Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Alexey Tumanov"'
Autor:
Romil Bhardwaj, Alexey Tumanov, Stephanie Wang, Richard Liaw, Philipp Moritz, Robert Nishihara, Ion Stoica
Publikováno v:
Proceedings of the 13th Symposium on Cloud Computing.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::08c5a5b8847ef2db0689ae023cbba165
https://doi.org/10.1145/3542929.3563498
https://doi.org/10.1145/3542929.3563498
Autor:
Yixuan Luo, Payman Behnam, Kiran Thorat, Zhuo Liu, Hongwu Peng, Shaoyi Huang, Shu Zhou, Omer Khan, Alexey Tumanov, Caiwen Ding, Tong Geng
Publikováno v:
2022 IEEE 40th International Conference on Computer Design (ICCD).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e1ff17a705bb8211df037c9137d9422c
https://doi.org/10.1109/iccd56317.2022.00049
https://doi.org/10.1109/iccd56317.2022.00049
Publikováno v:
E3S Web of Conferences. 389:09016
The relevance of the study is that investment activities associated with investments in fixed assets relate to the "system-forming" activity of the state and are articulated in a number of federal documents and national projects. As it is noted in th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b9d8fb97f804b9039b0b966b7da8fd8c
https://doi.org/10.1051/e3sconf/202338909016
https://doi.org/10.1051/e3sconf/202338909016
Autor:
Charles Reiss, Alexey Tumanov
Publikováno v:
Proceedings of the ACM Symposium on Cloud Computing.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::63b0375645d61578de555846801f575e
https://doi.org/10.1145/3472883.3517123
https://doi.org/10.1145/3472883.3517123
Autor:
Lisa Dunlap, Kirthevasan Kandasamy, Joseph E. Gonzalez, Alexey Tumanov, Ujval Misra, Richard Liaw, Romil Bhardwaj, Ion Stoica
Publikováno v:
EuroSys
Hyperparameter tuning is essential to achieving state-of-the-art accuracy in machine learning (ML), but requires substantial compute resources to perform. Existing systems primarily focus on effectively allocating resources for a hyperparameter tunin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2ddfae44364568ed695dae88ed825b8b
https://doi.org/10.1145/3447786.3456245
https://doi.org/10.1145/3447786.3456245
Autor:
Gur-Eyal Sela, Alexey Tumanov, Xiangxi Mo, Ion Stoica, Joseph E. Gonzalez, Daniel Crankshaw, Corey Zumar
Publikováno v:
SoCC
Serving ML prediction pipelines spanning multiple models and hardware accelerators is a key challenge in production machine learning. Optimally configuring these pipelines to meet tight end-to-end latency goals is complicated by the interaction betwe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::21000665ba821fecea03b58039c9e9fc
https://doi.org/10.1145/3419111.3421285
https://doi.org/10.1145/3419111.3421285
Autor:
Alexey Tumanov, Jimeng Sun, Shenda Hong, Kevin O Maher, Alaa Aljiffry, Alind Khare, Satria Priambada, Yanbo Xu
Publikováno v:
KDD
Deep learning models have achieved expert-level performance in healthcare with an exclusive focus on training accurate models. However, in many clinical environments such as intensive care unit (ICU), real-time model serving is equally if not more im
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2023eb5ccc4b9970cccf80d34c2f980c
https://doi.org/10.1145/3394486.3403212
https://doi.org/10.1145/3394486.3403212
Autor:
Alexey Tumanov, Richard Liaw, Ion Stoica, Romil Bhardwaj, Joseph E. Gonzalez, Lisa Dunlap, Yitian Zou
Publikováno v:
SoCC
Prior research in resource scheduling for machine learning training workloads has largely focused on minimizing job completion times. Commonly, these model training workloads collectively search over a large number of parameter values that control th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::97fec485ceecde9f93c7758933a7822a
Publikováno v:
SoCC
Machine learning (ML) workflows are extremely complex. The typical workflow consists of distinct stages of user interaction, such as preprocessing, training, and tuning, that are repeatedly executed by users but have heterogeneous computational requi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d74f574bd6448c9f0545300bd74e1f78
https://doi.org/10.1145/3357223.3362711
https://doi.org/10.1145/3357223.3362711
Autor:
Philipp Moritz, Robert Nishihara, John Liagouris, Ujval Misra, Alexey Tumanov, Stephanie Wang, Ion Stoica
Publikováno v:
SOSP
As cluster computing frameworks such as Spark, Dryad, Flink, and Ray are being deployed in mission critical applications and on larger and larger clusters, their ability to tolerate failures is growing in importance. These frameworks employ two broad
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2c1a5822ad076d682070c95626df5e4a
https://doi.org/10.1145/3341301.3359653
https://doi.org/10.1145/3341301.3359653