Performance Prediction
Performance Prediction is a methodological framework within Parallel Performance Analysis that estimates the execution behavior of parallel systems or algorithms under varying conditions by modeling performance metrics such as speedup, scalability, and resource utilization based on architectural, algorithmic, and workload-specific parameters.
3.5K
Publications
162.1K
Citations
10.7K
Authors
2.6K
Institutions
Publications per year
2017–2026
1.4K
3.5K
Machine learning in materials science
Jing Wei, Xuan Chu, Xiangyu Sun et al. · InfoMat · 2019 · 1K citations · Full text
Natural Sciences, Materials Engineering, Materials Science +14
1–5 of 3.5K
10.7K
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11
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2.6K
Tsinghua University
Beijing, China
25 h-index
87 publications
4.5K citations
University of Illinois Urbana-Champaign
Urbana, United States
24 h-index
101 publications
4.4K citations

Centre National de la Recherche Scientifique
Paris, France
21 h-index
71 publications
3.3K citations
Carnegie Mellon University
Pittsburgh, United States
21 h-index
44 publications
2.6K citations
IBM (United States)
Armonk, United States
21 h-index
54 publications
5.7K citations

Xi'an Jiaotong University
Xi'an, China
20 h-index
84 publications
3.6K citations

Zhejiang University
Hangzhou, China
20 h-index
84 publications
6.2K citations

Shanghai Jiao Tong University
Shanghai, China
20 h-index
119 publications
4.4K citations
1–8 of 2.6K
39 publications · 2.3K citations
25
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38 publications · 1.7K citations
25
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31 publications · 1.3K citations
23
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32 publications · 1.6K citations
23
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26 publications · 2K citations
23
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1–5