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CALL FOR PAPERS

First International Workshop on Software Correctness for HPC
Applications (Correctness 2017)

In conjunction with SC17: The International Conference for High
Performance Computing, Networking, Storage and Analysis, November 12,
2017, Denver, Colorado, USA. In cooperation with SIGHPC.

https://correctness-workshop.github.io/2017/
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Scope
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Ensuring correctness in high-performance computing (HPC) applications
is one of the fundamental challenges that the HPC community faces
today. While significant advances in verification, testing, and
debugging have been made to isolate software errors (or defects) in the
context of non-HPC software, several factors make achieving correctness
in HPC applications and systems much more challenging than in general
systems software: growing heterogeneity (architectures with CPUs, GPUs,
and special purpose accelerators), massive scale computations (very
high degree of concurrency), use of combined parallel programing models
(e.g., MPI+X), new scalable numerical algorithms (e.g., to leverage
reduced precision in floating-point arithmetic), and aggressive
compiler optimizations/transformations are some of the challenges that
make correctness harder in HPC.

As the complexity of future architectures, algorithms, and applications
in HPC increases, the ability to fully exploit exascale systems will be
limited without correctness. With the continuous use of HPC software to
advance scientific and technological capabilities, novel techniques and
practical tools for software correctness in HPC are invaluable.

The goal of the Correctness Workshop is to bring together researchers
and developers to present and discuss novel ideas to address the
problem of correctness in HPC. The workshop will feature contributed
papers and invited talks in this area.

Topics
======

Topics of interest include, but are not limited to:

* Formal methods and rigorous mathematical techniques for correctness
in HPC applications/systems
* Frameworks to address the challenges of testing complex HPC
applications (e.g., multiphysics applications)
* Approaches for the specification of numerical algorithms with the
goal of correctness checking
* Error identification in the design and implementation of numerical
algorithms using finite-precision floating point numbers
* Static and dynamic analysis to test and check correctness in the
entire HPC software ecosystem
* Practical and scalable tools for model checking, verification,
certification, or symbolic execution
* Analysis of error propagation and error handling in HPC libraries
* Techniques to control the effect of non-determinism when debugging
and testing HPC software
* Scalable debugging solutions for large-scale HPC applications
* Predictive debugging and testing approaches to forecast the
occurrence of errors in specific conditions
* Machine learning and anomaly detection approaches for bug detection
and localization
* Metrics to measure the degree of correctness of HPC
applications/systems
* Community-wide models to share past successes (e.g., bug report
databases, reproducible test cases)

Dates
=====

Paper submissions due: August 18, 2017
Notification of acceptance: September 15, 2017
Camera-ready papers due (firm): October 6, 2017
Workshop: SC 2017, Sun, Nov 12 (at 9am-12:30pm), 2017

Organizers
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Ignacio Laguna, LLNL
Cindy Rubio-González, UC Davis

Program Committee
=================

David Abramson, The University of Queensland, Australia
Eva Darulova, MPI-SWS, Germany
Alastair Donaldson, Imperial College London, UK
Ganesh Gopalakrishnan, University of Utah, USA
Paul Hovland, ANL, USA
Costin Iancu, LBNL, USA
Sriram Krishnamoorthy, PNNL, USA
David Lecomber, Allinea/ARM, UK
Richard Lethin, Reservoir Labs, Yale University, USA
Matthias Müller, RWTH Aachen University, Germany
Feng Qin, The Ohio State University, USA
Nathalie Revol, INRIA - ENS de Lyon, France
Koushik Sen, UC Berkeley, USA
Stephen Siegel, University of Delaware, USA
Armando Solar-Lezama, MIT, USA

Contact
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Please address workshop questions to Ignacio Laguna (ilaguna@llnl.gov)
and/or Cindy Rubio-González (crubio@ucdavis.edu).