Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Scott Sallinen"'
Autor:
Scott Sallinen, Matei Ripeanu
Publikováno v:
2021 IEEE/ACM 11th Workshop on Irregular Applications: Architectures and Algorithms (IA3).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5f63e45185a90bc56b961cf5f183d73c
https://doi.org/10.1109/ia354616.2021.00011
https://doi.org/10.1109/ia354616.2021.00011
Publikováno v:
IPDPS
Modern data generation is enormous; we now capture events at increasingly fine granularity, and require processing at rates approaching real-time. For graph analytics, this explosion in data volumes and processing demands has not been matched by impr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::46e03a26839eda8b4e8005cb0559386c
https://doi.org/10.1109/ipdps.2019.00108
https://doi.org/10.1109/ipdps.2019.00108
Publikováno v:
SC16: International Conference for High Performance Computing, Networking, Storage and Analysis.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ec03050cab8951e80c78feccdffdf90a
https://doi.org/10.1109/sc.2016.29
https://doi.org/10.1109/sc.2016.29
Publikováno v:
IPDPS
Stochastic Gradient Descent (SGD) is a popular optimization method used to train a variety of machine learning models. Most of SGD work to-date has concentrated on improving its statistical efficiency, in terms of rate of convergence to the optimal s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8d0fedcb3a87f26da6be17d8b8828ae5
https://doi.org/10.1109/ipdps.2016.107
https://doi.org/10.1109/ipdps.2016.107
Autor:
Maya Gokhale, Keita Iwabuchi, Roger Pearce, Satoshi Matsuoka, Brian Van Essen, Scott Sallinen
Publikováno v:
IPDPS Workshops
In many graph applications, the structure of the graph changes dynamically over time and may require real time analysis. However, constructing a large graph is expensive, and most studies for large graphs have not focused on a dynamic graph data stru
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c739bade49eff31ffc84464a2e436637
https://doi.org/10.1109/ipdpsw.2016.189
https://doi.org/10.1109/ipdpsw.2016.189
Publikováno v:
Euro-Par 2015: Parallel Processing Workshops ISBN: 9783319273075
Euro-Par Workshops
Euro-Par Workshops
Large scale-free graphs are famously difficult to process efficiently: the skewed vertex degree distribution makes it difficult to obtain balanced partitioning. Our research instead aims to turn this into an advantage by partitioning the workload to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9d3ceba18061be6482a02a614f470dfc
https://doi.org/10.1007/978-3-319-27308-2_20
https://doi.org/10.1007/978-3-319-27308-2_20
