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README.md
mobile-cpu-user-sat-data.csv

README.md

Mobile CPU User Satisfaction Data

Conditions of Use

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}
}

Introduction

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)

Methodology

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

Research Using this Dataset

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