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LPGPU2

Low-Power Parallel Computing on GPUs 2

Total Cost €

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EC-Contrib. €

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Partnership

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Project "LPGPU2" data sheet

The following table provides information about the project.

Coordinator
TECHNISCHE UNIVERSITAT BERLIN 

Organization address
address: STRASSE DES 17 JUNI 135
city: BERLIN
postcode: 10623
website: www.tu-berlin.de

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Germany [DE]
 Project website http://www.lpgpu.org
 Total cost 3˙954˙846 €
 EC max contribution 2˙975˙786 € (75%)
 Programme 1. H2020-EU.2.1.1. (INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT))
 Code Call H2020-ICT-2015
 Funding Scheme IA
 Starting year 2016
 Duration (year-month-day) from 2016-01-01   to  2018-09-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    TECHNISCHE UNIVERSITAT BERLIN DE (BERLIN) coordinator 692˙565.00
2    SAMSUNG ELECTRONICS (UK) LIMITED UK (CHERTSEY - SURREY) participant 781˙134.00
3    CODEPLAY SOFTWARE LIMITED UK (LONDON) participant 674˙275.00
4    THINK SILICON EREYNA KAI TECHNOLOGIA ANONYMI ETAIRIA EL (PLATANI RIO AKHAIAS) participant 627˙812.00
5    SPIN DIGITAL VIDEO TECHNOLOGIES GMBH DE (BERLIN) participant 200˙000.00

Map

 Project objective

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. Future low-power system-on-chips will have to provide higher performance and be able to support more complex applications, without using any additional power.

The strict power limitations means that these demands cannot be met through hardware improvements alone, however, but the software must better exploit the available resources. Unfortunately, programmers are hindered when creating low-power GPU software by the quality of current performance analysis tools. In low-power GPU contexts there is only a minimal amount of performance information, and essentially no power information, available to the programmer. As software becomes more complex it becomes increasingly unmanageable for programmers to optimise the software for low-power devices.

This project proposes to aid the programmer in creating software for low-power GPUs by building on the results of the first LPGPU project to provide a complete performance analysis process for the programmer. This project will address all aspects of performance analysis, from hardware power and performance counters, to a toolchain that processes and visualises information from these counters, to applications that will be used as use-cases to drive the entire design. To access the new hardware performance counters a standardisable API will be produced to interface to a prototype hardware implementation. This will let the analysis and visualisation tool connect to any GPU driver that implements the API. The consortium's expertise will be used not only to drive the initial design of the API and analyses, but also multiple application use-cases will be developed to demonstrate the efficacy of the toolchain.

 Deliverables

List of deliverables.
First press release Websites, patent fillings, videos etc. 2020-02-25 17:07:48
Public Summary Websites, patent fillings, videos etc. 2020-02-25 17:07:49
Marketing materials (poster, flyers and document templates, Project web-site, creation of social media accounts, Project logo) Websites, patent fillings, videos etc. 2020-02-25 17:07:49
Report on power model for mobile SoCs based on hardware performance counters Documents, reports 2020-02-25 17:07:49
Profiling-driven DVFS report Documents, reports 2020-02-25 17:07:47
White paper on Tool Validation, Application Optimizations, and GPU Customization Documents, reports 2020-02-25 17:07:47
Final Report on Tool Validation, Application Optimizations, and GPU Customization Documents, reports 2020-02-25 17:07:48
Final Periodic Report Documents, reports 2020-02-25 17:07:48
Industry standard performance monitoring API Demonstrators, pilots, prototypes 2020-02-25 17:07:47
Delivering the LPGPU2 tool to the Open Community Documents, reports 2020-02-25 17:07:48
Public report on LPGPU2 applications Documents, reports 2020-02-25 17:07:47
Final press release Websites, patent fillings, videos etc. 2020-02-25 17:07:47

Take a look to the deliverables list in detail:  detailed list of LPGPU2 deliverables.

 Publications

year authors and title journal last update
List of publications.
2017 Georgios Keramidas, Graham Mudd, Andrew Richards, Ben Juurlink
VENDOR-AGNOSTIC TOOLS FOR ASSESSING GPU PERFORMANCE/POWER
published pages: p27, ISSN: , DOI:
HiPEAC Info 49 2020-02-25
2018 Biao Wang, Diego Felix de Souza, Mauricio Alvarez-Mesa, Chi Ching Chi, Ben Juurlink, Aleksandar Ilić, Nuno Roma, Leonel Sousa
Highly parallel HEVC decoding for heterogeneous systems with CPU and GPU
published pages: 93-105, ISSN: 0923-5965, DOI: 10.1016/j.image.2017.12.009
Signal Processing: Image Communication 62 2020-02-25
2018 Nadjib Mammeri, Ben Juurlink
VComputeBench: A Vulkan Benchmark Suite for GPGPU on Mobile and Embedded GPUs
published pages: , ISSN: , DOI: 10.14279/depositonce-7346
Proceedings 2018 IEEE International Symposium on Workload Characterization (IISWC) 2018 2020-02-25
2019 Sohan Lal, Jan Lucas, Ben Juurlink
SLC: Memory Access Granularity Aware Selective Lossy Compression for GPUs
published pages: , ISSN: , DOI:
2019 Design, Automation & Test in Europe Conference & Exhibition (DATE) 2019 2020-02-25

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The information about "LPGPU2" are provided by the European Opendata Portal: CORDIS opendata.

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