CCF: a CORE: a QUALIS: a1 Viewed: 96660 Tracked: 167 Attend: 17
Call For Papers
SC 2026 (International Conference for High Performance Computing, Networking, Storage, and Analysis) is a CCF A / CORE A / QUALIS A1 conference held in Chicago, Illinois, USA on 2026-11-15. The paper submission deadline is 2026-04-01. Acceptance notifications are sent on 2026-07-01.
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
Machine learning 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
Irregular applications including graphs, network science, and text/pattern matching
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 & Networks
All aspects of high performance hardware including the optimization and evaluation of processors and networks.
Topics include:
Hardware/software co-design for HPC
Hardware support for programming languages or software development
Architectures for extreme heterogeneity or HPC/Quantum hybrids or HPC/Edge hybrids
HPC interconnects: topology, switch architecture, optical networks, software-defined networks, programmable networks
Network protocols for HPC, quality of service, congestion control, collective communication, offloading
I/O architecture/hardware and emerging storage technologies
Memory Systems & Architectures: caches, memory technology, non-volatile memory, coherence, translation
Multi-processor architecture and micro-architecture (e.g., reconfigurable, vector, stream, dataflow, GPUs, and custom/novel architecture)
Design-space exploration / performance projection for future systems
Evaluation and measurement on testbed or production hardware systems
Power-efficient design and power-management strategies
Resilience, error correction,high availability architectures
Secure architectures, side-channel attacks and mitigations for HPC
Data Analytics, Visualization, & 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. Further, submissions having a component focusing on the “Art of HPC” are appreciated.
Topics include:
Data analytics, visualization, and storage for HPC systems
Cloud-based analytics and scalable databases
Large-scale data mining, analysis, and visualization
Data reduction/compression
Data integration workflows and design and performance of data-centric workflows
I/O performance tuning and middleware
In situ data processing and visualization
Next-generation storage systems
Parallel storage systems (file, object, key-value, etc.)
Provenance, metadata, and data management
Reliability and fault tolerance in HPC storage
Storage tiering (on-premise and cloud)
Storage innovations using machine learning
Storage networks and scalable cloud solutions
Visual analytics for supercomputing systems, application monitoring, and machine learning model interpretation and tuning at scale
HPC for Machine Learning
The development and enhancement of algorithms, systems, and software for scalable machine learning utilizing high performance computing technology. This area is primarily addressing the use of HPC to improve ML rather than the use of ML to improve any technology covered by other areas. It is particularly designed for papers that have a strong ML component and that need to be evaluated by ML experts. Papers addressing the latter should be submitted to the respective areas.
Topics include:
HPC for ML
Parallel and distributed learning algorithms
Hardware-efficient training and inference
Model, pipeline, and data parallelism
Accelerated computing for ML
Large-scale data processing for ML
Performance modeling and analysis of ML applications
Scalable optimization methods for ML
Scalable hyperparameter tuning and optimization
Scalable neural architecture search
Model deployment and inference at scale
Systems, compilers, and languages for ML at scale
Performance Measurement, Modeling, & 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
post-Moore & Quantum Computing
Technologies that continue HPC performance scaling beyond the limits of Moore’s law, including system architecture, use of emerging hardware in future systems, programming frameworks, system software, and applications.
Topics include:
Hardware specialization and special-purpose computing
Taming extreme heterogeneity and improving code portability
Beyond von-Neumann computer architectures
Quantum computing
Neuromorphic and brain-inspired computing
Probabilistic, stochastic computing, approximate computing, and other unconventional methods
Novel post-CMOS device technologies and advanced packaging technologies for heterogeneous integration, evaluated at the system or application level
Superconducting digital computing
Programming models and programming paradigms for post-Moore systems
Tools for modeling, simulating, emulating, or benchmarking systems with post-Moore and post-CMOS devices or specialized hardware
Programming Frameworks
Compilers, programming languages, libraries, programming models, and runtime systems that enable management of hardware resources and support parallel programming for large-scale systems.
Topics include:
Compiler analysis, optimization and code generation
Program verification, program transformation and synthesis
Parallel programming languages, libraries, models, and application frameworks
Execution models and runtime systems
Communication libraries
Programming language and compilation techniques for reducing energy and data movement
Solutions for parallel-programming challenges (e.g., interoperability, memory consistency, determinism, reproducibility, race detection)
Tools and frameworks for fault tolerance and resilience
Tools and frameworks for parallel program development (e.g., debuggers and integrated development environments)
Programming models and framework for heterogeneous systems
Programming models and runtime for future novel systems
State of the practice
All aspects of the pragmatic practices of HPC, including operational IT infrastructure, services, facilities, large-scale application executions and benchmarks. Papers are expected to capture experiences and ongoing practice relating to modern computing centers or HPC-related software. Papers do not need to cover novel research or developments, but they are expected to offer novel insights and lessons for HPC architects, developers, administrators, or users.
Topics include:
Bridging of cloud data centers and supercomputing centers
Energy efficiency and carbon emission of HPC and data centers
Comparative system benchmarking over a wide spectrum of workloads
Deployment experiences of large-scale hardware and software infrastructures and facilities
Facilitation of “big data” associated with supercomputing
Infrastructural policy issues and management experiences, especially international experiences
Pragmatic resource management strategies and experiences
Monitoring and operational data analytics
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
Provenance, logistic concerns and reproducibility of data
Adoption and use of infrastructure as code paradigm
Management, support and impact of large workflows
Workload analysis, accounting and group users interactions
System Software & Cloud 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:
Convergence of HPC, cloud, edge, and other distributed computing resources
Analysis of cost, performance, and reliability of HPC, cloud, and edge facilities
Systems that facilitate distributed applications, such as workflow systems, task-oriented systems, functions-as-a-service, and service-oriented computing
Integration and management of HPC hardware in clouds and distributed systems
Scheduling, load balancing, resource provisioning, resource management, cost efficiency, fault tolerance, and reliability for large-scale systems and clouds
Green clouds, energy efficiency, power management, carbon awareness
Approaches for enabling adaptive and elastic system software
Parallel/networked file system integration with the OS and runtime
OS and runtime system enhancements for accelerators
Runtime and OS management of complex memory hierarchies
Interactions among the OS, middleware and tools
System software for reducing energy and data movement
Self-configuration, monitoring, and introspection
Security, sharing, auditing, and identity management
Virtualization, containerization, and other technologies for isolation and portability
Case studies of scalable distributed applications that span facilities
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