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HiPC 2019 : 26th IEEE International Conference on High Performance Computing, Data, and Analytics

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Conference Series : High Performance Computing
 
Link: https://hipc.org/call-for-papers/
 
When Dec 17, 2019 - Dec 20, 2019
Where Hyderabad, India
Abstract Registration Due Jun 7, 2019
Submission Deadline Jun 14, 2019
Notification Due Sep 30, 2019
Final Version Due Oct 14, 2019
 

Call For Papers

HiPC 2019 will be the 26th edition of the IEEE International Conference on High Performance Computing, Data, Analytics and Data Science. HiPC serves as a forum to present current work by researchers from around the world as well as highlight activities in Asia in the area high performance computing. The meeting focuses on all aspects of high performance computing systems and their scientific, engineering, and commercial applications. In 2016, to keep pace with new computing trends, the conference added two new areas of interest to its name, Data and Analytics, as reflected in the list below.

Authors are invited to submit original unpublished research manuscripts that demonstrate current research in all areas of high performance computing, data and analytics. Topics include (but are not limited to):

High Performance Computing


Algorithms

• Design of Parallel and Distributed Algorithms

• Algorithmic Techniques to Improve Energy and Power Efficiency

• Quantum and Bio-Inspired Algorithms

• Resilient and Fault Tolerant Algorithms

• Parallel Algorithms for Numerical Linear Algebra

• Concurrent Algorithms and Data Structures

• Load Balancing, Scheduling and Resource Management

• Parallel Graph Algorithms

• Algorithms for Combinatorial Scientific Computing

• Parallel Algorithms for Computational Biology

• Streaming Algorithms


Architectures

• Interconnection Networks and Architectures

• Cache/Memory Architecture for High Performance Computing

• High Performance/Scalable Storage Systems

• Power-Efficient and Reconfigurable Architectures

• Quantum and Bio-Inspired Architectures

• Software Support and Advanced Micro-architecture Techniques

• Resilient and Fault Tolerant Architectures


Applications

• Big Data Computing and Applications

• Cross-Cutting Methods such as Co-Design of Parallel Algorithms, Software, and Architectures

• Emerging Applications such as Biotechnology, IoT, and Nanotechnology

• Hardware Acceleration for Parallel Applications

• Parallelism in Scientific Data Visualization and Visual Analytics

• Scientific/Engineering/Industrial Applications and Workloads

• Scalable Machine Learning and Data Mining Applications

• Scalable Graph and other Irregular Applications

• Design of Simulation Applications and Peta- and Exascale Applications

Systems Software

• Big Data Analytics Systems and Software Architectures

• Compiler Technologies for High-Performance Computing

• Exascale Computing, Cloud Platforms, Data Center Architectures and Services

• Parallel Languages, Programming Environments, and Performance Assessment

• Operating Systems for Scalable High -Performance Computing

• Hybrid Parallel Programming with GPUs and Accelerators

• Dealing with Uncertainties, Resilient/Fault-Tolerant Systems


Data Science


Big Data Algorithms and Analytics

• Transparent and interpretable predictive models

• Socially responsible learning

• Learning with changing environment, domain adaptation

• Learning with structured input and output

• Model evolution

• Large-scale Graph and network modeling and analytics

• Stream data analytics

• Model evolution

• Unsupervised learning


Big Data Systems and Software

• Data science applications in healthcare, education, social science, business, transportation, energy, telecommunications, science, and humanities

• Social mining analytics and applications

• Visual analytic systems and software using large-scale data

• Web search and recommendation systems

• Social impact systems using big data

• Privacy preserving big data software

• Massive, cross-media, streaming systems

• Human-in-the-loop systems

• Crowdsourcing and collective intelligence applications

• Large-scale data science for the social good

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