Paper Submissions

Paper length should be no more than 10 pages (including references) in the ACM SigConf format located at: https://www.acm.org/publications/proceedings-template

The double-blind review process applies to all submissions. Please refrain from including names, affiliations, funding sources, or acknowledgments in the heading or body of the document. Authors should cite their own work in a third-party manner rather than redacting the citations.

System Architecture & Hardware Components: Parallel Computer Architecture and Accelerator Designs, Large-Scale System Architectures, Datacenter/Warehouse Computing Architecture, Machine Learning Architectures, Micro-Architecture for Parallel Computing, Architectural Support for Networking, New Memory and Storage Technologies, Near-Memory Computing, Parallel I/O, Architectures for Edge Computing, Post-Moore, Architectural Support for Reliability and Security.  

Programming Environments & System Software: Software: System Software, Middleware, Runtimes for parallel computing, Parallel and Distributed Programming Languages & Models, Programming Systems, Compilers, Libraries, Programming Infrastructures and Tools, Operating and Real-Time Systems. 

Multidisciplinary: Multidisciplinary: Innovation combining multiple disciplines, Converged HPC Cloud Edge computing, Complex Workflows, Methodologies for Performance Portability and/or Productivity across Architectures. 

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, Post-Moore parallel algorithms.  

Performance: Performance: Performance Modeling of Parallel or Distributed Computing, Performance Evaluation of Parallel or Distributed Systems; Scalability, Simulation Models, Analytical Models, Measurement-Based Evaluation.

Applications & Use Cases: Parallel, Distributed and Accelerated Applications, Scalable Data Analytics & Applied Machine Learning, Computational and Data-Driven Science & Engineering in computational sciences including, but not limited to Astrophysics, Computational Chemistry and Physics, Life Sciences, Earth Science, Materials Science, Finance, Geology and Engineering. 

AI in Computing:  AI for Application & Use Case, AI for System Architecture & Hardware Components, AI for Multidisciplinary, AI for Performance and AI for Programming Environments & Systems Software

Quantum Computing:  Parallel simulators of quantum computers, use of parallel computing for quantum compilation and optimization, co-design of parallel- and quantum-computing applications, hybrid parallel/quantum software-development tools.