HPC Illuminations Pavilion

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:

• 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

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:

• 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

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

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

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

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

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