Zobrazeno 1 - 10
of 47
pro vyhledávání: '"Michael A. Laurenzano"'
Publikováno v:
ACM Transactions on Architecture and Code Optimization. 16:1-25
We introduce Caliper , a technique for accurately estimating performance interference occurring in shared servers. Caliper overcomes the limitations of prior approaches by leveraging a micro-experiment-based technique. In contrast to state-of-the-art
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8f313b4bf1bcd833634fea763ca44a48
https://doi.org/10.1145/3323090
https://doi.org/10.1145/3323090
Autor:
Ronald G. Dreslinski, Michael A. Laurenzano, David Meisner, Lingjia Tang, Jason Mars, Yunqi Zhang, Thomas F. Wenisch, Chang-Hong Hsu
Publikováno v:
ACM Transactions on Computer Systems. 35:1-33
Reducing the long tail of the query latency distribution in modern warehouse scale computers is critical for improving performance and quality of service (QoS) of workloads such as Web Search and Memcached. Traditional turbo boost increases a process
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8806cb237798ef25174d656606fe1d81
https://doi.org/10.1145/3054742
https://doi.org/10.1145/3054742
Autor:
Jason Mars, Johann Hauswald, Parker Hill, Michael A. Laurenzano, Stefan Larson, Lingjia Tang, Jonathan K. Kummerfeld, Andrew Lee, Anish Mahendran
Publikováno v:
NAACL-HLT (1)
In a corpus of data, outliers are either errors: mistakes in the data that are counterproductive, or are unique: informative samples that improve model robustness. Identifying outliers can lead to better datasets by (1) removing noise in datasets and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::40c3f71aae02474cb2ef9b5ccd24156b
http://arxiv.org/abs/1904.03122
http://arxiv.org/abs/1904.03122
Autor:
Jason Mars, Jonathan K. Kummerfeld, Joseph Peper, Anish Mahendran, Andrew Lee, Lingjia Tang, Kevin Leach, Michael A. Laurenzano, Stefan Larson, Christopher Clarke, Parker Hill
Publikováno v:
EMNLP/IJCNLP (1)
Task-oriented dialog systems need to know when a query falls outside their range of supported intents, but current text classification corpora only define label sets that cover every example. We introduce a new dataset that includes queries that are
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::26f395a7eddd6eb0fc8df71e1cd98b3c
Publikováno v:
PLDI
This paper introduces Input Responsive Approximation (IRA), an approach that uses a canary input — a small program input carefully constructed to capture the intrinsic properties of the original input — to automatically control how program approx
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a5d02342fb91e918b9e4c0f253abed93
https://doi.org/10.1145/2980983.2908087
https://doi.org/10.1145/2980983.2908087
Autor:
Jason Mars, Johann Hauswald, Yunqi Zhang, Ronald G. Dreslinski, Cheng Li, Trevor Mudge, Arjun Khurana, Michael A. Laurenzano, Lingjia Tang, Austin Rovinski, Vinicius Petrucci
Publikováno v:
IEEE Micro. 36:42-53
Demand is expected to grow significantly for cloud services that deliver sophisticated artificial intelligence on the critical path of user queries, as is the case with intelligent personal assistants such as Apple's Siri. If the prediction of the tr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bec9443862d4580c98ec77f494d98b40
https://doi.org/10.1109/mm.2016.37
https://doi.org/10.1109/mm.2016.37
Autor:
Yunqi Zhang, Michael A. Laurenzano, Arjun Khurana, Johann Hauswald, Lingjia Tang, Ronald G. Dreslinski, Jason Mars, Austin Rovinski, Yiping Kang, Hailong Yang, Trevor Mudge, Cheng Li, Vinicius Petrucci
Publikováno v:
ACM Transactions on Computer Systems. 34:1-32
As user demand scales for intelligent personal assistants (IPAs) such as Apple’s Siri, Google’s Google Now, and Microsoft’s Cortana, we are approaching the computational limits of current datacenter (DC) architectures. It is an open question ho
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2d7892d0f86f0c73ac6e56f1f6175e56
https://doi.org/10.1145/2870631
https://doi.org/10.1145/2870631
Publikováno v:
PACT
A key focus of recent work in our community has been on devising increasingly sophisticated acceleration devices for deep neural network (DNN) computation, especially for networks driven by convolution layers. Yet, despite the promise of substantial
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a19c49052308123b89dacf555802318e
https://doi.org/10.1145/3243176.3243177
https://doi.org/10.1145/3243176.3243177
Publikováno v:
ISPASS
Cloud-scale datacenter management systems utilize virtualization to provide performance isolation while maximizing the utilization of the underlying hardware infrastructure. However, virtualization does not provide complete performance isolation as V
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d3b172525c6ba749878d7456771afe06
https://doi.org/10.1109/ispass.2018.00016
https://doi.org/10.1109/ispass.2018.00016
Autor:
Trevor Mudge, Lingjia Tang, Cheng Li, Johann Hauswald, Jason Mars, Michael A. Laurenzano, Arjun Khurana, Vinicius Petrucci, Yunqi Zhang, Austin Rovinski, Ronald G. Dreslinski
Publikováno v:
ASPLOS
As user demand scales for intelligent personal assistants (IPAs) such as Apple's Siri, Google's Google Now, and Microsoft's Cortana, we are approaching the computational limits of current datacenter architectures. It is an open question how future se
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::314c71b8ec7bdc2a478a817b352c8228
https://doi.org/10.1145/2786763.2694347
https://doi.org/10.1145/2786763.2694347