Conference Information
PAAP 2021: International Symposium on Parallel Architectures, Algorithms and Programming
http://www.paap2021.org/
Submission Date:
2021-09-30
Notification Date:
2021-10-30
Conference Date:
2021-12-10
Location:
Xi an, China
Years:
12
QUALIS: b5   Viewed: 20847   Tracked: 10   Attend: 7

Call For Papers
We are inviting new and unpublished papers on, but not limited to, the following topics: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
Last updated by Dou Sun in 2021-09-21
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