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Chinyen Chou

dblp:15/7063 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2009
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 2

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Cloud and datacenter computing · 100%
Databases, data mining, and information retrieval
1 paper
Machine learning and data management · 100%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning and data management
machine learning for systems		 0.012009
Machine learning-based prefetch optimization for data center applications · SC 2009

Methods — techniques the papers use, named apart from their topics

machine learning-based prefetching · 0.2
YearPublicationVenuePosition
2009 Prefetch optimizations on large-scale applications via parameter value prediction
abstract
A typical data center application requires the processor cycles of thousands of machines. Even a single-digit performance improvement can significantly reduce the cost and power consumption of a data center. Unfortunately, achieving sustained improvement, even if modest, is difficult. Data centers are dynamic environments where applications are frequently released and servers are continually upgraded. For maintainability and fault tolerance, the physical capabilities and configuration of the servers are abstracted from the application programmer.
Shih-Wei Liao, Tzu-Han Hung, Donald Nguyen, Hucheng Zhou, Chinyen Chou, Chia-Heng Tu
 ICS5
2009 Machine learning-based prefetch optimization for data center applications
abstract
Performance tuning for data centers is essential and complicated. It is important since a data center comprises thousands of machines and thus a single-digit performance improvement can significantly reduce cost and power consumption. Unfortunately, it is extremely difficult as data centers are dynamic environments where applications are frequently released and servers are continually upgraded.
Shih-Wei Liao, Tzu-Han Hung, Donald Nguyen, Chinyen Chou, Chia-Heng Tu, Hucheng Zhou
 SC4