, MPI, OpenMP, and CUDA—which are also often becoming more complex and detail-oriented themselves. These trends negatively impact the costs of developing, porting, and maintaining HPC applications.

Meanwhile, new programming models and languages are being developed that strive to improve upon the status quo. This is accomplished by unifying the expression of parallelism and locality across the system, raising the level of abstraction, making use of modern language design features, and/or leveraging the respective strengths of programmers, compilers, runtimes, and operating systems. These alternatives may take the form of parallel programming languages (e.g., Chapel, Fortran 2018, Julia, UPC), frameworks for large-scale data processing and analytics (e.g., Spark, Tensorflow, Dask), or libraries and embedded DSLs that extend existing languages (e.g., Legion, COMPSs, SHMEM, HPX, Charm++, UPC++, Coarray C++, Global Arrays).

The PAW-ATM workshop is designed to explore the expression of applications in scalable parallel programming models that serve as an alternative to the status quo. It is designed to bring together
applications experts and proponents of high-level programming models to present concrete and practical examples of using such alternative models and to illustrate the benefits of high-level approaches to
scalable programming.

The PAW-ATM workshop is designed as a forum for exhibiting studies of parallel applications developed using high-level parallel programming models serving as alternatives to MPI+X-based programming. We
encourage the submission of papers and talks that describe practical distributed-memory applications written using alternatives to MPI+X, and include characterizations of scalability and performance, expressiveness and programmability, as well as any downsides or areas for improvement in such models. Our hope is to create a forum in which architects, language designers, and users can present, learn about, and discuss the state of the art in alternative scalable programming models while also wrestling with how to increase their effectiveness and adoption. Beyond well-established HPC scientific simulations, we also encourage submissions exploring artificial intelligence, big data analytics, machine learning, and other emerging application areas.

Topics include, but are not limited to:
– Novel application development using high-level parallel programming languages and frameworks
– Examples that demonstrate performance, compiler optimization, error checking, and reduced software complexity
– Applications from artificial intelligence, data analytics, bioinformatics, and other novel areas
– Performance evaluation of applications developed using alternatives to MPI+X and comparisons to standard programming models
– Novel algorithms enabled by high-level parallel abstractions
– Experience with the use of new compiler and runtime environments
– Libraries using or supporting alternatives to MPI+X
– Benefits of hardware abstraction and data locality on algorithm implementation

SUBMISSION GUIDELINES:
Submissions are solicited in two categories:

1) Full-length papers presenting novel research results:
Full-length papers will be published in the workshop proceedings. Submitted papers must describe original work that has not appeared in, nor is under consideration for, another conference or journal. Papers shall be eight (8) pages minimum and not exceed ten (10) including text, appendices, and figures. Appendix pages related to the reproducibility initiative dependencies, namely the Artifact Description (AD) and Artifact Evaluation (AE), are not included in the page count.

2) Extended abstracts summarizing preliminary/published results:
Extended abstracts will be evaluated separately and will not be included in the published proceedings; they are intended to propose timely communications of novel work that will be formally submitted elsewhere at a later stage, and/or of already published work that would be of interest to the PAW-ATM audience in terms of topic and timeliness. Extended abstracts shall not exceed four (4) pages.

When deciding between submissions with similar merit, ties will be broken by giving weight to full-length paper submissions over extended abstracts. In addition, submissions whose focus relates more directly to the key themes of the workshop (application studies, computing at scale, high-level alternatives to MPI+X) will be given priority over those that don’t.

Submissions shall be submitted through Linklings: https://submissions.supercomputing.org

Submissions must use 10pt font in the IEEE format: https://www.ieee.org/conferences/publishing/templates.html

PAW-ATM follows the reproducibility initiative of SC19. For more information, please refer to: https://sc19.supercomputing.org/submit/reproducibility-initiative/
http://sourceryinstitute.github.io/PAW/

ORGANIZERS:
Workshop Chair:
Karla Morris – Sandia National Laboratory

Organizing Committee:
Rosa M. Badia – Barcelona Supercomputing Center
Bradford L. Chamberlain – Cray Inc.
Sean Treichler – NVIDIA

Program Committee Chairs:
Bill Long – Cray Inc.
Francesco Rizzi – NexGen Analytics

Program Committee:
Olivier Aumage – Inria
Rosa M. Badia – Barcelona Supercomputing Center
Vicenç Beltran – Barcelona Supercomputing Center
John Biddiscombe – CSCS Swiss National Supercomputing Centre
Bradford L. Chamberlain – Cray Inc.
Salvatore Filippone – Cranfield University
Marta G. Gasulla – Barcelona Supercomputing Center
Magne Haveraaen – University of Bergen
Costin Iancu – Lawrence Berkeley National Laboratory
Laxmikant Kale – University of Illinois
Karla Morris – Sandia National Laboratories
Bill Long – Cray Inc.
Swaroop S. Pophale – Oak Ridge National Laboratory
Jason Riedy – Georgia Institute of Technology
Francesco Rizzi – NexGen Analytics
Mitsuhisa Sato – RIKEN Advanced Institute for Computational Science
Elliott Slaughter – SLAC National Accelerator Laboratory
Sean Treichler – NVIDIA

Advisory Committee:
Salvatore Filippone – Cranfield University
Damian W. I. Rouson – Sourcery Institute
Katherine A. Yelick – Lawrence Berkeley National Laboratory