Distributed Heterogeneous Vertically IntegrateD ENergy Efficient Data centres
Lead Research Organisation:
Lancaster University
Department Name: Computing & Communications
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
Publications
C. Cummins
(2017)
Synthesizing benchmarks for predictive modeling
Chang L
(2018)
SleepGuard Capturing Rich Sleep Information Using Smartwatch Sensing Data
in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Chen D
(2019)
Optimizing Sparse Matrix-Vector Multiplications on an ARMv8-based Many-Core Architecture
in International Journal of Parallel Programming
Chen X
(2019)
Sensing Our World Using Wireless Signals
in IEEE Internet Computing
Cummins C
(2017)
End-to-End Deep Learning of Optimization Heuristics
Cummins C
(2017)
Synthesizing benchmarks for predictive modeling
G.X Ye
(2017)
Cracking Android Pattern Lock in Five Attempts
| Description | We have shown that by optimizing and scheduling the code in different ways different performance and energy trade-offs can be achieved on heterogeneous multi-core architectures. This demonstrates that compiler-based techniques can play a key role in performing energy and performance optimizations for heterogeneous multi- and many-core systems. We also perform the first comprehensive study the effectiveness of different power capping techniques. This provides the insights to design better power and performance optimization techniques in the future. We are among the first to show that deep learning can be used to replace compiler heuristics, leading to far better performance on parallel GPGPU programs. |
| Exploitation Route | We have released our prototyping compile tool as open source. It can be downloaded from https://github.com/zwang4/dividend. We have also published our results in over 10 papers from which the research community can benefit from our key finding. |
| Sectors | Digital/Communication/Information Technologies (including Software) |
| Description | Our work on compiler-based code size reduction has been licensed to a processor IP company and is being productised by a major IT company. |
| First Year Of Impact | 2019 |
| Sector | Digital/Communication/Information Technologies (including Software) |
| Impact Types | Economic |
| Description | EPSRC iCASE Studentship |
| Amount | £35,000 (GBP) |
| Organisation | Arm Limited |
| Sector | Private |
| Country | United Kingdom |
| Start | 01/2016 |
| End | 06/2019 |
| Description | Royal Society |
| Amount | £12,000 (GBP) |
| Organisation | The Royal Society |
| Sector | Charity/Non Profit |
| Country | United Kingdom |
| Start | 03/2017 |
| End | 03/2019 |
| Title | DeepTune - a deep learning based compiler optimisaiton tool |
| Description | DeepTune is an open-source framework for building compiler optimisation heuristics using deep learning techniques. DeepTune uses a deep neural network that learns heuristics over raw code, entirely without using code features. The neural network simultaneously constructs appropriate representations of the code and learns how best to optimize, removing the need for manual feature creation. |
| Type Of Material | Improvements to research infrastructure |
| Year Produced | 2017 |
| Provided To Others? | Yes |
| Impact | DeepTune is the world's first deep-learning-based autotuner for compiler heuristics. It opens up a new research field for using deep learning to model program structures for performance optimisation. A range of follow up works have built upon DeepTune. It also helps to secure follow-up industrial funding for over £500K. |
| URL | https://github.com/ChrisCummins/paper-end2end-dl |
| Title | HSA auto-tuning framework |
| Description | A compiler-based auto-tuning tool for HSA applications. It is the first automatic tool for tuning HAS applications. |
| Type Of Material | Improvements to research infrastructure |
| Year Produced | 2016 |
| Provided To Others? | Yes |
| Impact | There are two research groups (the project partners), Albert Cohen at Inria France, and Alexandru Amaricai from Politehnica University of Timi?oara, Romaina are using our tool |
| URL | https://github.com/zwang4/dividend |
| Description | Collaboration with Dionasys |
| Organisation | Peking University |
| Department | School of Electronics Engineering and Computer Science |
| Country | China |
| Sector | Academic/University |
| PI Contribution | We are collaborating on a collaboration project funded by the Royal Society. The project mines opensource repositories like github to automatically detect bugs and generate fixings. The Lancaster team contributes to the project on compiler and code analysis expertise. |
| Collaborator Contribution | The Peking university team contributes staff time and expertise on natural language processing to the project. |
| Impact | The project just started and no outcome were generated yet. |
| Start Year | 2017 |
| Description | Collaboration with Peking University |
| Organisation | Peking University |
| Department | School of Electronics Engineering and Computer Science |
| Country | China |
| Sector | Academic/University |
| PI Contribution | We are working on a joint project to mine the open sourced projects from github to detect and repair bugs. We contribute our expertise on code analysis to the project. |
| Collaborator Contribution | The collaborative partner contributes their expertise on natural language processing to the project. The partner team involves two academics and three postgraduate students. |
| Impact | This collaborative work has led to two joint publications: (DOI: 0.18653/v1/P17-1040 and Scale Up Event Extraction Learning via Automatic Training Data Generation). |
| Start Year | 2017 |
| Description | HSA collaboration with AMD |
| Organisation | Advanced Micro Devices (AMD) |
| Country | United States |
| Sector | Private |
| PI Contribution | This work has led to a collaboration with AMD who is a main contributor of the Heterogeneous System Architecture (HSA) Foundation. We are currently working on building a compiler-based HSA auto-tuner for the LLVM HSAIL compiler developed by AMD. |
| Collaborator Contribution | AMD has gave us access to their internal version of the HSA driver and provide technical support to their HSA architecture. |
| Impact | This has led to a prototype HSA auto-tuner released on github: https://github.com/zwang4/dividend |
| Start Year | 2016 |
| Title | HSA Auto-tuning tool |
| Description | A compiler-based auto-tuning tool for HSA applications. |
| Type Of Technology | Software |
| Year Produced | 2016 |
| Open Source License? | Yes |
| Impact | The first auto-tuning tool for HSA programs. |
| URL | https://github.com/zwang4/dividend |
| Description | Computer Science Podcast |
| Form Of Engagement Activity | A broadcast e.g. TV/radio/film/podcast (other than news/press) |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | We have continued running CompuCast (compucast.io), a Computer Science podcast this year. We have produced an episode on the relevant area of the grant. |
| Year(s) Of Engagement Activity | 2016 |
| URL | http://compucast.io |
| Description | NDSS paper |
| Form Of Engagement Activity | A magazine, newsletter or online publication |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Public/other audiences |
| Results and Impact | Our research into Android Pattern Lock security has received wide media coverage. The news appeared in most UK national newspapers and was reported on by media outlets around the world to a potential audience of millions (as reported by the press office at Lancaster University) |
| Year(s) Of Engagement Activity | 2016 |
| URL | http://www.thetimes.co.uk/edition/news/scientists-finger-security-flaw-on-smartphone-lock-dmql3hdp3 |