Submissions will be considered on any topic related to high performance computing within the areas below. Authors must indicate a primary area from the choices on the submissions form and are strongly encouraged to indicate a secondary area.

Small-scale studies – including single-node studies – are welcome as long as the paper clearly conveys the work’s contribution to high performance computing.

algorithms

The development, evaluation, and optimization of scalable, general-purpose, high performance algorithms.

Topics include:

    Algorithms for discrete and combinatorial optimization
    Algorithms for hybrid and heterogeneous systems with accelerators
    Algorithms for numerical methods and algebraic systems
    Data-intensive parallel algorithms
    Energy- and power-efficient algorithms
    Fault-tolerant algorithms
    Graph and network algorithms
    Load balancing and scheduling algorithms
    Machine learning algorithms
    Uncertainty quantification methods
    Other high performance computing algorithms

applications

The development and enhancement of algorithms, parallel implementations, models, software and problem solving environments for specific applications that require high performance resources.

Topics include:

    Bioinformatics and computational biology
    Computational earth and atmospheric sciences
    Computational materials science and engineering
    Computational astrophysics/astronomy, chemistry, and physics
    Computational fluid dynamics and mechanics
    Computation and data enabled social science
    Computational design optimization for aerospace, energy, manufacturing, and industrial applications
    Computational medicine and bioengineering
    Irregular applications including graphs, network science, and text/pattern matching
    Improved models, algorithms, performance or scalability of specific applications and respective software
    Use of uncertainty quantification, statistical, and machine-learning techniques to improve a specific HPC application
    Other high performance applications

Architecture & Networks

All aspects of high performance hardware including the optimization and evaluation of processors and networks.

Topics include:

    Hardware/software co-design for HPC 
    Hardware support for programming languages or software development
    Architectures for extreme heterogeneity or HPC/Quantum hybrids
    HPC interconnects: topology, switch architecture, optical networks, software-defined networks
    Network protocols, quality of service, congestion control, collective communication, offloading
    I/O architecture/hardware and emerging storage technologies
    Memory Systems & Architectures: caches, memory technology, non-volatile memory, coherence, translation
    Multi-processor architecture and micro-architecture (e.g., reconfigurable, vector, stream, dataflow, GPUs, and custom/novel architecture)
    Design-space exploration / performance projection for future systems
    Evaluation and measurement on testbed or production hardware systems
    Power-efficient design and power-management strategies
    Resilience, error correction,high availability architectures
    Secure architectures, side-channel attacks and mitigations for HPC

Data Analytics, Visualization, & Storage

All aspects of data analytics, visualization, storage, and storage I/O related to HPC systems, Submissions on work done at scale are highly favored. Further, submissions having a component focusing on the “Art of HPC” are appreciated.

Topics include:

    Data analytics, visualization, and storage for HPC systems
    Cloud-based analytics and scalable databases
    Data mining, analysis, and visualization
    Data reduction/compression for simulation data
    Data integration workflows and design and performance of data-centric workflows
    I/O performance tuning and middleware
    In situ data processing and visualization
    Next-generation storage systems
    Parallel storage systems (file, object, key-value, etc.)
    Provenance, metadata, and data management
    Reliability and fault tolerance in HPC storage
    Storage tiering (on-premise and cloud)
    Storage innovations using machine learning
    Storage networks and scalable cloud solutions
    Visual analytics for supercomputing systems, application monitoring, and machine learning model interpretation and tuning at scale

HPC for Machine Learning

The development and enhancement of algorithms, systems, and software for scalable machine learning utilizing high performance computing technology. This area is primarily addressing the use of HPC to improve ML rather than the use of ML to improve any technology covered by other areas. It is particularly designed for papers that have a strong ML component and that need to be evaluated by ML experts. Papers addressing the latter should be submitted to the respective areas.

Topics include:

    HPC for ML
    Parallel and distributed learning algorithms
    Hardware-efficient training and inference
    Model, pipeline, and data parallelism 
    Accelerated computing for ML
    Large-scale data processing for ML
    Performance modeling and analysis of ML applications
    Scalable optimization methods for ML
    Scalable hyperparameter tuning and optimization
    Scalable neural architecture search
    Model deployment and inference at scale
    Systems, compilers, and languages for ML at scale

Performance Measurement, Modeling, & Tools

Novel methods and tools for measuring, evaluating, and/or analyzing performance for large-scale systems.

