Eun-Jin Im

691 citations
18 papers · 523 · h-index 7

Impact in

Papers in

Eun-Jin Im

15 papers receiving 493 citations

Peers

Eun-Jin Im
Comparison fields: 5 of 38
  • Hardware and Architecture 400
  • Computational Mathematics 19
  • Computer Networks and Communications 335
  • Computational Theory and Mathematics 121
  • Nuclear and High Energy Physics 36
Replace Pavel Tvrdı́k with:
Pavel Tvrdı́k Czechia
Ian Karlin United States
Dhiraj Kalamkar United States
Tingxing Dong United States
Mathieu Faverge France
Susan Ostrouchov United States
Rajib Nath United States
Roman Iakymchuk Sweden
A. Monakov Russia
Mawussi Zounon United Kingdom
Eun-Jin Im relative to Pavel Tvrdı́k Czechia Pavel Tvrdı́k's profile →
Citations per field
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Pavel Tvrdı́k · 1×
Citations per year

Countries citing papers authored by Eun-Jin Im

Since Specialization
Citations

This map shows the geographic impact of Eun-Jin Im's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Eun-Jin Im with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eun-Jin Im more than expected).

Fields of papers citing papers by Eun-Jin Im

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Eun-Jin Im. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Eun-Jin Im. The network helps show where Eun-Jin Im may publish in the future.

Co-authors

The 25 scholars most cited alongside Eun-Jin Im, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Eun-Jin Im Line = papers co-authored together Eun-Jin Im links everyone, so they are left out of the graph.

All Works

18 of 18 papers shown
#Work
1 2004257
2 2001113
3
Optimizing Sparse Matrix Vector Multiplication on SMP.
199950
4 201137
5 201129
6 20118
7 20137
8
Optimization of Sparse Matrix Kernels for Data Mining
20006
9
Model-Based Memory Hierarchy Optimizations for Sparse Matrices
19986
10 20102
11 20122
12 20032
13
An Efficient Computation of Matrix Triple Products
20061
14 20111
15 20061
16 20131
17 20250
18 20120

About Eun-Jin Im

Eun-Jin Im is a scholar working on Hardware and Architecture, Computer Networks and Communications, Artificial Intelligence, Computational Theory and Mathematics and General Health Professions, having authored 18 papers that have together received 523 indexed citations. Recurring topics across this work include Parallel Computing and Optimization Techniques (6 papers), Distributed and Parallel Computing Systems (4 papers), Matrix Theory and Algorithms (3 papers), Network Packet Processing and Optimization (2 papers), Aging, Elder Care, and Social Issues (2 papers), Health, Medicine and Society (2 papers), Hermeneutics and Narrative Identity (2 papers) and Interconnection Networks and Systems (2 papers). The work is most often cited by research in Hardware and Architecture (400 citations), Computational Mathematics (19 citations), Computer Networks and Communications (335 citations), Computational Theory and Mathematics (121 citations) and Nuclear and High Energy Physics (36 citations). Eun-Jin Im has collaborated with scholars based in South Korea, United States and Spain. Frequent co-authors include Katherine Yelick, Richard Vuduc, S. Ethier, Samuel Williams, Khaled Z. Ibrahim, Leonid Oliker, Kamesh Madduri, John Shalf, MyungKeun Yoon and Youngman Kim. Their work appears in journals such as Parallel Computing, Journal of Parallel and Distributed Computing, IEEE Access, The International Journal of High Performance Computing Applications and Lecture notes in computer science.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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