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IEEE

HiPC 2026 — 33rd IEEE International Conference on High Performance Computing, Data, and Analytics (HiPC)

Location: Bengaluru , India

Submission deadline: 6/17/2026

Conference dates: 12/16/2026 - 12/19/2026

Research H-index
14

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

HiPC is the annual IEEE international conference on high performance computing, data and analytics held in India. It invites original contributions on high performance computing, parallel algorithms, data analytics, scalable systems, GPU and accelerator computing, HPC for AI, distributed computing. Submissions may take the form of full research papers, extended abstracts and poster presentations reporting theoretical, computational and experimental work, as well as industrial case studies and applications. The meeting brings together researchers, engineers, industry practitioners and students to exchange recent results and discuss emerging directions in the field. Accepted papers are published in the conference proceedings and submitted to IEEE Xplore.

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.

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.

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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