Call for Papers
Papers should not exceed ten pages (including references) in the ACM SIG Conference format.
All accepted papers that are presented at the conference will be published in the ACM Digital Library.
See the main page in this site for the important deadlines.
Submission Link for PapersLinklings submission site
ICPP, the International Conference on Parallel Processing, provides a forum for engineers and scientists in academia, industry and government to present their latest research findings in all aspects of parallel and distributed computing. ICPP 2020 will be held in the campus of the University of Alberta in Edmonton, Alberta, Canada. ICPP is one of the oldest computer science conferences and is a premier venue for presenting the latest research on all aspects of parallel processing. Topics of interest in ICPP 2020 Papers include, but not limited to:
- Algorithms: Parallel and Distributed Algorithms, Parallel and Distributed Combinatorial & Numerical Methods, Scheduling Algorithms for Parallel and Distributed Applications and Platforms, Algorithmic Innovations for Parallel and Distributed Machine Learning
- Applications: Parallel and Distributed Applications, Scalable Data Analytics & Applied Machine Learning, Computational and Data-driven Science & Engineering
- Architecture: Micro Architecture for Parallel Computing, Parallel Computer Architecture and Accelerators Designs, Datacenter/Warehouse Computing Architecture, Machine Learning Architectures, Architecture Support for Networking, New Memory Technologies, Near-Memory Computing, Architectures for Edge Computing, Architectural Support for Reliability and Security.
- Performance Modeling and Evaluation: Performance Modeling of Parallel or Distributed Computing, Performance Evaluations of Parallel or Distributed Systems; Simulation Models; Analytical Models; Measurement based Evaluation
- Systems Software: Parallel and Distributed Programming Languages & Models, Programming Systems, Compilers, Libraries, Programming Infrastructures and Tools, Operating and Real-Time Systems, Middleware, Systems Software for Machine Learning