Abstracts Submissions Close
April 2, 2021

Full Paper Submissions Close
April 9, 2021 (No extensions; includes manuscript, AD, and optional AE appendix)

Reviews Sent
May 14, 2021

Resubmission Deadline
May 28, 2021 (Includes manuscript, AD, and optional AE appendix)

Notifications Sent
June 21, 2021

Major Revision Deadline
July 16, 2021 (Includes manuscript, AD, and optional AE appendix)

Major Revision Notifications Sent
August 6, 2021

Final Paper Deadline
August 27, 2021


Overview

The SC Papers program is the leading venue for presenting high-quality original research, groundbreaking ideas, and compelling insights on future trends in high performance computing, networking, storage, and analysis. Technical papers are peer-reviewed and an Artifact Description is mandatory for all papers submitted to SC.
Areas/Tracks

Submissions will be considered on any topic related to high performance computing within the areas below. Authors must indicate a primary area from the choices on the submissions form and are strongly encouraged to indicate a secondary area.

Small-scale studies – including single-node studies – are welcome as long as the paper clearly conveys the work’s contribution to high performance computing.
Algorithms

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
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

System Software

Operating system (OS), runtime system and other low-level software research & development that enables allocation and management of hardware resources for HPC applications and services.

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