Conference Information
CLUSTER 2017 : IEEE Cluster
https://cluster17.github.io/
Submission Date:
2017-05-07 Extended
Notification Date:
2017-06-30
Conference Date:
2017-09-05
Location:
Hawaii, USA
Years:
19
CCF: c   CORE: a   Viewed: 9974   Tracked: 42   Attend: 3

Conference Location
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Call For Papers
IEEE Cluster 2017 is the 19th edition of the IEEE Cluster conference series, organized in cooperation with SIGHPC.

Clusters are the primary architecture for building today’s rapidly evolving cloud and HPC infrastructures, and are used to solve some of the most complex problems. The challenge to make them scalable, efficient, and more functional requires a community effort in the areas of cluster system design, management and monitoring, at the hardware, system, middleware and application levels.

Following the successes of previous IEEE Cluster conferences, for IEEE Cluster 2017, which will be held September 05-08, 2016 in Hawaii USA, 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. Research papers must clearly demonstrate novel research contributions while papers reporting experiences must clearly describe lessons learned and impact, along with the utility of the approach compared to previous ones.

2017 Highlight: Convergence of Big Data and High-Performance Computing. While the tools and cultures for High-Performance Computing and Big Data Analytics have evolved in divergent ways, both rely on cluster architectures. As we witness an increasing awareness that further progress in scientific research depends on both areas, the interoperability and scaling convergence of these two ecosystems is expected to be critical to the future. Therefore, we have chosen this year to highlight research topics in all areas expected to bring progress in understanding if, why and how clusters should support this convergence. Specific topics are dedicated to this direction within all conference areas alongside more traditional topics.

Authors must indicate the primary area of preference out of the four areas below. They may optionally rank the other areas. The paper may be accepted as a full 10-page paper, or the committee might decide to accept it as a short paper with 4 pages in the proceedings. Note: references are not counted in the above limits on the number of pages.

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
Performance modeling and measurement
Novel algorithms on clusters
Hybrid programming techniques (e.g., MPI+OpenMP)
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/ManyCore and 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 systems
System software supporting the convergence of HPC and Big Data processing
Cloud-enabling cluster technologies and virtualization
Energy-efficient middleware
Cluster system-level protocols and APIs
Cluster security
Resource and job management
Programming and software development environments on clusters
Fault tolerance and high-availability

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 visualization tools
Programming models for big data processing
Big data application studies on cluster architectures
Last updated by Dou Sun in 2017-04-29
Related Publications
Acceptance Ratio
YearSubmittedAcceptedAccepted(%)
20122015828.9%
20111403927.9%
20101073330.8%
20091004848%
2008922830.4%
20071064239.6%
20061274233.1%
20051384532.6%
20041504832%
20031644829.3%
20021164538.8%
2001743547.3%
20001443423.6%
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