PPAM 2017 - 12th International Conference on Parallel Processing and Applied Mathematics - PPAM 2017

The PPAM 2017 conference, twelfth in a series, will cover topics in parallel and distributed computing, including theory and applications, as well as applied mathematics. The focus will be on models, algorithms, and software tools which facilitate efficient and convenient utilization of modern parallel and distributed computing architectures, as well as on large-scale applications, including big data and machine learning problems.
PPAM is a biennial conference started in 1994, with the proceedings published by Springer in the Lecture Notes in Computer Sciences series. In 2017 the PPAM conference will take place in Lublin, the largest Polish city east of the Vistula River, an academic and cultural centre, proud of its rich history and picturesque Old Town.
The PPAM 2017 conference is organized by Czestochowa University of Technology together with Maria Curie-Skłodowska University (UMCS) in Lublin, under the patronage of Committee of Informatics of Polish Academy of Sciences, in technical cooperation with IEEE Computer Society and ICT COST Action IC1305 "Network for Sustainable Ultrascale Computing (NESUS)".
Topics of interest include, but are not limited to:
Parallel/distributed architectures, enabling technologies
Cluster and cloud computing
Multi-core and many-core parallel computing, GPU computing
Heterogeneous/hybrid computing and accelerators
Parallel/distributed algorithms: numerical and non-numerical
Scheduling, mapping, load balancing
Performance analysis and prediction
Performance issues on various types of parallel systems
Autotuning: methods, tools, and applications
Power and energy aspects of computation
Parallel/distributed programming
Tools and environments for parallel/distributed computing
Security and dependability in parallel/distributed environments
HPC numerical linear algebra
HPC methods of solving differential equations
Evolutionary computing, meta-heuristics and neural networks
Machine learning and HPC
HPC interval analysis
Applied Computing in mechanics, material processing, biology and medicine, physics, chemistry, business, environmental modeling, etc.
Applications of parallel/distributed computing
Methods and tools for parallel solution of large-scale problems, including big data and machine learning applications
Neuromorphic computing
KEYNOTE SPEAKERS (TENTATIVE LIST)
Rosa Badia Barcelona Supercomputing Center, Spain
Franck Capello Argonne National Laboratory, USA
Cris Cecka NVIDIA & Stanford University, USA
Jack Dongarra University of Tennessee and ORNL, USA
Thomas Fahringer University Innsbruck, Austria
Dominik Göddeke University of Stuttgart, Germany
William Gropp University Illinois Urbana-Champaign, USA
Georg Hager University Erlangen-Nurnberg, Germany
Alexey Lastovetsky University College Dublin, Ireland
Satoshi Matsuoka Tokyo Institute of Technology, Japan
Karlheinz Meier University of Heidelberg, Germany
Manish Parashar Rutgers University, USA
Jean-Marc Pierson University Paul Sabatier, France
Bronis R. de Supinski Lawrence Livermore National Laboratory, USA
Uwe Schwiegelshohn TU Dortmund University, Germany
Boleslaw K. Szymanski Rensselaer Polytechnic Institute, USA
Michela Taufer University of Delaware, USA
Andrei Tchernykh CICESE Research Center, Mexico
Jeffrey Vetter ORNL & Georgia Tech, USA