Distributed Computing Throughput
Distributed Computing Throughput is a performance metric that quantifies the total volume of computational tasks successfully processed per unit time across interconnected nodes in a distributed system, distinguishing itself from related metrics by explicitly accounting for inter-node communication, coordination, and workload distribution efficiency.
19
Publications
539
Citations
65
Authors
41
Institutions
Publications per year
2017–2026
3
Parents
19
The Role of Network Topology for Distributed Machine Learning
Giovanni Neglia, Gianmarco Calbi, Don Towsley et al. · 2019 · 52 citations · Full text
1–5 of 19
65
2
h-index
University at Buffalo, State University of New York, United States
2 publications · 58 citations
2
h-index
University at Buffalo, State University of New York, United States
2 publications · 58 citations
2
h-index
1
h-index
1
h-index
1–5 of 65
41
Technion – Israel Institute of Technology
Haifa, Israel
2 h-index
3 publications
45 citations
Louisiana State University
Baton Rouge, United States
2 h-index
8 publications
270 citations
University of Tennessee at Knoxville
Knoxville, United States
1 h-index
2 publications
60 citations
University of Geneva
Geneva, Switzerland
1 h-index
2 publications
134 citations
Northwestern University
Evanston, United States
1 h-index
3 publications
291 citations
Stanford University
Stanford, United States
1 h-index
4 publications
56 citations

Oak Ridge National Laboratory
Oak Ridge, United States
1 h-index
2 publications
60 citations
Laboratoire Cognitions Humaine et Artificielle
Saint-Denis, France
1 h-index
1 publications
20 citations
1–8 of 41
TNSM · 2 publications · 65 citations
2
h-index
TPDS · 2 publications · 69 citations
2
h-index
1 publications · 30 citations
1
h-index
1 publications · 12 citations
1
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Conference · 1 publications · 14 citations
1
h-index
1–5