Paper Submissions

Topics include:

• Algorithmic techniques to improve energy and power efficiency
• Algorithmic techniques to improve load balance
• Data-intensive parallel algorithms
• Discrete and combinatorial problems
• Fault-tolerant algorithms
• Graph and network algorithms
• Hybrid/heterogeneous/accelerated algorithms
• Numerical methods and algebraic systems
• Scheduling algorithms
• Uncertainty quantification
• Other high performance 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
• Other high performance applications
• Use of uncertainty quantification, statistical, and machine-learning techniques to improve a specific HPC application
• Improved models, algorithms, performance or scalability of specific applications and respective software

Topics include:

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

Topics include:

• Compute and storage cloud architectures including many-core computing and accelerators in the cloud
• HPC and cloud convergence at infrastructure and software level
• Innovative methods for using cloud-based systems for HPC applications
• Support and tuning of Big Data cloud data ecosystems on HPC infrastructures
• Parallel programming models and tools at the intersection of cloud and HPC
• Virtualization and containerization for HPC, virtualized high performance I/O network interconnects, parallel and distributed file systems in virtual environments
• Cloud workflow, data, and resource management including dynamic resource provisioning
• Methods, systems, and architectures for scalable data stream processing
• Scheduling, load balancing, resource provisioning, energy efficiency, fault tolerance, and reliability for cloud computing
• Self-configuration, management, information services, and monitoring
• Service-oriented architectures and tools for integration of clouds, clusters, and distributed computing
• Cloud security and identity management
• Science case studies on cloud infrastructure
• Machine learning for science in the cloud

Topics include:

• 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
• Scalable storage systems
• 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
• Cloud-based storage
• Storage systems for data-intensive computing
• Data science
• Cloud-based analytics at scale
• Innovations for visualizing data at scale (big data)
• Innovations in image processing of data at scale (big data)

Topics include:

• Machine learning and optimization models for extreme scale systems
• Enhancing applicability of machine learning in HPC (e.g. usability)
• Learning large models / optimizing hyper parameters (e.g. deep learning, representation learning)
• Facilitating very large ensembles in extreme scale systems
• Training machine learning models on large datasets and scientific data
• Overcoming the machine learning problems inherent to large datasets (e.g. noisy labels, missing data, scalable ingest)
• Large scale machine learning applications utilizing HPC
• Future research challenges for machine learning at large scale
• Hybrid machine learning algorithms for hybrid HPC compute architectures
• Systems, compilers, and languages for machine learning at scale

Topics include:

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

See the Papers FAQ for more information.

Topics include:

• Parallel programming languages, libraries, models, and notations
• 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., interoperability, memory consistency, determinism, race detection, work stealing, or load balancing)
• Parallel application frameworks
• Tools for parallel program development (e.g., debuggers and integrated development environments)
• 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)
• Compiler analysis and optimization; program transformation
• Runtime systems as they interact with programming systems

Topics include:

• Bridging of cloud data centers and supercomputing centers
• Comparative system benchmarking over a wide spectrum of workloads
• Deployment experiences of large-scale infrastructures and facilities
• Facilitation of “big data” associated with supercomputing
• 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
• User support experiences with large-scale and novel machines
• Infrastructural policy issues, especially international experiences
• Software engineering best practices for HPC

See the Papers FAQ for more information.

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