Data Locality Scheduling
Data Locality Scheduling is a distributed scheduling strategy that minimizes data transfer overhead by prioritizing the assignment of computational tasks to processing nodes physically co-located with their required data.
99
Publications
7.8K
Citations
304
Authors
132
Institutions
Publications per year
2017–2026
21
Parents
99
Mesos: a platform for fine-grained resource sharing in the data center
Benjamin Hindman, Andy Konwinski, Matei Zaharia et al. · UC Berkeley · 2011 · 1.6K citations
Matei Zaharia, Dhruba Borthakur, Joydeep Sen Sarma et al. · 2010 · 1.4K citations
Job Scheduling for Multi-User MapReduce Clusters
Matei Zaharia, Dhruba Borthakur, Joydeep Sen Sarma et al. · 2009 · 324 citations
SpArch: Efficient Architecture for Sparse Matrix Multiplication
Zhekai Zhang, Hanrui Wang, Song Han et al. · 2020 · 257 citations
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132
University of Illinois Urbana-Champaign
Urbana, United States
8 h-index
23 publications
1K citations
University of California, Berkeley
Berkeley, United States
6 h-index
30 publications
18.5K citations
Arizona State University
Tempe, United States
5 h-index
13 publications
663 citations

Huazhong University of Science and Technology
Wuhan, China
4 h-index
16 publications
559 citations

Southeast University
Nanjing, China
4 h-index
14 publications
1.1K citations
IBM (United States)
Armonk, United States
4 h-index
9 publications
397 citations

University of Colorado Colorado Springs
Colorado Springs, United States
4 h-index
6 publications
255 citations
Microsoft (United States)
Redmond, United States
4 h-index
11 publications
1K citations
1–8 of 132
TPDS · 4 publications · 142 citations
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3 publications · 99 citations
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Repository · 3 publications · 61 citations
3
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2 publications · 57 citations
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TON · 2 publications · 201 citations
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