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

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

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

Topics include:

• Architectural support for programming languages or software development.
• Architectures to support extremely heterogeneous composable systems (e.g., chiplets)
• Design-space exploration / performance projection for future systems
• Evaluation and measurement on testbed or production hardware systems
• Hardware acceleration of containerization and virtualization mechanisms for HPC
• Interconnect technologies, topology, switch architecture, optical networks, software-defined networks
• I/O architecture/hardware and emerging storage technologies
• Memory systems: caches, memory technology, non-volatile memory, memory system architecture (to include address translation for cores and accelerators)
• Multi-processor architecture and micro-architecture (e.g., reconfigurable, vector, stream, dataflow, GPUs, and custom/novel architecture)
• Network protocols, quality of service, congestion control, collective communication
• Power-efficient design and power-management strategies
• Resilience, error correction, high availability architectures
• Scalable and composable coherence (for cores and accelerators)
• Secure architectures, side-channel attacks, and mitigation
• Software/hardware co-design, domain specific language support

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, models, and languages that facilitate distributed applications, such as workflow systems, task-oriented systems, functions-as-a-service, and service-oriented computing.
• Systems, models, and languages for big data, streaming data, and in-situ data analysis on clouds and distributed systems
• Integration and management of high performance computing hardware (such as accelerators, complex memories, advanced networks) in clouds and distributed systems.
• Scheduling, load balancing, resource provisioning, resource management, cost efficiency, fault tolerance, and reliability for clouds
• Green clouds, energy efficiency, power management
• Self-configuration, management, 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

All aspects of data analytics, visualization, storage, and storage I/O related to HPC systems, Submissions on work done at scale are highly favored.

Topics include:

• Cloud-based analytics at scale
• Databases and scalable structured storage for HPC
• Data mining, analysis, and visualization for modeling and simulation
• Data reduction/compression on HPC and clouds for simulation, and experimental data
• Design and optimization of integrated workflows for visual analytics
• Ensemble analysis and visualization
• I/O performance tuning, benchmarking, and middleware
• In situ data processing and visualization
• Next-generation storage systems and media
• Parallel file, object, key-value, campaign, and archival systems
• Provenance, metadata, and data management
• Reliability and fault tolerance in HPC storage
• Scalable storage, metadata, namespaces, and data management
• Storage tiering, entirely on-premise internal tiering as well as tiering between on-premise and cloud
• Storage innovations using machine learning such as predictive tiering, failure, etc.
• Storage networks
• Scalable cloud, multi-cloud, and hybrid storage
• Storage systems for data-intensive computing
• Visual analytics for monitoring and optimizing supercomputing systems and applications
• Visual analytics for interpreting and tuning machine learning models at scale

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. Papers addressing the latter should be submitted to the respective areas.

Topics include:

• HPC for ML
• Data parallelism and model parallelism
• Efficient hardware for machine learning
• Hardware-efficient training and inference
• Performance modeling of machine learning applications
• Scalable optimization methods for machine learning
• Scalable hyper-parameter optimization
• Scalable neural architecture search
• Scalable IO for machine learning
• Systems, compilers, and languages for machine learning at scale
• Testing, debugging, and profiling machine learning applications
• Visualization for machine learning at scale

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

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
• 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

Operating system, runtime system, technologies, and software building blocks that enable management of hardware resources and support parallel programming for large-scale systems.

Topics include:

• Compiler analysis/optimization, Program verification, and Program transformation/synthesis to enhance cross platform portability, maintainability, result reproducibility, resilience, etc. (e.g., combined static and dynamic analysis methods, testing, formal methods)
• Parallel programming languages, libraries, models, notations, application frameworks, and runtime systems
• System software, and programming language and compilation techniques for reducing energy and data movement (e.g., precision allocation, use of approximations, tiling)
• Solutions for parallel-programming challenges (e.g., support for global address spaces, interoperability, memory consistency, determinism, reproducibility, race detection, work stealing, or load balancing)
• Tools and frameworks for parallel program development (e.g., debuggers and integrated development environments)
• Approaches for enabling adaptive and introspective system software
• OS and runtime system enhancements for attached and integrated accelerators
• Interactions among the OS, runtime, compiler, middleware, and tools
• Parallel/networked file system integration with the OS and runtime
• Resource management, job scheduling, system interoperations and energy-aware techniques for large-scale systems
• Runtime and OS management of complex memory hierarchies

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 and power efficiency of HPC and data centers
• Comparative system benchmarking over a wide spectrum of workloads
• Containers at scale: performance and overhead
• Deployment experiences of large-scale hardware and software infrastructures and facilities
• Facilitation of “big data” associated with supercomputing
• Infrastructural policy issues, especially international experiences
• Long-term infrastructure management 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
• Reproducibility of data