Paper Submissions Due
Results Notification
Camera-ready Version Due
NPC 2019 Conference
May 15, 2019
Extended to May 22, 2019 (AOE)
Extended to May 31, 2019 (AOE)
(Firm deadline)
July 1, 2019
Extended to July 8, 2019
July 15, 2019
Extended to July 22, 2019
August 23 - August 24, 2019
in Hohhot, Inner Mongolia, China
University of California Irvine, USA
Tsinghua University, China
IBM T.J. Watson Research Center, USA
Shanghai Jiao Tong University, China
Japan
Université de Cergy-Pontoise, France
IBM T.J. Watson Research Center, USA
Xiaoxin Tang
Shanghai University of Finance and Economics
Network and Parallel Computing (NPC 2019)
August 23 - August 24, 2019
in Hohhot, Inner Mongolia, China
NPC is in its 16th year and previous conferences have attracted high quality papers and wide international participation. We will publish top papers from NPC 2019 in a special issue of the International Journal of Parallel Programming (IJPP) or Tsinghua Science and Technology. The proceedings will be published as part of Springer LNCS.
Submission of a paper to NPC’2019 entails a commitment that, if the paper is accepted, at least one of the authors will register and attend the conference to present the work. NPC’2019 Manuscripts should be submitted via EasyChair at the following link: https://easychair.org/conferences/?conf=npc2019.
High Performance Computing and Big Data are two main areas where NPC 2019 will provide a dynamic forum to explore, discuss and debate state-of-the-art technology issues and challenges. High-performance computers and big-data systems are tied inextricably to the broader computing ecosystem and its designs and market adoption. They also highlight information security needs and economic competitiveness in ways that distinguish them from most other scientific instruments. We strongly believe that the stake is high, and it is far beyond the boundaries of nations and continents, and should strongly encourage a broad international participation.
We share the view that, during the past decade, the tools and cultures of high-performance computing and big data analytics are diverging to the detriment of both, and the international community should find a unified path that can best serve the need of a broad spectrum of major application areas. Unlike other tools, which are limited to particular scientific domains, computational modeling and data analytics are applicable to all areas of science and engineering, as they breathe life into the underlying mathematics of scientific models.
Parallel and distributed applications and algorithms
• Parallel and distributed issues and opportunities on artificial intelligence application.
• Parallel algorithms for computational and data-enabled scientific, engineering, biological and medical applications
• Parallel algorithms for accelerators, neuromorphic architectures, and other emerging architectures
Parallel and distributed architectures and systems
• Emerging architectures and systems at all scales, from embedded to cloud.
• Systems for enabling parallelism at an extreme scale
• Power-efficient and green computing systems
• Neuromorphic architectures and cognitive computing accelerators
• Heterogeneous multicore architectures and accelerators
• In-Memory and near-data computing
• Network and interconnect architectures
• Storage systems in novel big data architectures
Parallel and distributed software environments and tools
• Programming models and compilation for existing and emerging platforms
• Dataflow programming models, frameworks, languages and environments for data-enabled platforms
• Virtualization of machines, networks, and storage
• I/O, file systems, and data management
• Resource management, scheduling, and load balancing
Inner Mongolia International Convention and Exhibition Center（内蒙古国际会展中心）
