Información de la conferencia

PPAM 2026: Internation Conference on Parallel Processing and Applied Mathematics

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Día de Entrega:
2026-04-24 Cerrado
Fecha de Notificación:
2026-05-31
Fecha de conferencia:
2026-08-30
Ubicación:
Poznan, Poland
Ediciones:
16
Vistas: 22033   Seguidores: 3   Asistentes: 0
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Índice Conference Partner (CP-I)

50,6 / 100
Puesto n.º 1.557 de 5.761 congresos · 28% superior

N.º 11 de 71 en Matemáticas y ciencias físicas N.º 153 de 341 en Sistemas y arquitectura

Reconocimiento académico (35%) Sin datos: se puntúa con la línea base neutra de 50 —
Selectividad en la revisión (20%) Sin datos: se puntúa con la línea base neutra de 50 —
Ediciones celebradas (20%)
76
Atención de la comunidad (10%)
25
Integridad del registro público (15%)
35

Datos utilizados: Ediciones documentadas: 16 · Investigadores que lo siguen aquí: 3 · Investigadores que abrieron esta página en los últimos 24 meses: 4

Falta en el registro público: Tasas de aceptación históricas (+4,5) · Ediciones anteriores (+3,0) · Premios al mejor artículo (+2,3)
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Solicitud de Artículos

PPAM 2026 (Internation Conference on Parallel Processing and Applied Mathematics) is an academic conference held in Poznan, Poland on 2026-08-30. The paper submission deadline is 2026-04-24. Acceptance notifications are sent on 2026-05-31.

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
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