AREAS OF INTEREST

The Research Papers Committee encourages the submission of high-quality papers reporting original work in theoretical, experimental, and industrial research & development. The ISC submission process will be divided into five tracks this year, as follows:

Architectures, Networks, & Storage 	  	

    Next-generation processors and accelerators for HPC
    Extreme heterogeneous chips/chiplets for HPC beyond exascale
    The future of computing beyond Moore's Law
    Network technology & architecture
    Domain-specific architectures / accelerators
    Memory technologies & hierarchies
    Storage architectures & file systems
    Scalable architectures for big data and data science
    Exascale computing
    Distributed / disaggregated resources / systems

HPC Algorithms & Applications	  	

    Novel algorithms for HPC
    Algorithmic-based fault tolerance
    Communication-reducing & synchronization-reducing algorithms
    Time-space trade-offs in algorithms and applications
    Extreme-scale applications
    Energy-efficient algorithms & applications
    Convergence of simulations & big data
    Application scalability on future architectures
    Data-intensive applications
    Coupled simulations
    Implementations on GPUs & accelerators
    In-situ data analysis

Programming Environments & Systems Software 	  	

    Parallel programming models, languages, and compilers
    Tools and libraries for performance & productivity at scale
    Scalable application frameworks
    Runtime systems for HPC
    Resilience / system robustness
    Operating systems & workload management
    Tools for system monitoring & administration
    Power, energy management, and scheduling
    Workflow management
    Databases

Machine Learning, AI, & Quantum Computing	  	

    AI/Machine learning & HPC
    HPC system design for AI & machine learning ​
    I/O, domain-specific languages, and tools for scalable machine learning
    Applied machine learning studies
    Uncertainty quantification
    HPC for parallel/distributed deep learning
    Explainable AI and FAIR Principles for ML and data management
    Quantum computing architecture, algorithms, and software
    Quantum computing & HPC
    Other cutting-edge or emerging HPC technologies

Performance Modeling, Evaluation, & Analysis 	  	 

    Performance models and prediction
    Performance engineering and optimizations
    Performance measurement tools and techniques
    Power consumption measurement and modeling
    Performance analysis and visualization tools
    Simulations for performance modeling and prediction
    Workflow modeling