Conference Information
HiPC 2019: International Conference on High Performance Computing
http://hipc.org/
Submission Date:
2019-06-21 Extended
Notification Date:
2019-09-30
Conference Date:
2019-12-17
Location:
Hyderabad, India
Years:
26
CCF: c   QUALIS: b1   Viewed: 11919   Tracked: 48   Attend: 10

Conference Location
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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 areas of high-performance computing and data science. The meeting focuses on all aspects of high-performance computing systems, and data science and analytics, and their scientific, engineering, and commercial applications.

Authors are invited to submit original unpublished research manuscripts that demonstrate current research in all areas of high-performance computing, and data science and analytics, covering all traditional areas and emerging topics including from machine learning, big data analytics, and blockchain.  Each submission should be submitted to one of the tracks listed under the two broad themes of High-Performance Computing and Data Science.

 High-Performance Computing

Algorithms: This track invites papers that describe original research on developing new parallel and distributed computing algorithms, and related advances. Examples of topics that are of interest include (but not limited to)

• New parallel and distributed algorithms and design techniques;

• Advances in enhancing algorithmic properties or providing guarantees (e.g., fault tolerance, resilience, concurrency, data locality, communication-avoiding);

• Classical and emerging computation models (e.g., parallel/distributed models, quantum computing, neuromorphic and other bioinspired models);

• Provably efficient parallel and distributed algorithms for advanced scientific computing and irregular applications (e.g., numerical linear algebra, graph algorithms, computational biology); and

• Algorithmic techniques for resource allocation and optimization (e.g., scheduling, load balancing, resource management);

Architectures: This track invites papers that describe original research on the design and evaluation of high-performance computing architectures, and related advances. Examples of topics of interest include (but not limited to):

• Design and evaluation of high-performance processing architectures (e.g., reconfigurable, system-on-chip, many cores, vector processors);

• Design and evaluation of networks for high-performance computing platforms (e.g., interconnect topologies, network-on-chip);

• Design and evaluation of memory, cache and storage architectures (e.g., 3D, photonic, Processing-In-Memory, NVRAM, burst buffers, parallel I/O);

• Approaches to improve architectural properties (e.g., energy/power efficiency, reconfigurable, resilience/fault tolerance, security/privacy); and

• Emerging computational architectures (e.g., quantum computing, neuromorphic and other bioinspired architectures).

Applications: This track invites papers that describe original research on the design and implementation of scalable applications for execution on parallel and distributed platforms, and related advances. Examples of topics of interest include (but not limited to):

• Design and implementation of shared and distributed memory parallel applications (e.g., scientific computing and industry applications, emerging applications in IoT and life sciences – biology, medicine, chemistry, etc.);

• Design and simulation methodologies for scaling applications on peta and exascale platforms (e.g., co-design approaches, hardware/software co-design, heterogeneous and hybrid programming);

• Hardware acceleration of parallel applications (e.g., CPU/GPUs, multi-GPU clusters, FPGA, vector processors, manycore); and

• Design of application benchmarks for parallel and distributed platforms.


Systems Software: This track invites papers that describe original research on the design, implementation, and evaluation of systems software for high-performance computing platforms, and related advances. Examples of topics of interest include (but not limited to):

• Scalable systems and software architectures for high-performance computing (e.g., middleware, operating systems, I/O services);

• Techniques to enhance parallel performance (e.g., compiler/runtime optimization, learning from application traces, profiling);

• Techniques to enhance parallel application development and productivity (e.g., Domain-Specific Languages, programming environments, performance/correctness checking and debugging);

• Techniques to deal with uncertainties, hardware/software resilience, and fault tolerance;

• Software for the cloud, data center, and exascale platforms (e.g., middleware tools, schedulers, resource allocation, data migration, load balancing); and

• Software and programming paradigms for heterogeneous platforms (e.g., libraries for CPU/GPU, multi-GPU clusters, and other accelerator platforms);

Data Science

Scalable Algorithms and Analytics: This track invites papers that describe original research on developing scalable algorithms for data analysis at scale, and related advances. Examples of topics of interest include (but not limited to):

• New scalable algorithms for fundamental data analysis tasks (supervised, unsupervised learning, and pattern discovery);

• Scalable algorithms that are designed to address the characteristics of different data sources and settings (e.g., graphs, social networks, sequences, data streams);

• Scalable algorithms and techniques to reduce the complexity of large-scale data (e.g., streaming, sublinear data structures, summarization, compressive analytics);

• Scalable algorithms that are designed to address requirements in different data-driven application domains (e.g., life sciences, business, agriculture); and

• Scalable algorithms that ensure the transparency and fairness of the analysis.

Scalable Systems and Software: This track invites papers that describe original research on developing scalable systems and software for handling data at scale, and related advances. Examples of topics of interest include (but not limited to):

• Design of scalable system software to support various applications (e.g., recommendation systems, web search, crowdsourcing applications, streaming applications);

• Design of scalable system software for various architectures (e.g., OpenPower, GPUs, FPGAs);

• Architectures and systems software to support various operations in large data frameworks (e.g., storage, retrieval, automated workflows, data organization, visualization, visual analytics, human-in-the-loop);

• Design and implementation of systems software for distributed data frameworks (e.g., distributed file system, virtualization, cloud services, resource optimization, scheduling); and

• Standards and protocols for enhancing various aspects of data analytics (e.g., open data standards, privacy-preserving, and secure schemes).

One or more best paper awards will be given for outstanding contributed papers.
Last updated by Dou Sun in 2019-06-02
Acceptance Ratio
YearSubmittedAcceptedAccepted(%)
20181513321.9%
20171844222.8%
20161604025%
20152014823.9%
20142124923.1%
20131964925%
20121634125.2%
20112064019.4%
20102084019.2%
20093203510.9%
20083174614.5%
20072535220.6%
20063355215.5%
20053625013.8%
20042144822.4%
20031644829.3%
20021455739.3%
20011082926.9%
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