Conference Information
PDCAT 2020: International Conference on Parallel and Distributed Computing, Applications and Technologies
http://hpcc.siat.ac.cn/meeting/pdcat-paap2020/index.html
Submission Date:
2020-11-10 Extended
Notification Date:
2020-11-24
Conference Date:
2020-12-28
Location:
Shenzhen, China
Years:
21
CORE: b   QUALIS: b2   Viewed: 20623   Tracked: 18   Attend: 5

Conference Location
Call For Papers
We are inviting new and unpublished papers on, but not limited to, the following topics:

Networking and 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 and Applications:
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 2020-10-18
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