Conference Information
PPAM 2019: Internation Conference on Parallel Processing and Applied Mathematics
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Bialystok, Poland
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The PPAM 2019 conference, thirteenth 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 artificial intelligence 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 2019 the PPAM conference will take place in Bialystok, the largest city in northeastern Poland, academic and cultural centre, located close to the world-famous Bialowieza Forest. 

The PPAM 2019 conference is organized by the Department of Computer and Information Science of the Czestochowa University of Technology together with the Bialystok University of Technology, under the patronage of the Committee of Informatics of the Polish Academy of Sciences, in technical cooperation with the IEEE Computer Society and IEEE Computational Intelligence Society.

Topics of interest include, but are not limited to:

    Parallel/distributed architectures, enabling technologies
    Cluster, cloud and fog 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
    Auto-tuning and auto-parallelisation: 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, artificial intelligence 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 artificial intelligence and machine learning applications
    Neuromorphic computing
Last updated by Dou Sun in 2018-12-01
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