The PPAM 2026 conference, sixteenth in a series, will cover topics in parallel and distributed processing, 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. A special attention is expected to be given to the future of computing beyond Moore's Law, and AI/ML approaches in high performance computing.

PPAM is a biennial conference started in 1994, with the proceedings published by Springer in the Lecture Notes in Computer Sciences series. The PPAM Proceedings are indexed in main databases such as Web of Science, Scopus, and DBLP.

Next year, the PPAM conference is scheduled to take place in Poznan, the important industrial, academic and cultural center in the western part of Poland, one of four historical capitals of medieval Poland.

The PPAM 2026 conference is organized by the Department of Computer Science of the Czestochowa University of Technology together with the Poznan University of Technology, in technical cooperation with Poznań Supercomputing and Networking Center.

PPAM 2026 is expected to be an in-person conference with the possibility of remote participation. However, we strongly encourage in-person participation, as PPAM also has very compelling networking aspects and a social and cultural program. 

Topics of interest include, but are not limited to:

    Parallel/distributed architectures, enabling technologies
    Quantum computing and communication
    Neuromorphic computing
    Computing beyond Moore's Law
    Cluster, cloud, edge 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-parallelization: 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, with the special emphasis on LLMs
    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