Submissions will be considered on any topic related to high-performance computing including, but not limited to, the nine topical areas below.

Algorithms: The development, evaluation and optimization of scalable, general-purpose, high-performance algorithms.

• Algorithmic techniques to improve energy efficiency

• Algorithmic techniques to improve load balance

• Data-intensive parallel algorithms

• Discrete and combinatorial problems

• Fault-tolerant algorithms

• Graph algorithms

• Hybrid/heterogeneous/accelerated algorithms

• Network algorithms

• Numerical methods, linear and nonlinear systems

• Scheduling algorithms

• Uncertainty quantification

• Other high-performance algorithms

Applications: The development and enhancement of algorithms, models, software and problem solving environments for domain-specific applications that require high-performance resources.

• Bioinformatics and computational biology

• Computational earth and atmospheric sciences

• Computational materials science and engineering

• Computational astrophysics/astronomy, chemistry, fluid dynamics, mechanics and physics

• Computation and data enabled science

• Computational design optimization for aerospace, energy, manufacturing and industrial applications

• Computational medicine and bioengineering

• Use of uncertainty quantification techniques

• Other high-performance applications

Architecture and Networks: All aspects of high-performance hardware including the optimization and evaluation of processors and networks.

• Innovative hardware/software co-design

• Interconnect technologies (e.g., InfiniBand, Myrinet, Ethernet and Routable PCI), switch/router architecture, network topologies, on-chip or optical networks and network fault tolerance

• Software defined networks

• Memory systems and novel memory architectures

• Parallel and scalable system architectures

• Power-efficient, high-availability, stream, vector, embedded and reconfigurable architectures, and emerging technologies

• Processor architecture, chip multiprocessors, GPUs, cache, and memory subsystems

• Protocols (e.g., TCP, UDP and sockets), quality of service, congestion management and collective communication

Clouds and Distributed Computing: All aspects of clouds and distributed computing that are related to high-performance computing systems, including architecture, configuration, optimization and evaluation.

• Compute and storage cloud architectures

• Data management and scientific applications

• Problem-solving environments and portals

• Programming models and tools for computing on clouds and grids

• Quality of service and service-level agreement management

• Scheduling, load balancing, workflows and resource provisioning

• Security and identity management

• Self-configuration, management, information services and monitoring

• Service-oriented architectures and tools for integration of clouds, clusters and distributed computing

• Virtualization and overlays

Data Analytics, Visualization and Storage: All aspects of data analytics, visualization and storage related to high-performance computing systems.

• Databases and scalable structured storage for HPC

• Data mining, analysis and visualization for modeling and simulation

• I/O performance tuning, benchmarking and middleware

• Next-generation storage systems and media

• Parallel file, storage and archival systems

• Provenance

• Reliability and fault tolerance in HPC storage

• Scalable storage, metadata and data management

• Storage networks

• Storage systems for data intensive computing

• Visualization and image processing

Performance: Cross-cutting aspects of large-scale performance, including power and/or resilience, that typically span multiple areas of expertise and are crucial factors in the design of scalable high-performance computing systems.

• Analysis, modeling or simulation for performance, power and/or resilience

• Empirical measurement of performance, power and/or resilience on real-world systems

• Methodologies and formalisms for performance, power and/or resilience

• Methodologies, metrics and workloads for performance, power and/or resilience analysis and tools

• Performance, power and/or resilience analysis beyond execution time and flop/s

• Performance, power and/or resilience studies of HPC subsystems, such as processor, network, memory and I/O

• Tools, code instrumentation and instrumentation infrastructure for measurement and monitoring of performance, power and/or resilience

• Workload characterization and benchmarking

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 high-performance computing architectures.

• Compiler analysis and optimization; program transformation

• Parallel application frameworks

• Parallel programming languages, libraries, models and notations

• Runtime 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 aspects related to the pragmatic practices of HPC, including infrastructure, services, facilities and large-scale application executions. Submissions that develop best practices, optimized designs or benchmarks are of particular interest. Although concrete case studies within a conceptual framework often serve as the basis for accepted papers, how the experience generalizes to wider applicability should be explored.

• 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

System Software: Operating system (OS), runtime system and other low- level software research & development that enables allocation and management of hardware resources for high-performance computing applications and services.

• Alternative and specialized parallel operating systems and runtime systems

• Approaches for enabling adaptive and introspective system software

• Communication optimization

• Distributed shared memory systems

• System support for global address spaces

• Enhancements for attached and integrated accelerators

• Interactions between the OS, runtime, compiler, middleware, and tools

• Resource management

• Run-time 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