Información de la conferencia

HiPC 2026: IEEE International Conference on High Performance Computing, Data, and Analytics

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Día de Entrega:
2026-06-17
Fecha de Notificación:
2026-09-18
Fecha de conferencia:
2026-12-16
Ubicación:
Bengaluru, India
Ediciones:
33
CCF: C   QUALIS: B1   Vistas: 151358   Seguidores: 103   Asistentes: 15

Índice Conference Partner (CP-I)

81,0 / 100
Puesto n.º 186 de 5.693 congresos · 4% superior

N.º 35 de 341 en Sistemas y arquitectura

Reconocimiento académico (35%)
78
Selectividad en la revisión (20%)
86
Ediciones celebradas (20%)
95
Atención de la comunidad (10%)
78
Integridad del registro público (15%)
65

Datos utilizados: Categorías: CCF C, QUALIS B1 · Tasa de aceptación: 23.7% (media de 5 ediciones registradas) · Ediciones documentadas: 33 · Investigadores que lo siguen aquí: 103 · Investigadores que abrieron esta página en los últimos 24 meses: 78

Falta en el registro público: Ediciones anteriores (+3,0) · Premios al mejor artículo (+2,3)
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Solicitud de Artículos

HiPC 2026 (IEEE International Conference on High Performance Computing, Data, and Analytics) is a CCF C / QUALIS B1 conference held in Bengaluru, India on 2026-12-16. The paper submission deadline is 2026-06-17. Acceptance notifications are sent on 2026-09-18.

HiPC 2026 will be the 33rd edition of the IEEE International Conference on High Performance Computing. HiPC serves as a forum to present current work by researchers in the areas of high performance computing, artificial intelligence, hardware systems, edge computing and quantum computing and their scientific, engineering, and commercial applications. Authors are invited to submit original unpublished research manuscripts that demonstrate current research in all areas of high performance computing, artificial intelligence, and quantum computing platforms and their applications. Each submission should be submitted under one of the five broad themes listed below. HiPC proceedings appear in IEEE Xplore Digital Library, which is Scopus-indexed. Distinguished paper awards will be given for outstanding contributed papers. Authors of selected high-quality papers in HiPC 2026 will be invited to submit extended versions of their papers for possible publication in a special issue of the Journal of Parallel and Distributed Computing. High Performance Computing Systems and Applications This track invites papers that describe original research on using HPC systems and applications, and related advances. Examples of topics of interest include (but not limited to): New parallel and distributed algorithms and design techniques; advances in enhancing algorithmic properties or providing guarantees; Algorithmic techniques for resource allocation and optimization (e.g., scheduling, load balancing, resource management); Provably efficient parallel and distributed algorithms for advanced scientific computing and irregular applications (e.g., numerical linear algebra, graph algorithms, computational biology); High performance processing architectures (e.g., reconfigurable, system-on-chip, many cores, vector processors, tensor cores); Memory, cache, networks, and storage architectures (e.g., 3D, photonic, Processing-In-Memory, NVRAM, burst buffers, parallel I/O); Shared and distributed memory parallel applications Techniques to enhance parallel performance, or parallel application development and productivity Software for cloud, data center, and exascale platforms Software and programming paradigms for heterogeneous platforms Artificial Intelligence Systems and Applications This track invites papers that describe original research on using AI/ML for systems design or systems design for AI/ML application and related advances. Examples of topics of interest include (but not limited to): AI/ML methods for system design and optimization (e.g. efficient design space exploration, job scheduling, energy efficiency) in computing systems; AI/ML methods that benefit HPC applications or HPC system management; Scaling and accelerating machine learning, deep learning, natural language processing and computer vision applications; Efficient model training, inference, and serving (includes specialized hardware design and SW techniques); Fairness, interpretability, and explainability for AI/ML applications; End-to-end machine learning pipeline optimization (data prep and data cleaning); Compound AI systems and AI agent systems; Methods, algorithms, optimizations, systems and software architecture for scaling AI/ML applications on high-performance computing Machine learning benchmarks for parallel and distributed platforms and datasets. Quantum Computing Systems and Applications This track invites papers that describe original research on designing innovative quantum and quantum-classical hybrid algorithms, hardware, applications, compiler and runtime systems. Examples of topics of interest include (but not limited to): Design and development of innovative quantum algorithms to address complex computational challenges across various science domains. Protocols, design, and evaluation of hardware architecture and software frameworks enabling integration of classical computing and quantum computing (e.g., quantum computing architecture, error mitigation, error correction, hybrid quantum-classical applications & benchmarks) Programming languages, compilers, and optimization techniques for developing quantum system software, and their integration into hybrid quantum-classical computing workflows Quantum system software for quantum computers based on different qubit technology (superconducting, neutral atom, photonics). Optimizations, tools, simulators, and testbeds for quantum-enhanced smart systems Quantum computing system architecture and software co-design for AI workloads Novel AI methods and tools for enhancing the utility of near-term quantum computers System software, applications, and architecture for quantum sensing and quantum networks/communications design & control. Edge Computing Systems and Applications This track invites papers that describe original research on building and using edge computing systems and applications, and related advances. Examples of topics of interest include (but not limited to): AI and IoT applications, digital twins, and other edge-driven applications Emerging edge workloads and novel systems support Algorithms, systems, and paradigms that enable collaborative, distributed, decentralized, communication efficient learning at the edge or hybrid cloud-edge Learning-based resource management at the edge Serverless and other programming models for edge environments DevOps practices across edge and cloud Multitenancy and resource sharing at the edge Edge-driven HPC and HPC-steered edge computing Energy-efficient, low-power, and sustainable hardware/software architectures Green and sustainable Edge AI Cyber-security and privacy in edge computing Security and privacy for distributed learning and inference Data and AI lifecycle management across edge and cloud Hardware Systems, Accelerators, and Emerging Technologies This track invites papers that describe original research on building hardware systems, various accelerators, and emerging technologies. Examples of topics of interest include (but not limited to): Post-exascale high-performance computing Post-Moore’s Law Systems: Neuromorphic, biologically-inspired, superconducting, and hyperdimensional computing Applications leveraging GPUs, FPGAs, TPUs, DPUs, on-chip accelerators, or other novel architectures. Heterogeneous system architectures (ARM, RISC-V, custom extensions) Memory hierarchies and novel memory systems Energy efficiency and thermal considerations Security and reliability in hardware systems Performance modeling and prediction High-level programming models (such as OpenMP, OpenACC, SYCL, OneAPI, Kokkos, Raja) Low-level programming interfaces (such as OpenCL, CUDA) Debugging, profiling, testing, and verification methods for hardware systems Memory management and data movement optimization
Última actualización por Dou Sun el

Tasa de aceptación

Tasa de aceptación media: 22.4% en 20 años (2001–2020).

AñoEnviadosAceptadosAceptados(%)
20201273326%
20191713922.8%
20181513321.9%
20171844222.8%
20161604025%
20152014823.9%
20142124923.1%
20131964925%
20121634125.2%
20112064019.4%
20102084019.2%
20093203510.9%
20083174614.5%
20072535220.6%
20063355215.5%
20053625013.8%
20042144822.4%
20031644829.3%
20021455739.3%
20011082926.9%

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