会议信息

CLUSTER 2026: IEEE International Conference on Cluster Computing

截稿日期:
2026-04-23 已截稿
通知日期:
2026-07-05
会议日期:
2026-09-22
会议地点:
Alexandria, Virginia, USA
届数:
28
请登录查看会议网址
免费注册：查看官网链接、跟踪截稿日期，并接收邮件提醒。
注册 登录
CCF: B   ICORE: C   浏览: 138072   关注: 116   参加: 19
这一页信息有误？告诉我们
嵌入截止倒计时徽章
CLUSTER
用 API 获取这条数据
搜索与榜单列表完全无需凭证；本页的完整详情需要一把免费 API 密钥。详见开发者接入页。

会伴指数 (CP-I)

78.7 / 100
全站第 224 名 / 共 5,776 个会议 · 前 4%

系统与体系结构 第 43 / 342

学术认可 (35%)
82
投稿选择性 (20%)
74
会议传承 (20%)
91
社区关注 (10%)
73
资料公开度 (15%)
65

用到的输入: 收录等级：CCF B, ICORE C · 录用率：33.2%（有记录的 5 届的均值） · 有据可查的届次：28 · 在会伴关注它的研究者：116 人 · 过去 24 个月打开过本页的研究者：34 人

公开资料里还缺: 历届信息 (+3.0) · 最佳论文记录 (+2.3)
主办方认领本会议后，可直接在这一页补上；分数每晚重算。如何提升这个分数

置信度 100% —— 分数中有多大比例来自实际观测到的数据，而不是中性基准。 这个分数是怎么算出来的 · 查看完整榜单 · 算法版本 1.2 · 算于 2026-11-12

征稿

CLUSTER 2026 (IEEE International Conference on Cluster Computing) is a CCF B / ICORE C conference held in Alexandria, Virginia, USA on 2026-09-22. The paper submission deadline is 2026-04-23. Acceptance notifications are sent on 2026-07-05.

IEEE Cluster 2026 is the 28th edition of the IEEE Cluster conference series. It is being held in cooperation with SIGHPC. Computing clusters remain the primary system architecture for building many of today’s rapidly evolving computing infrastructures including high-performance computing, cloud computing, machine learning training and inference systems, and big data, and are used to solve some of the most complex problems. The challenges posed making them scalable, efficient, productive, and increasingly effective require community efforts in the areas of cluster system design, advancing the capabilities of the software stack, system management and monitoring, and the design of algorithms, methods, and applications to leverage the overall infrastructure. For IEEE Cluster 2026, which will be held September 22-25, 2026 in Alexandria, Virginia, United States of America, we again solicit high-quality original work that advances the state-of-the-art in clusters and closely related fields. All papers will be rigorously peer-reviewed for their originality, technical depth and correctness, potential impact, relevance to the conference, and quality of presentation. Generally research papers must clearly demonstrate novel research contributions, however papers reporting experiences are also welcome, but they must clearly describe the lessons learned and the resulting impact, along with the utility of the approach in comparison to previous work. Authors must indicate the primary topic area of their submissions from the four topic areas provided below. In addition, they may optionally rank their paper relative to the overall set of topics. Transversal and emerging topics such as AI for HPC, HPC for AI, quantum computing, accelerators, and many others, are welcome within the respective areas even if they are not mentioned explicitly. Papers are limited to 10 pages, although references do not need to fit within this page limit. IEEE Cluster 2026 follows a dual-anonymous review process. For an explanation and description of this review process, please refer to the following link: https://clustercomp.org/2026/dual_anonymous.html Guidelines for Artificial Intelligence (AI)-Generated Text The use of content generated by artificial intelligence (AI) in a paper (including but not limited to text, figures, images, and code) shall be disclosed in the acknowledgments section of any paper submitted to an IEEE publication. The AI system used shall be identified, and specific sections of the paper that use AI-generated content shall be identified and accompanied by a brief explanation regarding the level at which the AI system was used to generate the content. The use of AI systems for editing and grammar enhancement is common practice and, as such, is generally outside the intent of the above policy. In this case, disclosure as noted above is recommended. Please also refer to the IEEE Submission Policies Area 1: Application, Algorithms, and Libraries HPC and Big Data application studies on large-scale clusters Applications at the boundary of HPC and Big Data New applications for converged HPC/Big Data clusters Application-level performance and energy modeling and measurement Novel algorithms on clusters Hybrid programming techniques in applications and libraries (e.g., MPI+X) Cluster benchmarks Application-level libraries on clusters Effective use of clusters in novel applications Performance evaluation tools Area 2: Architecture, Network/Communications, and Management Node and system architecture for HPC and Big Data clusters Architecture for converged HPC/Big Data clusters Energy-efficient cluster architectures Packaging, power and cooling Accelerators, reconfigurable and domain-specific hardware Heterogeneous clusters Interconnect/memory architectures Single system/distributed image clusters Administration, monitoring and maintenance tools Area 3: Programming and System Software Cluster system software/operating systems Programming models for converged HPC/Big Data/Machine Learning systems System software supporting the convergence of HPC, Big Data, and Machine Learning processing Cloud-enabling cluster technologies and virtualization Energy-efficient middleware Cluster system-level protocols and APIs Cluster security Management of local, center-wide and disaggregate resources and job Programming and software development environments on clusters Fault tolerance and high-availability Administration, monitoring and maintenance tools Area 4: Data, Storage, and Visualization Cluster architectures for Big Data storage and processing Middleware for Big Data management Cluster-based cloud architectures for Big Data Storage systems supporting the convergence of HPC and Big Data processing File systems and I/O libraries Support and integration of non-volatile memory Visualization clusters and tiled displays Big Data/Large scale visualization tools Big Data application studies on cluster architectures
由 Dou Sun 最后更新于

录用率

平均录用率: 34% 13 年间 (2000–2012).

年份提交数录用数录用率(%)
20122015828.9%
20111403927.9%
20101073330.8%
20091004848%
2008922830.4%
20071064239.6%
20061274233.1%
20051384532.6%
20041504832%
20031644829.3%
20021164538.8%
2001743547.3%
20001443423.6%

相关会议

相关期刊

CCF全称影响因子出版商ISSN
Cluster Computing4.1Springer1386-7857
BSoftware & Systems Modeling3.2Springer1619-1366
BPattern Recognition9.1Elsevier0031-3203
BIEEE Transactions on Neural Networks and Learning Systems8.9IEEE1045-9227
BInformation Sciences6.0Elsevier0020-0255
BComputer Networks4.7Elsevier1389-1286
BComputers & Security5.4Elsevier0167-4048
BIEEE Transactions on Intelligent Transportation Systems8.4IEEE1524-9050
BJournal of Systems and Software3.8Elsevier0164-1212
BInformation Processing & Management6.9Elsevier0306-4573

评论 0

暂无评论。

请登录后发表评论