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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
- 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
- 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 and Networks
All aspects of high performance hardware including the optimization and evaluation of processors and networks.
Topics include:
- 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
Clouds and Distributed 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:
- HPC, cloud, and edge computing convergence at infrastructure and software level, including service-oriented architectures and tools
- Job/workflow scheduling, load balancing, resource provisioning, energy efficiency, fault tolerance, and reliability
- Methods, systems, and architectures for big data and data stream processing in HPC and cloud systems
- OS/runtime and system-software enhancements for many-core systems, accelerators, complex memory space/hierarchies, I/O, and network structures
- Parallel programming models and tools at the intersection of cloud, edge, and HPC
- Self-configuration, management, information services, monitoring, and introspective system software
- Security and identity management in HPC and cloud systems
- Scalable HPC and machine learning case studies on distributed and/or cloud systems
- Virtualization and containerization to support HPC and emerging uses such as machine learning
Data Analytics, Visualization, and 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.
Topics include:
- Cloud-based analytics at scale
- Databases and scalable structured storage for HPC
- Data mining, analysis, and visualization for modeling and simulation
- Data analytics and frameworks supporting data analytics
- Ensemble analysis and visualization
- I/O performance tuning, benchmarking, and middleware
- 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
Machine Learning and HPC
The development and enhancement of algorithms, systems, and software for scalable machine learning utilizing high-performance and cloud computing platforms.
Topics include:
- ML for HPC / 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
Performance Measurement, Modeling, and 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
Programming Systems
Technologies that support parallel programming for large-scale systems as well as smaller-scale components that will plausibly serve as building blocks for next-generation HPC architectures.
Topics include:
- Compiler analysis and optimization; program transformation
- Parallel programming languages, libraries, models, and notations
- Parallel application frameworks
- Programming language and compilation techniques for reducing energy and data movement (e.g., precision allocation, use of approximations, tiling)
- Program analysis, synthesis, and verification to enhance cross-platform portability, maintainability, result reproducibility, resilience (e.g., combined static and dynamic analysis methods, testing, formal methods)
- Runtime systems as they interact with programming systems
- Solutions for parallel-programming challenges (e.g., interoperability, memory consistency, determinism, race detection, work stealing, or load balancing)
- Tools for parallel program development (e.g., debuggers and integrated development environments)
State of the Practice
All R&D aspects of the pragmatic practices of HPC, including operational IT infrastructure, services, facilities, large-scale application executions and benchmarks.
Topics include:
- Bridging of cloud data centers and supercomputing centers
- Comparative system benchmarking over a wide spectrum of workloads
- Containers at scale: performance and overhead
- Deployment experiences of large-scale infrastructures and facilities
- Facilitation of “big data” associated with supercomputing
- Infrastructural policy issues, especially international experiences
- Long-term infrastructural management experiences
- Pragmatic resource management strategies and experiences
- 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
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