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29th IEEE International Conference on High Performance Computing, Data, and Analytics

29th IEEE International Conference on High Performance Computing, Data, and Analytics (HiPC)

Name: International Conference on High Performance Computing
Start: 2022-12-18
End: 2022-12-21
Location: Bengaluru

Location: Bengaluru , India

Submission deadline: 7/4/2022

Conference dates: 12/18/2022 - 12/21/2022

Research H-index

Official website

Ranking & Metrics

Discipline name	Position	Best Scientists	Publications	D-Index
Electronics and Electrical Engineering	817	4	5	2
Computer Science	191	104	160	15

Call for Papers

High Performance Computing

Topics for papers include, but are not limited to the topics given under the categories below.

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., 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. 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 (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. 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 (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. 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 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

Topics for papers include, but are not limited to the topics given under the categories below.

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 (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);
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 (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).

Overview

This comprehensive ranking presents a curated list of scientific conferences within the dynamic field of Engineering and Technology. Developed by Research.com—one of the preeminent platforms for science research and trusted data across all major scientific disciplines since 2014—this ranking stands as a reliable guide for academics, professionals, and institutions seeking to identify influential conferences worldwide.

The ranking positions are determined using a unique bibliometric score, meticulously designed by Research.com experts. This score takes into account both the estimated h-index and the number of leading scientists who have contributed to each conference in the past three years, ensuring a multi-dimensional and objective assessment of scientific impact within the field.

Impact Score values represented in the ranking were carefully gathered on 2024-11-27, providing the most up-to-date evaluation of conference performance. The rigorous selection process underpinning this ranking involved an initial examination of over 2,262 conferences. Each conference was scrutinized through an in-depth review of more than 26,934 scientific documents, which were authored by a cohort of 9,385 distinguished and respected scholars in Engineering and Technology over the past three years.

This extended investigation not only highlights the commitment to quality and accuracy by the Research.com team, but also underscores the depth and complexity embedded within the analysis. For comprehensive information regarding the methodology and computational approach behind the ranking scores, please visit our Methodology Page.

Papers citation over time

A key indicator for each conference is its effectiveness in reaching other researchers with the papers published at that venue.

The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.

Top authors and change over time

The top authors publishing at IEEE International Conference on High Performance Computing, Data, and Analytics (based on the number of publications) are:

Jack Dongarra (61 papers) published 8 papers at the last edition, 5 less than at the previous edition,
Laxmikant V. Kale (37 papers) published 3 papers at the last edition,
Martin Schulz (37 papers) published 7 papers at the last edition the same number as at the previous edition,
Bormin Huang (30 papers) published 1 paper at the last edition, 4 less than at the previous edition,
William Gropp (25 papers) published 2 papers at the last edition, 1 more than at the previous edition.

The overall trend for top authors publishing at this conference is outlined below. The chart shows the number of publications at each edition of the conference for top authors.

Top affiliations and change over time

Only papers with recognized affiliations are considered

The top affiliations publishing at IEEE International Conference on High Performance Computing, Data, and Analytics (based on the number of publications) are:

Argonne National Laboratory (185 papers) published 11 papers at the last edition, 8 less than at the previous edition,
IBM (142 papers) published 17 papers at the last edition, 1 less than at the previous edition,
Oak Ridge National Laboratory (139 papers) published 15 papers at the last edition, 1 less than at the previous edition,
Lawrence Berkeley National Laboratory (134 papers) published 16 papers at the last edition, 8 more than at the previous edition,
Lawrence Livermore National Laboratory (131 papers) published 17 papers at the last edition, 1 more than at the previous edition.

The overall trend for top affiliations publishing at this conference is outlined below. The chart shows the number of publications at each edition of the conference for top affiliations.

Publication chance based on affiliation

The publication chance index shows the ratio of articles published by the best research institutions at the conference edition to all articles published within that conference. The best research institutions were selected based on the largest number of articles published during all editions of the conference.

The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.

During the most recent 2016 edition, 9.88% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 24.12% were posted by at least one author from the top 10 institutions publishing at the conference. Another 11.62% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 11.84% of all publications and 52.41% were from other institutions.

Returning Authors Index

A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of conferences they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same conference from year to year.

The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the conference in relation to all participants in a given year.

Returning Institution Index

The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.

The experience to innovation index

Our experience to innovation index was created to show a cross-section of the experience level of authors publishing at a conference. The index includes the authors publishing at the last edition of a conference, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).

The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:

Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

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