Proceedings of the 2018 International Conference on Computer Science, Electronics and Communication Engineering (CSECE 2018)

Effect of Workload Characteristics on Similarity Analysis

Authors
Jiang Sha, Wenjuan Xu
Corresponding Author
Jiang Sha
Available Online February 2018.
DOI
https://doi.org/10.2991/csece-18.2018.90How to use a DOI?
Keywords
microarchitecture-independent characteristics; similarity analysis; mobile applications; serializing instruction
Abstract
Workload characterization is the basis for similarity analysis, which is the core idea behind benchmark subsetting to pick up the most representative programs or program slices. The set of characteristics is crucial to the result of similarity analysis. Current studies typically use microarchitecture-independent characteristics (MICs) which reveal the inherent program behaviors to evaluate the similarities. In this paper, we propose a novel MICs: serializing instruction distance (SID). SID can describe the serializing instructions behavior that causes a significant performance loss of system-intensive mobile applications. The distribution of critical path length is also used as a MICs because it can reflect the inherent instruction level parallelism (ILP). Furthermore, we employ the comprehensive set of MICs to pick a representative set of program slices for each program of a mobile benchmark suites: Moby. The instructions per cycle (IPC) of each program slice is used to predict the whole program performance. The coefficient of variation of IPCs is under 6% and weighted average IPC prediction error is only 7%.
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Proceedings
2018 International Conference on Computer Science, Electronics and Communication Engineering (CSECE 2018)
Part of series
Advances in Computer Science Research
Publication Date
February 2018
ISBN
978-94-6252-487-3
ISSN
2352-538X
DOI
https://doi.org/10.2991/csece-18.2018.90How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Jiang Sha
AU  - Wenjuan Xu
PY  - 2018/02
DA  - 2018/02
TI  - Effect of Workload Characteristics on Similarity Analysis
BT  - 2018 International Conference on Computer Science, Electronics and Communication Engineering (CSECE 2018)
PB  - Atlantis Press
SP  - 423
EP  - 426
SN  - 2352-538X
UR  - https://doi.org/10.2991/csece-18.2018.90
DO  - https://doi.org/10.2991/csece-18.2018.90
ID  - Sha2018/02
ER  -