Paper Submissions

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

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

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

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

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

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

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

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)

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

Topics include:

• Alternative and specialized parallel operating systems and runtime systems
• Approaches for enabling adaptive and introspective system software
• Communication optimization
• Software distributed shared memory systems
• System-software support for global address spaces
• 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
• Runtime and OS management of complex memory hierarchies
• System software strategies for controlling energy and temperature
• Support for fault tolerance and resilience
• Virtualization and virtual machines