[hpc-announce] CfP: 30th IEEE International Conference on High Performance Computing, Data, and Analytics (HiPC)

Anand Panangadan anandvp at hipc.org
Tue Mar 28 22:44:59 CDT 2023


30th IEEE International Conference on High Performance Computing,
Data, and Analytics (HiPC)
December 18-21, 2023
Goa, India
https://hipc.org/

HiPC 2023 will be the 30th 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Authors are invited to submit original unpublished research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.

High Performance Computing tracks:

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;
- Algorithmic techniques for resource allocation and optimization;
- Provably efficient parallel and distributed algorithms for advanced
scientific computing and irregular applications;
- Classical and emerging computation models.

Architecture. 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):
- High performance processing architectures;
- Networks for high performance computing platforms;
- Memory, cache and storage architectures;
- Approaches to improve architectural properties;
- Emerging computational architectures.

Applications. This track invites papers that describe original
research on the design and implementation of scalable and high
performance applications for execution on parallel, distributed and
accelerated platforms, and related advances. Examples of topics of
interest include (but not limited to):
- Shared and distributed memory parallel applications;
- Methods, algorithms, and optimizations for scaling applications on
peta- and exa-scale platforms;
- Hardware acceleration of parallel applications;
- Application benchmarks and workloads 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;
- Techniques to enhance parallel performance;
- Techniques to enhance parallel application development and productivity;
- Techniques to deal with uncertainties, hardware/software resilience,
and fault tolerance;
- Software for cloud, data center, and exascale platforms;
- Software and programming paradigms for heterogeneous platforms.

Scalable Data Science tracks:

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, data (pre-)processing and pattern
discovery);
- Scalable algorithms that are designed to address the characteristics
of different data sources and settings;
- Scalable algorithms and techniques to reduce the complexity of
large-scale data;
- Scalable algorithms that are designed to address requirements in
different data-driven application domains;
- 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, and
computer vision applications.

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):
- New parallel and distributed algorithms and design techniques;
- Design of scalable system software to support various applications;
- Scalable system software for various architectures;
- Architectures and systems software to support various operations in
large data frameworks;
- Systems software for distributed data frameworks;
- Standards and protocols for enhancing various aspects of data analytics.

General Co-Chairs
- Chiranjib Sur, Shell, India
- Neelima Bayyapu, Manipal Institute of Technology, India

Vice General Co-Chairs
- Sanmukh Rao Kuppannagari, Case Western Reserve University, USA
- Vivek Yadav, International Institute of Information Technology,
Bangalore, India

Program Co-Chairs
- High Performance Computing: Yogish Sabharwal, IBM Research, India
- Scalable Data Science: Gerald F Lofstead II, Sandia National Laboratories, USA

Program Vice-Chairs
HPC Tracks
- Algorithms: Jee Choi, University of Oregon, USA
- Applications: Preeti Malakar, IIT Kanpur, India
- Architecture: Saurabh Gupta, AMD, India
- System Software: Daniele De Sensi, Sapienza University of Rome, Italy

Scalable Data Science Tracks
- Scalable Algorithms and Analytics: Venkat Chakaravarthy, IBM Research, India
- Scalable Systems and Software: Lena Oden, Argonne National Laboratory, USA

Steering Committee Chair
- Viktor K. Prasanna, University of Southern California, USA

Please see the following site for details: https://hipc.org/


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