Effect of Workload Characteristics on Similarity Analysis

DOI

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%.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jiang Sha
AU  - Wenjuan Xu
PY  - 2018/02
DA  - 2018/02
TI  - Effect of Workload Characteristics on Similarity Analysis
BT  - Proceedings of the 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  - 10.2991/csece-18.2018.90
ID  - Sha2018/02
ER  -