Algorithms and Software for Emerging Architectures (ASEArch)
Lead Research Organisation:
University of Oxford
Department Name: Oxford e-Research Centre
Abstract
Advanced computing is an essential tool in addressing scientific problems of national interest, including climate change, nanoscience, the virtual human, new materials, next-generation power sources and astrophysics; as importantly it is equally essential to solve commercial and industrial problems in financial modelling, engineering, and real-time decision systems. In recent years the computer systems that underpin these software applications have changed radically and it is no longer possible to simply run the same application software efficiently on new machines. This CCP will address this challenge by creating new knowledge regarding new hardware and software systems and disseminating to other computational science groups. That dissemination will occur through publications, workshops, in-depth study groups, new algorithms and software kernels.
The CCP is building on, and leveraging, several existing networks and institutes and is well positioned to leverage international efforts too. There is agreed engagement with the application-driven CCPs and a broad range of network collaborators.
The activity will have impact on academic computational scientist through direct engagement, availability of technology watch outputs; reports on best practice; papers on new algorithms; and open source software. Through the engagement of our industry partners and appropriate knowledge transfer network we will also have impact on commercial applications.
The CCP is building on, and leveraging, several existing networks and institutes and is well positioned to leverage international efforts too. There is agreed engagement with the application-driven CCPs and a broad range of network collaborators.
The activity will have impact on academic computational scientist through direct engagement, availability of technology watch outputs; reports on best practice; papers on new algorithms; and open source software. Through the engagement of our industry partners and appropriate knowledge transfer network we will also have impact on commercial applications.
Planned Impact
The immediate beneficiaries of this initiative will include scientists and developers in the other CCPs, in other computational science projects and in industrial R&D. The impact will be on both the academic and commercial worlds through knowledge transfer in both directions, adoption of software kernels and know-how in industry, through industry input to the technology watch, and dissemination of that into the academic community.
The outputs of ASEArch will be fourfold:
1. An internationally competitive activity resulting in algorithms and software that will lead to more effective use of high-performance chips and computers.
2. Publications and papers on new algorithms, software tools and best practice.
3. An increase in education and training resulting in a new generation of researchers and a more highly skilled workforce.
4. Open source software that implements new algorithms for advanced computing available to anyone for incorporation into applications, products or services.
The activities of this CCP will ensure that the existing investments in scientific software are not lost as new machines architectures become prevalent. The return on investment in future research software and on investments in new HPC hardware will be increased by this initiative.
The outputs of ASEArch will be fourfold:
1. An internationally competitive activity resulting in algorithms and software that will lead to more effective use of high-performance chips and computers.
2. Publications and papers on new algorithms, software tools and best practice.
3. An increase in education and training resulting in a new generation of researchers and a more highly skilled workforce.
4. Open source software that implements new algorithms for advanced computing available to anyone for incorporation into applications, products or services.
The activities of this CCP will ensure that the existing investments in scientific software are not lost as new machines architectures become prevalent. The return on investment in future research software and on investments in new HPC hardware will be increased by this initiative.
Organisations
Publications
Adámek K
(2020)
GPU Fast Convolution via the Overlap-and-Save Method in Shared Memory
in ACM Transactions on Architecture and Code Optimization
Dunning P
(2016)
Level-set topology optimization with many linear buckling constraints using an efficient and robust eigensolver
in International Journal for Numerical Methods in Engineering
Giles M
(2016)
Algorithm 955 Approximation of the Inverse Poisson Cumulative Distribution Function
in ACM Transactions on Mathematical Software
Giles M
(2014)
GPU Implementation of Finite Difference Solvers
Giles MB
(2014)
Trends in high-performance computing for engineering calculations.
in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Hernandez-Fernandez M
(2019)
Using GPUs to accelerate computational diffusion MRI: From microstructure estimation to tractography and connectomes.
in NeuroImage
Hogg J
(2016)
A Sparse Symmetric Indefinite Direct Solver for GPU Architectures
in ACM Transactions on Mathematical Software
Hogg J
(2013)
A Fast Dense Triangular Solve in CUDA
in SIAM Journal on Scientific Computing
Kazantsev D
(2017)
New iterative reconstruction methods for fan-beam tomography
in Inverse Problems in Science and Engineering
Description | This CCP (Collaborative Computational Project) supported other CCPs in the exploitation of novel architectures. This was accomplished through workshops, training and the development of open source software with widespread applicability. |
Exploitation Route | The software was equally applicable in both academic and non-academic contexts. Open source software was developed for the use of other CPPs, and some was also contributed to NVIDIA's CUBLAS and CUSPARSE libraries. |
Sectors | Digital/Communication/Information Technologies (including Software) |
URL | http://people.maths.ox.ac.uk/gilesm/codes/BS_1D/,http://people.maths.ox.ac.uk/gilesm/codes/BS_3D/ |
Description | During the project the other CCPs (Computational Collaborative Projects) accessed our information and findings from our website. Later, we contributed software to NVIDIA's CUBLAS and CUSPARSE mathematical libraries, as well as making software openly available under an open source license at http://people.maths.ox.ac.uk/gilesm/codes/BS_1D/, http://people.maths.ox.ac.uk/gilesm/codes/BS_3D/. This research on GPU computing was also fundamental in building our understanding of the power on GPU computing, which in turn led to later activities such as our leadership in creating JADE, the UK's first supercomputer for AI / machine learning: https://www.jade.ac.uk/ |
First Year Of Impact | 2013 |
Sector | Aerospace, Defence and Marine,Chemicals,Digital/Communication/Information Technologies (including Software),Education,Energy,Financial Services, and Management Consultancy,Manufacturing, including Industrial Biotechology,Pharmaceuticals and Medical Biotechnology |
Impact Types | Economic |
Description | Future-proof massively-parallel execution of multi-block applications |
Amount | £282,844 (GBP) |
Funding ID | EP/K038494/1 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 08/2013 |
End | 08/2016 |
Description | JADE: Joint Academic Data science Endeavour |
Amount | £3,000,000 (GBP) |
Funding ID | EP/P020275/1 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 09/2016 |
End | 03/2020 |
Description | e-Infrastructure South Consortium - Centre for Innovation |
Amount | £2,820,000 (GBP) |
Funding ID | EP/K000136/1 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 01/2011 |
End | 03/2012 |
Description | CUDA Programming on NVIDIA GPUs |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Postgraduate students |
Results and Impact | One week course on CUDA programming on NVIDIA GPUs, available to both academics and non-academics. Lots of the students have since gone on to use CUDA programming in their research. |
Year(s) Of Engagement Activity | 2008,2009,2010,2011,2012,2013,2014 |
URL | http://people.maths.ox.ac.uk/gilesm/cuda/ |