Cite the paper whenever you use this data in any publication or presentation.
The BiBTex is provided below for convenience:
@inproceedings{halpern2016mobile,
title={Mobile CPU’s Rise to Power: Quantifying the Impact of
Generational Mobile CPU Design Trends on Performance,
Energy, and User Satisfaction},
author={Halpern, Matthew and
Zhu, Yuhao and
Janapa Reddi, Vijay},
booktitle={High Performance Computer Architecture (HPCA),
2016 IEEE 22nd International Symposium on},
year={2016}
}
The data in this spreadsheet is a companion to the paper:
M. Halpern, Y. Zhu, V. J. Reddi, "Mobile CPU's Rise to Power: Quantifying the Impact of Generational Mobile CPU Design Trends on Performance, Energy, and User Satisfaction", in the 22nd Symposium on High Performance Computer Architecture (HPCA), March, 2016.
The data is presented as a CSV with the following columns:
| Column | Description |
|---|---|
| id | MTurk worker unique identifier (anonymized) |
| url | URL to YouTube video worker rated |
| rating | Satisfaction rating 1 - 5 (higher is better) |
| accept_time | Time worker completed survey |
| ip | Worker IP address (anonymized) |
| benchmark | The application rated |
| cpu_cores | CPU cores enabled (1, 2, 3, 4) |
| cpu_freq | CPU frequency in MHz (422.4, 729.6, 1036.8, 1497.6, 1958.4, 2457.6) |
| gpu_freq | GPU frequency in MHz (200, 320, 389, 462.4, 578) |
Additional methodology can be found in Section 3.1 within the paper.
Equipment Specifications
| Component | Value |
|---|---|
| Smartphone | Samsung Galaxy S5 GT-I9505 |
| System-on-Chip | Qualcomm Snapdragon 8930AB |
| CPU | Quad-core 2.5 GHz Krait 400 |
| GPU | 578 MHz Adreno 330 |
Probabilistic Modeling for Crowdsourcing Partially-Subjective Ratings
An T. Nguyen, Matthew Halpern, Byron C. Wallace and Matthew Lease
AAAI HCOMP 2016
Source code for this work can be found at: https://github.com/thanhan/subjective-crowd-hcomp16.git