Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-7571
Main Title: Enabling GPU software developers to optimize their applications – The LPGPU2approach
Author(s): Juurlink, Ben
Lucas, Jan
Mammeri, Nadjib
Keramidas, Georgios
Pontzolkova, Katerina
Aransay, Ignacio
Kokkala, Chrysa
Bliss, Martyn
Richards, Andrew
Type: Conference Object
Language Code: en
Abstract: Low-power GPUs have become ubiquitous, they can be found in domains ranging from wearable and mobile computing to automotive systems. With this ubiquity has come a wider range of applications exploiting low-power GPUs, placing ever increasing demands on the expected performance and power efficiency of the devices. The LPGPU 2 project is an EU-funded, Innovation Action, 30-month-project targeting to develop an analysis and visualization framework that enables GPU application developers to improve the performance and power consumption of their applications. To this end, the project follows a holistic approach. First, several applications (use cases) are being developed for or ported to low-power GPUs. These applications will be optimized using the tooling framework in the last phase of the project. In addition, power measurement devices and power models are devised that are 10× more accurate than the state of the art. The ultimate goal of the project is to promote open vendor-neutral standards via the Khronos group. This paper briefly reports on the achievements made in the first phase of the project (till month 18) and focuses on the progress made in applications; in power measurement, estimation, and modelling; and in the analysis and visualization tool suite.
URI: https://depositonce.tu-berlin.de//handle/11303/8425
http://dx.doi.org/10.14279/depositonce-7571
Issue Date: 2017
Date Available: 7-Nov-2018
DDC Class: 004 Datenverarbeitung; Informatik
Subject(s): GPU
low power
embedded computing
power modelling
performance counters
microbenchmarks
visualization
API interposer
data collection
Sponsor/Funder: EC/H2020/688759/EU/Low-Power Parallel Computing on GPUs 2/LPGPU2
License: http://rightsstatements.org/vocab/InC/1.0/
Proceedings Title: 2017 Conference on Design and Architectures for Signal and Image Processing (DASIP)
Publisher: IEEE
Publisher Place: Piscataway, New Jersey
Publisher DOI: 10.1109/DASIP.2017.8122116
ISBN: 978-1-5386-3534-6
978-1-5386-3533-9
978-1-5386-3535-3
Appears in Collections:FG Architektur eingebetteter Systeme » Publications

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