[hpc-announce] CfP: Paper submission deadline approaching: 29th IEEE International Conference on High Performance Computing, Data & Analytics (HiPC 2022)

[Anand Panangadan]

HiPC 2022
29th IEEE International Conference on High Performance Computing, Data
& Analytics

Dec. 18-21, 2022
Bengaluru, India
Website: http://www.hipc.org

CALL FOR PAPERS

HiPC 2022 will be the 29th 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Authors are invited to submit original unpublished research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. Each submission should be submitted to one of the
six tracks listed under the two broad themes of High Performance
Computing and Data Science.
Up to two best paper awards will be given for outstanding contributed papers.

Depending on how the COVID-19 pandemic situation evolves, the
presentation may be in person or in a virtual format.
Authors of selected high-quality papers in HiPC 2022 will be invited
to submit extended versions of their papers for possible publication
in a special issue of the Journal of Parallel and Distributed
Computing (JPDC).

HIGH PERFORMANCE COMPUTING

Algorithms:
- New parallel and distributed algorithms and design techniques;
- Advances in enhancing algorithmic properties or providing guarantees
(e.g., concurrency, data locality, communication-avoiding,
asynchronous, hybrid CPU-GPU algorithms, fault tolerance,
resilience,);
- Algorithmic techniques for resource allocation and optimization
(e.g., scheduling, load balancing, resource management);
- Provably efficient parallel and distributed algorithms for advanced
scientific computing and irregular applications (e.g., numerical
linear algebra, graph algorithms, computational biology);
- Classical and emerging computation models (e.g.,
parallel/distributed models, quantum computing, neuromorphic and other
bioinspired models).

Architecture:
- High performance processing architectures (e.g., reconfigurable,
system-on-chip, many cores, vector processors);
- Networks for high performance computing platforms (e.g.,
interconnect topologies, network-on-chip);
- 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);
- Emerging computational architectures (e.g., quantum computing,
neuromorphic and other bioinspired architectures).

Applications:
- Shared and distributed memory parallel applications (e.g.,
scientific computing, simulation and visualization applications, graph
and irregular applications, data-intensive applications,
science/engineering/industry applications, emerging applications in
IoT and life sciences, etc.);
- Methods, algorithms, and optimizations for scaling applications on
peta- and exa-scale platforms (e.g., co-design of hardware and
software, heterogeneous and hybrid programming);
- Hardware acceleration of parallel applications (e.g., GPUs, FPGA,
vector processors, manycore);
- Application benchmarks and workloads for parallel and distributed platforms.

Systems Software:
- 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 cloud, data center, and exascale platforms (e.g.,
middleware tools, schedulers, resource allocation, data migration,
load balancing);
- Software and programming paradigms for heterogeneous platforms
(e.g., libraries for CPU/GPU, multi-GPU clusters, and other
accelerator platforms).

SCALABLE DATA SCIENCE
Scalable Algorithms and Analytics:
- New scalable algorithms for fundamental data analysis tasks
(supervised, unsupervised learning, data (pre-)processing 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, health sciences);
- Scalable algorithms that ensure the transparency and fairness of the analysis;
- Case studies, experimental studies, and benchmarks for scalable
algorithms and analytics;
- Scaling and accelerating machine learning, deep learning, natural
language processing and computer vision applications.

Scalable Systems and Software:
- New parallel and distributed algorithms and design techniques;
- Design of scalable system software to support various applications
(e.g., recommendation systems, web search, crowdsourcing applications,
streaming applications);
- 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);
- Systems software for distributed data frameworks (e.g., distributed
file system, data deduplication, virtualization, cloud services,
resource optimization, scheduling);
- Standards and protocols for enhancing various aspects of data
analytics (e.g., open data standards, privacy-preserving, and secure
schemes).

Important dates
- Submission site open: June 15, 2022
- Abstract submissions: July 4, 2022 AOE
- Full Paper submissions: July 8, 2022 AOE
- First-round Author notifications: September 12, 2022
- Submission of revised papers along with response to reviews: October 10, 2022
- Author notification for revised papers: November 1, 2022
- Camera-ready version: November 15, 2022
- Conference dates: December 18-21, 2022

General Co-chairs:
- Chiranjib Sur, Shell, India
- Neelima Bayyapu, Consultant, India

Vice General Co-chairs:
- Sanmukh Rao Kuppannagari, University of Southern California, USA
- Vivek Yadav, IIIT-Bangalore, India- -

Program Co–chairs:
- High performance computing: Sathish Vadhiyar, Indian Institute of
Science, India
- Data science: Jun Wang, University of Central Florida, USA

Program Vice-Chairs
HPC TRACKS
- Algorithms: Thomas Herault, University of Tennessee, USA
- Applications: Yogish Sabharwal, IBM IRL, India
- Architecture: Diana Goehringer, TU Dresden, Germany
- System Software: Jyothi Vedurada, IIT, Hyderabad

DATA SCIENCE TRACKS
- Scalable Algorithms and Analytics: Zhishan Guo, University of
Central Florida, USA
- Scalable Systems and Software: Dan Huang, Sun Yat-Sen University, PRC

Steering committee chair:
- Viktor K. Prasanna, University of Southern California, USA