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REfactoring Parallel Heterogeneous Resource-Aware Applications  - a Software Engineering Approach

REfactoring Parallel Heterogeneous Resource-Aware Applications - a Software Engineering Approach

Objective

"The RePhrase project directly meets the challenge of ICT-09-2014, by studying the critically important issue of improving software development practice for parallel data-intensive applications. Data-intensive applications are among the most important and commonly encountered kinds of industrial application, and are increasingly important with
the emergence of ""big data"" problems. Emerging heterogeneous parallel architectures form ideal platforms to exploit the
massive-scale inherent parallelism that is usually implicit in such applications, but which is often difficult to extract in practice.
Solving this problem will bring major economic benefits to the software industry.
To address this challenge, RePhrase brings together a team of leading industrial and academic researchers, software engineers, systems developers, parallelism experts and domain experts from large companies, SMEs and leading universities. It aims to develop a novel software engineering methodology for developing complex, large-scale parallel data-intensive applications, supported by a very high-level programming model. We will exploit advanced pattern-based programming, refactoring, testing, debugging, verification and adaptive-scheduling technologies to build an interoperable tool-chain supporting our methodology, based on but significantly extending existing industrial and research tools. These tools will significantly ease, and even automate, all phases of typical software development, from design and implementation to long-term maintenance and software evolution. The generality of our approach will be ensured by targeting C++ and the most popular low-level parallel programming models, such as the C++11/14/17 standards, pthreads, OpenMP, Intel TBB, OpenCL and CUDA. We will demonstrate our approach on a range of large-scale data-intensive applications, taken from different domains, including bio-medical image processing, data analysis, machine learning, computer vision and railway diagnosis."

Coordinator

THE UNIVERSITY COURT OF THE UNIVERSITY OF ST ANDREWS

Address

North Street 66 College Gate
Ky16 9aj St Andrews

United Kingdom

Activity type

Higher or Secondary Education Establishments

EU Contribution

€ 580 919,50

Participants (8)

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IBM ISRAEL - SCIENCE AND TECHNOLOGY LTD

Israel

EU Contribution

€ 837 625

SOFTWARE COMPETENCE CENTER HAGENBERG GMBH

Austria

EU Contribution

€ 568 960

UNIVERSIDAD CARLOS III DE MADRID

Spain

EU Contribution

€ 355 416,25

CONSORCIO CENTRO DE INVESTIGACION BIOMEDICA EN RED M.P.

Spain

EU Contribution

€ 83 125

UNIVERSITA DI PISA

Italy

EU Contribution

€ 277 375

EVOPRO INNOVATION KFT

Hungary

EU Contribution

€ 194 690

PROGRAMMING RESEARCH LTD

United Kingdom

EU Contribution

€ 551 416,25

UNIVERSITA DEGLI STUDI DI TORINO

Italy

EU Contribution

€ 124 500

Project information

Grant agreement ID: 644235

  • Start date

    1 April 2015

  • End date

    31 March 2018

Funded under:

H2020-EU.2.1.1.3.

  • Overall budget:

    € 3 574 027

  • EU contribution

    € 3 574 027

Coordinated by:

THE UNIVERSITY COURT OF THE UNIVERSITY OF ST ANDREWS

United Kingdom