The 9th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP'18)
Taipei Taiwan, December 26-28, 2018


Accepted and selected papers will be published in special issues of the following SCI indexed journals:
1.Telecommunication Systems
2.International Journal of Ad Hoc and Ubiquitous Computing

Important Dates
Paper submission Deadline:July 10 September 13, 2018
Notification acceptance :September 23, 2018
Camera-ready paper and registration:September 30, 2018
Conference :Dec. 26-28, 2018

    Following the successful PAAP’08 in Hefei, PAAP’09 in Nanning, PAAP’10 in Dalian, and PAAP’11 in Tianjin, PAAP’12 in Taipei, PAAP’14 in Beijing, PAAP’15 in Nanjing, PAAP’17 in Haikou, the Nineth International Symposium on Parallel Architectures, Algorithms and Programming (PAAP’18) will be held in Taipei, Taiwan during December 26-28, 2018. The PAAP’18 aims at addressing advances in research on parallel computing and information security, covering topics from theoretic studies to technology issues and applications. The conference is to provide a forum for scientists and engineers in academia and industry to present their research results and development activities in all aspects of computer engineering and it also provides opportunities for the delegates to exchange new ideas and application experiences face to face, to establish business or research relations and to find global partners for future collaboration, especially. We devoutly wish participants to learn the basic theory of a gradual approach, practical experimental data, new business models, and the latest academic trends from this conference.
    PAAP’18 is sponsored by IEEE Computer Society (Taipei Section). The conference of PAAP’18 will be organized by the National Taiwan University of Science and Technology (NTUST). NTUST is located in Taipei city very close to the 101 building, the building ranked officially as the world's tallest from 2004 until the opening of the Burj Khalifa in Dubai in 2010; also it is in the vicinity of the National Palace Museum, which houses hundreds of thousands of Chinese antiques and art works.

We are inviting new and unpublished papers on, but not limited to, the following topics:
  • Architectures:
    Multi/many-core architectures
    Interconnection networks
    Cluster, grid and cloud computing systems
    Network-on-chip architectures
    Survivable and safety-critical systems
    Ubiquitous computing systems
    Sensor, wireless and RFID systems
    Reconfigurable architectures
    Self-healing, self-protecting network systems
  • Algorithms:
    Combinatorial and graph algorithms
    Numeric algorithms
    Task mapping and job scheduling
    Parallel/distributed databases and knowledge discovery
    High-performance scientific computing
    Resource allocation and management
    Power-aware Computing
    Secure distributed computing
    Network routing and traffic control
  • Parallel Programming:
    Multi/many-core programming
    Parallel programming theory and models
    Formal methods and verification
    Middleware for parallel systems
    Parallel programming languages
    Parallel compilers and runtime systems
    Performance analysis, debugging and optimization
    Parallel libraries and application frameworks
  • High Performance Systems:
    Operating systems for parallel/distributed systems
    High-performance computer arithmetic
    Memory hierarchy and caching
    Performance tuning, optimization and profiling
    Human-computer interaction in parallel/distributed systems
    Photonic and quantum computing
    Media computing in parallel/distributed systems
    Software engineering for parallel/distributed system
  • Privacy and Security:
    Cloud security
    Data privacy protection
    Cryptography
    Intrusion detection
    Copyright protection
    Access control
    Data provenance
    Trusted computing
  • Big Data Processing and Deep Learning:
    Mass data stream processing in clouds
    Big data models and computation theory
    Big data mining and fusion
    Dimension reduction for large data sets
    Big data placement, scheduling and optimization
    Multi-source data processing and integration
    Deep learning models
    Deep learning applications