April 24, 2017
May 1, 2017
2017 IEEE International Conference on Cluster Computing (Cluster 2017)
in cooperation with SIGHPC
Honolulu, Hawaii, USA
September 5-8, 2017
Abstract deadline: May 7th, 2017
Full Papers due: May 15th, 2017
Paper Acceptance Notification: June 30th, 2017
Camera-ready deadline: T.B.D.
Conference: September 5 – 8, 2017
Deadlines are Anywhere on Earth (AoE)
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 (http://www.clustercomp.org/), 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/ManyCores 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
Paper Format: Since the camera-ready version of accepted papers must be compliant with the IEEE Xplore format for publication, submitted papers must conform to the following Xplore layout, page limit, and font size.
– Submissions must be in PDF format.
– Submissions are required to be within 10 pages (Not counting references).
– Submissions must be single-spaced, 2-column numbered pages in IEEE Xplore format (8.5×11-inch paper, margins in inches — top:0.75, bottom:1.0, sides:0.625, and between columns:0.25, main text:10pt).
– Submissions are NOT double-blind. Author information can be included on the submission and will be visible to the reviewers.
– LaTeX and Word Templates are available here: http://www.ieee.org/conferences_events/conferences/publishing/templates.html
– Only web-based submissions are allowed.
– Please submit your paper via the submission system: https://easychair.org/conferences/?conf=ieeecluster2017
Naoya Maruyama, Riken, Japan
Todd Gamblin, Lawrence Livermore National Laboratory, USA
Gabriel Antoniu, INRIA, France
Richard Vuduc, Georgia Institute of Technology, USA
Michela Taufer, University of Delaware, USA and Aparna Chandramowlishwaran, UC-Irvine (Area 1)
Frank Mueller, North Carolina State University, USA (Area 2)
Kenjiro Taura, University of Tokyo, Japan (Area 3)
Maria Perez, Universidad Politecnica de Madrid, Spain and Robert Sisneros, UIUC, USA (Area 4)
Local Arrangements Chairs:
Henri Cassanova, University of Hawaii, USA
Lipyoew Lim, University of Hawaii, USA