Topics include:

    Analysis, modeling, or simulation methods for performance
    Methodologies, metrics, and formalisms for performance analysis and tools
    Novel and broadly applicable performance optimization techniques
    Performance studies of HPC hardware and software subsystems such as processor, network, memory, accelerators, and storage
    Scalable tools and instrumentation infrastructure for measurement, monitoring, and/or visualization of performance
    System-design tradeoffs between performance and other metrics (e.g., performance and resilience, performance and security)
    Workload characterization and benchmarking techniques

post-Moore Computing

Technologies that continue the scaling of supercomputing performance beyond the limits of Moore’s law, including system architecture, programming frameworks, system software, and applications.

Topics include:

    Hardware specialization and taming extreme heterogeneity
    Beyond von-Neumann computer architectures
    Special purpose computing (e.g., Anton or GRAPE)
    Quantum computing, especially focusing on hybrid HPC/QC
    Neuromorphic and brain-inspired computing
    Probabilistic, stochastic computing, and approximate computing
    Novel post-CMOS device technologies and advanced packaging technologies for heterogeneous integration (evaluated in a supercomputing systems or application context)
    Superconducting electronics for supercomputing
    Programming models and programming paradigms for post-Moore systems
    Tools for modeling, simulating, emulating, or benchmarking post-Moore and post-CMOS devices and systems

Programming Frameworks

Compilers, programming languages, libraries, programming models, and runtime systems that enable management of hardware resources and support parallel programming for large-scale systems.

Topics include:

    Compiler analysis, optimization and code generation 
    Program verification, program transformation and synthesis 
    Parallel programming languages, libraries, models, and application frameworks
    Execution models and runtime systems
    Communication libraries 
    Programming language and compilation techniques for reducing energy and data movement 
    Solutions for parallel-programming challenges (e.g., interoperability, memory consistency, determinism, reproducibility, race detection)
    Tools and frameworks for fault tolerance and resilience
    Tools and frameworks for parallel program development (e.g., debuggers and integrated development environments)
    Programming models and framework for heterogeneous systems
    Programming models and runtime for future novel systems

State of the practice

All aspects of the pragmatic practices of HPC, including operational IT infrastructure, services, facilities, large-scale application executions and benchmarks. Papers are expected to capture experiences and ongoing practice relating to modern computing centers or HPC-related software. Papers do not need to cover novel research or developments, but they are expected to offer novel insights and lessons for HPC architects, developers, administrators, or users.

Topics include:

    Bridging of cloud data centers and supercomputing centers
    Energy efficiency and carbon emission of HPC and data centers
    Comparative system benchmarking over a wide spectrum of workloads
    Deployment experiences of large-scale hardware and software infrastructures and facilities
    Facilitation of “big data” associated with supercomputing
    Infrastructural policy issues and management experiences, especially international experiences
    Pragmatic resource management strategies and experiences
    Monitoring and operational data analytics
    Procurement, technology investment and acquisition best practices
    Quantitative results of education, training, and dissemination activities
    Software engineering best practices for HPC
    User support experiences with large-scale and novel machines
    Provenance, logistic concerns and reproducibility of data
    Adoption and use of infrastructure as code paradigm
    Management, support and impact of large workflows
    Workload analysis, accounting and group users interactions

System Software & Cloud Computing

Cloud and system software architecture, configuration, optimization and evaluation, support for parallel programming on large-scale systems or building blocks for next-generation HPC architectures.

Topics include:

    Convergence of HPC, cloud, edge, and other distributed computing resources
    Analysis of cost, performance, and reliability of HPC, cloud, and edge facilities
    Systems that facilitate distributed applications, such as workflow systems, task-oriented systems, functions-as-a-service, and service-oriented computing
    Integration and management of HPC hardware in clouds and distributed systems
    Scheduling, load balancing, resource provisioning, resource management, cost efficiency, fault tolerance, and reliability for large-scale systems and clouds
    Green clouds, energy efficiency, power management, carbon awareness
    Approaches for enabling adaptive and elastic system software
    Parallel/networked file system integration with the OS and runtime
    OS and runtime system enhancements for accelerators
    Runtime and OS management of complex memory hierarchies
    Interactions among the OS, middleware and tools
    System software for reducing energy and data movement 
    Self-configuration, monitoring, and introspection
    Security, sharing, auditing, and identity management
    Virtualization, containerization, and other technologies for isolation and portability
    Case studies of scalable distributed applications that span facilities