会議情報

PAAP 2022: International Symposium on Parallel Architectures, Algorithms and Programming

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投稿締切日:
2022-07-30
通知日:
2022-09-30
開催日:
2022-11-04
開催地:
Beijing, China
開催回数:
13
QUALIS: b5   閲覧: 42595   フォロー: 12   参加: 10

論文募集

PAAP 2022 (International Symposium on Parallel Architectures, Algorithms and Programming) is a QUALIS B5 conference held in Beijing, China on 2022-11-04. The paper submission deadline is 2022-07-30. Acceptance notifications are sent on 2022-09-30.

We are inviting new and unpublished papers on, but not limited to, the following topics: Architectures: Multi/many-core architectures Interconnection networks Cluster, grid and cloud computing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 Architectural support for programming language or software development 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 Workshop: Topic: Security and privacy of federated learning Requirement: The format of the paper is the same as the main conference, except that its type is selected as "workshop" when submitting.
最終更新：Dou Sun（）

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関連ジャーナル

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