Skip to main content

Low-Power Parallel Computing on GPUs 2

Deliverables

Report on power model for mobile SoCs based on hardware performance counters

This report will contain a description of the power model developed in T4.2, the microbenchmark suite and the power measurement testbed from T4.1. It will also provide insight into the capabilities of the various performance counters for power estimation. The report will also provide an evaluation of the power estimation tool. The power consumption of applications will be measured using the testbed and compared to the estimates provided by the tool

Public report on LPGPU2 applications

Public report on LPGPU2 applications. This report will describe the public part of the applications optimizations in T3.1, T3.4, T3.5, and T3.6.

Profiling-driven DVFS report

This report will summarize the profile driven DVFS algorithm

Final Report on Tool Validation, Application Optimizations, and GPU Customization

This deliverable will report the outcome of the tool validation phase. All the tasks that are active in the last semester of the project (T7.3, T7.4, and T7.5) will contribute to this deliverable

Delivering the LPGPU2 tool to the Open Community

This deliverable will be prepared as part of Task 7.7. It will contain instructions to download (and install) the LPGPU2 tool from a public repository. In addition, it will include a part in the form of application note about best-practices of the tool’s usage.

White paper on Tool Validation, Application Optimizations, and GPU Customization

White paper on Tool Validation, Application Optimizations, and GPU Customization. This deliverable will prepare the material from D7.2 as a white paper, to ensure wider dissemination.

Final Periodic Report

Final Report of the LPGPU2 project

Final press release

Final press release

First press release

Initial press release

Marketing materials (poster, flyers and document templates, Project web-site, creation of social media accounts, Project logo)

Marketing materials (poster, flyers and document templates, Project web-site, creation of social media accounts, Project logo)

Public Summary

A 4-page public summary of the project targeted at the general public

Industry standard performance monitoring API

This deliverable will contain a proposed definition of performance monitoring APIs as part of Khronos standards, including OpenGL ES and OpenCL

Searching for OpenAIRE data...

Publications

Optimal DC/AC data bus inversion coding

Author(s): Jan Lucas, Sohan Lal, Ben Juurlink
Published in: 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE), 2018, 2018, Page(s) 1063-1068, ISBN 978-3-9819263-0-9
Publisher: IEEE
DOI: 10.23919/DATE.2018.8342169

Enabling GPU software developers to optimize their applications — The LPGPU 2 approach

Author(s): Ben Juurlink, Jan Lucas, Nadjib Mammeri, Georgios Keramidas, Katerina Pontzolkova, Ignacio Aransay, Chrysa Kokkala, Martyn Bliss, Andrew Richards
Published in: 2017 Conference on Design and Architectures for Signal and Image Processing (DASIP), 2017, 2017, Page(s) 1-6, ISBN 978-1-5386-3534-6
Publisher: IEEE
DOI: 10.1109/DASIP.2017.8122116

The LPGPU2 Project - Low-Power Parallel Computing on GPUs: Extended Abstract

Author(s): Ben Juurlink, Jan Lucas, Nadjib Mammeri, Martyn Bliss, Georgios Keramidas, Chrysa Kokkala, Andrew Richards
Published in: Proceedings of the 20th International Workshop on Software and Compilers for Embedded Systems - SCOPES '17, 2017, 2017, Page(s) 76-80, ISBN 9781-450350396
Publisher: ACM Press
DOI: 10.1145/3078659.3078672

E^2MC: Entropy Encoding Based Memory Compression for GPUs

Author(s): Sohan Lal, Jan Lucas, Ben Juurlink
Published in: 2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS), 2017, 2017, Page(s) 1119-1128, ISBN 978-1-5386-3914-6
Publisher: IEEE
DOI: 10.1109/IPDPS.2017.101

VComputeBench: A Vulkan Benchmark Suite for GPGPU on Mobile and Embedded GPUs

Author(s): Nadjib Mammeri, Ben Juurlink
Published in: Proceedings 2018 IEEE International Symposium on Workload Characterization (IISWC), 2018, 2018
Publisher: IEEE
DOI: 10.14279/depositonce-7346

SLC: Memory Access Granularity Aware Selective Lossy Compression for GPUs

Author(s): Sohan Lal, Jan Lucas, Ben Juurlink
Published in: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), 2019, 2019
Publisher: IEEE

Highly parallel HEVC decoding for heterogeneous systems with CPU and GPU

Author(s): Biao Wang, Diego Felix de Souza, Mauricio Alvarez-Mesa, Chi Ching Chi, Ben Juurlink, Aleksandar Ilić, Nuno Roma, Leonel Sousa
Published in: Signal Processing: Image Communication, 62, 2018, Page(s) 93-105, ISSN 0923-5965
Publisher: Elsevier BV
DOI: 10.1016/j.image.2017.12.009

VENDOR-AGNOSTIC TOOLS FOR ASSESSING GPU PERFORMANCE/POWER

Author(s): Georgios Keramidas, Graham Mudd, Andrew Richards, Ben Juurlink
Published in: HiPEAC Info, 49, 2017, Page(s) p27
Publisher: HiPEAC