Correctness 2017

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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

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Topics of interest include, but are not limited to:

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

Dates

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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

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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).