Scholar4AI Updated 2026-11-02 12:20 UTC+09:00

IPDPS: IEEE International Parallel and Distributed Processing Symposium (was IPPS and SPDP) — papers and institutions, 2024–2026

Papers of IPDPS, IEEE International Parallel and Distributed Processing Symposium (was IPPS and SPDP) (CORE rank A; field: Distributed computing and systems software, Computer science (general)), 2024–2026: per year, the institutions, countries and topics, and the most-cited papers. Ranks are those of the CORE Conference Rankings, edition ICORE2026 (portal.core.edu.au), applied to every year shown. Papers are the works OpenAlex lists for the conference's proceedings, or for the issues of a conference-linked journal that publishes its papers (such as PACMPL, PVLDB or ACM TOG), by publication year; workshops, companion volumes, posters and Findings are left out. A paper counts once for each institution and country among its authors' affiliations as OpenAlex assigns them; papers without affiliations in OpenAlex count for none. For conferences published in book series that OpenAlex files as one source (LNCS, LIPIcs, SIAM and IOS Press proceedings), the papers are found by DOI: those of the volumes with the conference's title in Crossref, or LIPIcs DOIs, which name the conference. Conferences without DOIs (PMLR, USENIX, OpenReview) are listed without counts. A dash (–) for a year means OpenAlex has no papers of the conference for it yet: the proceedings are not published or indexed yet, or the conference did not meet that year (some meet every other year). 2026 counts what OpenAlex had indexed by 2026-11-02. Also as Markdown.

Share: X LinkedIn Bluesky Threads Facebook Reddit Hacker News Hatena LINE Email
Copy:

← All conferences

Key findings#

  • IPDPS (IEEE International Parallel and Distributed Processing Symposium (was IPPS and SPDP)) is rated A by the CORE Conference Rankings (ICORE2026); OpenAlex lists 333 of its papers from 2024–2026 (100 in 2024, 118 in 2025, 115 in 2026).
  • By the countries of the authors' institutions, the most papers come from the United States (160), China (68) and Germany (19).
  • The institutions with the most papers are Argonne National Laboratory (24), The Ohio State University (14) and Pacific Northwest National Laboratory (12).
  • Its most-cited paper of these years is “Benchmarking and Dissecting the Nvidia Hopper GPU Architecture” (2024), with 58 citations.
  • OpenAlex has affiliations for only 90% of its papers, so the institution and country counts are incomplete.

Papers per year#

IPDPS papers per publication year in OpenAlex (–: not in OpenAlex yet)
YearPapersWith affiliations
202410088
2025118110
2026115101

Sources#

Where OpenAlex lists the papers
OpenAlex sourceWhich papers
S4210174069The conference's proceedings

Who publishes there#

Institutions with the most IPDPS papers, 2024–2026
#InstitutionPapers
1Argonne National Laboratory24
2The Ohio State University14
3Pacific Northwest National Laboratory12
4Georgia Institute of Technology11
5University of Iowa10
6Lawrence Berkeley National Laboratory10
7National University of Defense Technology10
8Chinese Academy of Sciences10
9University of Delaware10
10Institut national de recherche en sciences et technologies du numérique9
Countries with the most IPDPS papers, 2024–2026
#CountryPapers
1United States160
2China68
3Germany19
4United Kingdom16
5France13
6Switzerland12
7Spain12
8Japan8
9Hong Kong7
10Italy6
Keywords with the most IPDPS papers, 2024–2026
#KeywordPapers
1GPU acceleration32
2energy efficiency20
3high-performance computing18
4scalability16
5graph neural networks14
6large language models14
7exascale computing12
8load balancing10
9lossy compression10
10federated learning9

Most-cited papers#

Most-cited IPDPS papers of 2024–2026 (citations counted by OpenAlex)
#CitationsTitlePublishedAuthors
158Benchmarking and Dissecting the Nvidia Hopper GPU Architecture2024-05-27Weile Luo, Ruibo Fan, Zeyu Li et al.
222An Optimized Error-controlled MPI Collective Framework Integrated with Lossy Compression2024-05-27Jiajun Huang, Sheng Di, Xiaodong Yu et al.
319MeanCache: User-Centric Semantic Caching for LLM Web Services2025-06-03Waris Gill, Mohamed Elidrisi, Pallavi Kalapatapu et al.
419Comparative Study of Large Language Model Architectures on Frontier2024-05-27Junqi Yin, Avishek Bose, Guojing Cong et al.
518LightDAG: A Low-latency DAG-based BFT Consensus through Lightweight Broadcast2024-05-27Xiaohai Dai, Guanxiong Wang, Jiang Xiao et al.
616Performance Characterization of CXL Memory and Its Use Cases2025-06-03Xi Wang, Jie Liu, Jianbo Wu et al.
716Tackling Cold Start in Serverless Computing with Multi-Level Container Reuse2024-05-27Amelie Chi Zhou, Rongzheng Huang, Zhoubin Ke et al.
815Accelerating Lossy and Lossless Compression on Emerging BlueField DPU Architectures2024-05-27Yuke Li, Arjun Kashyap, Weicong Chen et al.
913HiCCL: A Hierarchical Collective Communication Library2025-06-03Mert Hidayetoğlu, Simon Garcia de Gonzalo, Elliott Slaughter et al.
1013Optimizing General Matrix Multiplications on Modern Multi-core DSPs2024-05-27Kainan Yu, Xinxin Qi, Peng Zhang et al.

Conferences in the same field#

ICRA · WWW · DAC · INFOCOM · MobiCom · ASPLOS · ISCA · SIGCOMM · HPCA · PODC · MICRO · SOSP · PerCom · IROS · DATE · SC · ICWS · ICDCS · MobiSys · EuroSys · IMC · ICS · ICCAD · DSN · ISLPED · HPDC · MMSys · ITC · FPGA · ICSOC · RTAS · MSWIM · CoNEXT · RTSS · DISC · Middleware · HotOS

Cite this page

Scholar4AI. (2026). IPDPS: IEEE International Parallel and Distributed Processing Symposium (was IPPS and SPDP) — papers and institutions, 2024–2026. Journal and Conference Research Rankings. Retrieved November 2, 2026, from https://scholar4ai.com/proceedings/ipdps

@misc{scholar4ai-proceedings-ipdps,
  author = {{Scholar4AI}},
  title = {IPDPS: IEEE International Parallel and Distributed Processing Symposium (was IPPS and SPDP) — papers and institutions, 2024–2026},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/proceedings/ipdps}},
  urldate = {2026-11-02},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

Free to reuse for non-commercial purposes with attribution (CC BY-NC 4.0).