Aim & Scope

Azerbaijan Journal of High Performance Computing publishes articles that include the latest scientific and research findings in the field of knowledge edges or industrial activities in High Performance Computing Systems after two blind reviews and other related operations related to the referee process. The main goal of this journal is to publish up-to-date articles in the field of solving challenges associated with the management of High Performance Computing Systems and their applications in various industrial areas.

Besides the publication of articles by international authors, this journal publishes articles about the research of local authors, taking into account that ADNSU (in English as ASOIU) is the hub of High Performance Computing Systems in the Republic of Azerbaijan. The journal publishes articles related to all aspects of High Performance Computing Systems (Supercomputer, Cluster Computing, Peer Peer Computing, Grid, and Cloud Computing, Fog, Edge Computing, IoT Computing, and Exascale computing systems).

The journal welcomes research in the following areas:

High Performance Computing
  • Technology, innovation and new concepts
  • Architecture, systems, and frameworks for managing High Performance Computing Systems and Exascale Computing Systems or applying said systems in scientific and industrial fields
  • Algorithms, systematic approaches, languages, programs, and paradigms in the field of distributed and parallel computing
  • Criteria and methods for evaluating the performance of computing systems or the application of computing systems in various scientific fields
  • Simulation and emulation, data analysis
  • Application and system software for Scientific Computing and Big Data management
HPC and Artificial Intelligence / Machine Learning (since 2025)
  • Scalable and distributed machine learning on HPC platforms
  • AI for scientific discovery and industrial applications
  • Edge and Fog AI computing
  • AI-enhanced simulation, modeling, and big data analytics
  • Trustworthy and explainable AI in large-scale systems
  • Neuromorphic, quantum-inspired, and hardware-efficient AI
  • Integration of AI/ML techniques with distributed computing infrastructures

This journal provides instant and open access to content. We welcome your contributions and suggestions to this journal.