How to Submit
- Preparing Your Submission
- Where to Submit
- Transparency and Reproducibility Initiative
- Review Criteria
- Review, Response, Revision
How to Submit
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 SC20.
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.
The development, evaluation, and optimization of scalable, general-purpose, high performance algorithms.
- 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
The development and enhancement of algorithms, parallel implementations, models, software and problem solving environments for 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, 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.
- 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.
- 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.
- 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.
- 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.
- 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 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.
- 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.
- 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
Preparing Your Submission
A paper submission has three components: the paper itself, an Artifact Description Appendix (AD), and an Artifact Evaluation Appendix (AE). The Artifact Description Appendix, or explanation of why there is no artifact description, is mandatory. The Artifact Evaluation Appendix is optional.
Papers that have not previously been published in peer-reviewed venues are eligible for submission to SC. For example, papers pre-posted to arXiv, institutional repositories, and personal websites (but no other peer-reviewed venues) remain eligible for SC submission.
Papers that were published in a workshop are eligible if they have been substantially enhanced (i.e. 30% new material).
Submissions are limited to 10 pages (U.S. letter – 8.5″x11″), excluding the bibliography, using the IEEE proceedings template.
AD and AE appendices are automatically generated and do not count against the 10 pages.
Authors of accepted papers may provide supplemental material with their final version of the paper (e.g., additional proofs, videos, or images).
Where to Submit
Transparency and Reproducibility Initiative
We believe that reproducible science is essential, and that SC continues to innovate in this area. For SC20 there will be greater integration of the AD/AE Appendices into the review process with AD/AE Appendices considered at every stage of paper review. AD/AE Appendices will continue to be auto-generated from author responses to a standard form that is embedded in the SC online submission system. While the Artifact Description Appendix, or explanation of why there is no Artifact Description Appendix, is mandatory, the Artifact Evaluation Appendix continues to be optional. Learn more about the Transparency and Reproducibility Initiative.
Papers are peer-reviewed by a committee of experts. Each paper will have three to four reviews. The peer review process is double-blind for the paper and double-open for the Appendices. Paper reviewers do not have access to the names of authors. Appendices reviewers and authors will know each other’s names. While Papers Committee members are named on the SC20 Planning Committee page, the names of the individuals reviewing each paper are not made available to the paper authors. Learn more about the SC double-blind review policy, and see examples in the Papers FAQ (Available Winter 2020).
Review, Response, Revision
From an author’s perspective, the following are the key steps:
- Authors submit a title, abstract, and other metadata.
- Authors submit their full paper and complete an AD/AE form describing their computational artifacts (or lack of computational artifacts) and, optionally, text discussing how they evaluated their computational results.
- Papers not respecting the submission guidelines will be subject to immediate reject without review. For example, papers not respecting the double-blind submission or papers exceeding the page limit.
- Authors receive an initial set of reviews of their paper. Papers not reaching the minimum quality criteria to go to the second review stage will be rejected at this point. Early rejection will allow authors to revise and resubmit their papers to other venues.
- Authors of papers that reach the second review stage have an opportunity to revise their paper and prepare an accompanying response to the reviewers.
- Author revisions and accompanying response will be available to the reviewers at least a week before the Papers Committee meeting.
- Authors are notified of their paper’s status: Accept, Reject, or Major Revisions Required.
- In the case of Major Revisions Required, authors prepare a major revision for a third stage review.
- After the third stage review, the paper will be either accepted or rejected.
- Authors of accepted papers prepare the final version of their paper.
Conflict of Interest
Please review the SC Conference Conflict of Interest guidelines before submitting your paper.
Please see the IEEE guidelines on identifying plagiarism. Authors should submit new, original work that represents a significant advance from even their own prior publications.
If your Paper is selected, at least one author must register for the Technical Program in order to attend the SC Conference and present the paper.
Finalizing Accepted Papers
Upon acceptance, all Papers (including those that goes through major revisions) will be listed in the online SC Schedule. We expect this to happen at the end of August 2020.
Papers are archived in the ACM Digital Library and IEEE Xplore; members of SIGHPC or subscribers to the archives may access the full papers without charge. This publication contains the full text of all Papers and their Artifact Description appendices presented at the SC Conference.
Papers presentations will be held Tuesday–Thursday, November 17–19, 2020. Papers sessions are 30 minutes. Day, time, and location for each paper session will be published in the online SC Schedule in August.
Paper authors will receive instructions and more information on recording and uploading their videos and presentations well in advance of the conference.
Speakers/Presenters will receive instructions and more information on recording and uploading their videos and presentations well in advance of the conference.
Best Paper (BP) and Best Student Paper (BSP) nominations are made during the review process and are highlighted in the online SC schedule. BP and BSP winners are selected at the conference by a committee who attends the corresponding paper presentations, and winners are announced at the Thursday Awards ceremony.
Questions about Paper Submissions?