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Samuel Oshin

dblp:208/1985 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · none

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

Systems, architecture and hardware · 1

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
Performance modeling and evaluation · 30% High-performance computing · 30% Electronic design automation · 30%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
High-performance computing › performance engineering
performance reproducibility
0.312017
Run-to-run variability on Xeon Phi based cray XC systems · SC 2017
Performance modeling and evaluation
performance variability
0.312017
Run-to-run variability on Xeon Phi based cray XC systems · SC 2017
Electronic design automation › yield analysis
process variation modeling
0.312017
Run-to-run variability on Xeon Phi based cray XC systems · SC 2017
Interconnection networks and networks-on-chip › network topology › low-diameter topology
dragonfly network
0.112017
Run-to-run variability on Xeon Phi based cray XC systems · SC 2017

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

performance characterization · 0.3
YearPublicationVenuePosition
2017 Run-to-run variability on Xeon Phi based cray XC systems
abstract
The increasing complexity of HPC systems has introduced new sources of variability, which can contribute to significant differences in run-to-run performance of applications. With components at various levels of the system contributing variability, application developers and system users are now faced with the difficult task of running and tuning their applications in an environment where run-to-run performance measurements can vary by as much as a factor of two to three. In this study, we classify, quantify, and present ways to mitigate the sources of run-to-run variability on Cray XC systems with Intel Xeon Phi processors and a dragonfly interconnect. We further demonstrate that the code-tuning performance observed in a variability-mitigating environment correlates with the performance observed in production running conditions.
Sudheer Chunduri, Kevin Harms, Scott Parker, Vitali A. Morozov, Samuel Oshin, Naveen Cherukuri, Kalyan Kumaran
SC5