Eun-Jin Im

684 citations
14 papers · 361 · h-index 7

Impact in

Papers in

Eun-Jin Im

12 papers receiving 340 citations

Peers

Eun-Jin Im
Comparison fields: 5 of 35
  • Hardware and Architecture 270
  • Computational Mathematics 11
  • Computer Networks and Communications 227
  • Computational Theory and Mathematics 75
  • Nuclear and High Energy Physics 31
Replace Pavel Tvrdı́k with:
Pavel Tvrdı́k Czechia
Ian Karlin United States
Mathieu Faverge France
Dhiraj Kalamkar United States
Padma Raghavan United States
Rajib Nath United States
Roy Williams United States
Tingxing Dong United States
Justin Holewinski United States
William Killian United States
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 20 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

14 of 14 papers shown
#Work
1 2004232
2
Optimizing Sparse Matrix Vector Multiplication on SMP.
199941
3 201132
4 201125
5 20118
6
Optimization of Sparse Matrix Kernels for Data Mining
20006
7 20136
8
Model-Based Memory Hierarchy Optimizations for Sparse Matrices
19985
9 20102
10
An Efficient Computation of Matrix Triple Products
20061
11 20131
12 20121
13 20111
14 20120

About Eun-Jin Im

Eun-Jin Im is a scholar working on Computer Networks and Communications, Hardware and Architecture, General Health Professions, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 14 papers that have together received 361 indexed citations. Recurring topics across this work include Parallel Computing and Optimization Techniques (3 papers), Caching and Content Delivery (2 papers), Health, Medicine and Society (2 papers), Distributed and Parallel Computing Systems (2 papers), Particle accelerators and beam dynamics (2 papers), Aging, Elder Care, and Social Issues (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 (270 citations), Computational Mathematics (11 citations), Computer Networks and Communications (227 citations), Computational Theory and Mathematics (75 citations) and Nuclear and High Energy Physics (31 citations). Eun-Jin Im has collaborated with scholars based in South Korea and United States. Frequent co-authors include Katherine Yelick, Richard Vuduc, Khaled Z. Ibrahim, Leonid Oliker, S. Ethier, Samuel Williams, Kamesh Madduri, John Shalf, MyungKeun Yoon and Youngman Kim. Their work appears in journals such as The International Journal of High Performance Computing Applications, Parallel Computing, Journal of Parallel and Distributed Computing, PPSC and eScholarship (California Digital Library).

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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