会议名称(中文)：  2014 IEEE群集计算国际会议 
会议名称(英文)：  2014 IEEE International Conference on Cluster Computing (Cluster 2014) 
所属学科：  计算机科学理论,计算机系统结构与硬件,计算机应用技术 
开始日期：  2014-09-22 
结束日期：  2014-09-26 
所在国家：  西班牙 
所在城市：  西班牙 
具体地点：  Madrid, Spain 
主办单位：  IEEE Computer Society、Universidad Politecnica de Madrid, Spain 
 
[ 重要日期 ]
摘要截稿日期：  2014-04-24 
全文截稿日期：  2014-05-02 
论文录用通知日期：  2014-06-30
 
[ 会务组联系方式 ]  
会议网站：  http://www.cluster2014.org/ 
会议背景介绍：  Welcome to IEEE Cluster 2014. The conference will be held 22-26 September 2014 at the School of Industrial Engineering, UPM.

Clusters have become the workhorse for computational science and engineering research, powering innovation and discovery that advance science and society. They are the base for building today's rapidly evolving cloud and HPC infrastructures, and are used to solve some of the most complex problems.

Cluster 2014 welcomes paper submissions on innovative work from researchers and practitioners in academia, government, and industry that describe original research and development efforts in cluster computing. 
征文范围及要求：  Major topics of interest include, but are not limited to:
▪ Track 1: Cluster Design, Configuration and Administration
▪ Cluster Architecture for Big Data storage and processing
▪ Energy-efficient cluster architectures
▪ Node and system architecture
▪ Packaging, power and cooling
▪ Visualization clusters and tiled displays
▪ GPU and hybrid CPU/GPU clusters
▪ Interconnect/memory architectures
▪ Single system image clusters
▪ Administration and maintenance tools
▪ Track 2: Cluster Software, Middleware, Tools
▪ Middleware for Big Data management
▪ Big Data visualization tools
▪ Performance modeling for Big Data processing
▪ Performance evaluation, analysis and optimization
▪ Cloud-enabling cluster technologies and virtualization
▪ Energy-efficient middleware
▪ Protocols, libraries, and interfaces
▪ Lightweight communication protocols
▪ Security
▪ Resource and job management
▪ Scheduling and load balancing
▪ Reliability and high-availability architecture
▪ Fault tolerance, checkpointing and recovery
▪ Cost and performance implications of reliability
▪ Software environments and tools
▪ Track 3: Cluster Storage and File Systems
▪ Cluster support for Big Data processing
▪ Cloud storage for Big Data
▪ Storage support for Data-intensive computing
▪ Storage cluster architectures
▪ File systems and I/O libraries
▪ Track 4: Cluster Applications
▪ Programming models for Big Data processing
▪ Big Data Application studies on cluster architectures
▪ HPC Applications on GPUs
▪ Programming languages and environments
▪ Hybrid programming techniques (MPI+OpenMP, MPI+OpenCL, etc.)
▪ Benchmarking & profiling tools
▪ Performance prediction & modeling