Dirac 2.5 Operations

Lead Research Organisation: University of Edinburgh
Department Name: Sch of Physics and Astronomy

Abstract

Physicists across the astronomy, nuclear and particle physics communities are focussed
on understanding how the Universe works at a very fundamental level. The distance scales
with which they work vary by 50 orders of magnitude from the smallest distances probed
by experiments at the Large Hadron Collider, deep within the atomic
nucleus, to the largest scale galaxy clusters discovered out in space. The Science challenges,
however, are linked through questions such as: How did the Universe begin and how is it evolving?
and What are the fundamental constituents and fabric of the Universe and how do they interact?

Progress requires new astronomical observations and experimental data but also
new theoretical insights. Theoretical understanding comes increasingly from large-scale
computations that allow us to confront the consequences of our theories very accurately
with the data or allow us to interrogate the data in detail to extract information that has
impact on our theories. These computations test the fastest computers that we have and
push the boundaries of technology in this sector. They also provide an excellent
environment for training students in state-of-the-art techniques for code optimisation and
data mining and visualisation.

The DiRAC-2.5 project builds on the success of the DiRAC HPC facility and will provide the resources needed
to support cutting edge research during 2017 in all areas of science supported by STFC.

DiRAC-2.5 will provide maintain the existing DiRAC-2 services from April 2017, and also provide and increase in computational
resources at Durham, Cambridge and Leicester.

This grant will support the operation of the Edinburgh DiRAC services, which presently comprise
98384 operational computing cores serving around 80% of DiRAC computing cycles. The system is made up
from both the original 1.26PFlop/s DiRAC BlueGene/Q system and, following a recent transfer
to Edinburgh by STFC, six racks of the Hartree BlueJoule supercomputer.

The DiRAC project also will offer a team of three research software engineers who will help DiRAC researchers to ensure their scientific codes to extract
the best possible performance from the hardware components of the DiRAC clusters. These highly skilled programmers will
increase the effective computational power of the DiRAC facility during 2017.

Planned Impact

The expected impact of the DiRAC 2.5 HPC facility is fully described in the attached pathways to impact document and includes:

1) Disseminating best practice in High Performance Computing software engineering throughout the theoretical Particle Physics, Astronomy and Nuclear physics communities in the UK as well as to industry partners.

2) Working on co-design projects with industry partners to improve future generations of hardware and software.

3) Development of new techniques in the area of High Performance Data Analytics which will benefit industry partners and researchers in other fields such as biomedicine, biology, engineering, economics and social science, and the natural environment who can use this new technology to improve research outcomes in their areas.

4) Share best practice on the design and operation of distributed HPC facilities with UK National e-Infrastructure partners.

5) Training of the next generation of research scientists of physical scientists to tackle problems effectively on state-of-the-art of High Performance Computing facilities. Such skills are much in demand from high-tech industry.

6) Engagement with the general public to promote interest in science, and to explain how our ability to solve complex problems using the latest computer technology leads to new scientific capabilities/insights. Engagement of this kind also naturally encourages the uptake of STEM subjects in schools.
 
Description DiRAC 2.5 is a facility to support leading-edge computational astronomy and particle physics in the UK. This has resulted in over 1000 peer-reviewed publications. Many new discoveries about the formation and evolution of galaxies, star formation, planet formation and particle physics theory have been made possible by the award
Exploitation Route Build on the scientific knowledge and computational techniques developed. Many international collaborative projects are supported by the HPC resources provided by DiRAC.
Sectors Aerospace

Defence and Marine

Creative Economy

Digital/Communication/Information Technologies (including Software)

Education

Healthcare

URL http://www.dirac.ac.uk
 
Description A close working relationship on co-design of hardware and software.
First Year Of Impact 2015
Sector Digital/Communication/Information Technologies (including Software),Education
Impact Types Economic

 
Title Lattice dataset for the paper arXiv:2202.08795 "Simulating rare kaon decays using domain wall lattice QCD with physical light quark masses" 
Description Release for https://arxiv.org/abs/2202.08795 
Type Of Material Database/Collection of data 
Year Produced 2022 
Provided To Others? Yes  
URL https://zenodo.org/record/6369178
 
Title Symplectic lattice gauge theories on Grid: approaching the conformal window---data release 
Description This is the data release relative to the paper "Symplectic lattice gauge theories on Grid: approaching the conformal window" (arXiv:2306.11649). It contains pre-analysed data that can be plotted, and raw data that can be analysed and plotted through the analysis code in doi:10.5281/zenodo.8136514. 
Type Of Material Database/Collection of data 
Year Produced 2023 
Provided To Others? Yes  
URL https://zenodo.org/record/8136452
 
Title Symplectic lattice gauge theories on Grid: approaching the conformal window---data release 
Description This is the data release relative to the paper "Symplectic lattice gauge theories on Grid: approaching the conformal window" (arXiv:2306.11649). It contains pre-analysed data that can be plotted, and raw data that can be analysed and plotted through the analysis code in doi:10.5281/zenodo.8136514. 
Type Of Material Database/Collection of data 
Year Produced 2023 
Provided To Others? Yes  
URL https://zenodo.org/record/8136451
 
Description Intel IPAG QCD codesign project 
Organisation Intel Corporation
Department Intel Corporation (Jones Farm)
Country United States 
Sector Private 
PI Contribution We have collaborated with Intel corporation since 2014 with $720k of total direct funding, starting initially as an Intel parallel computing centre, and expanding to direct close collaboration with Intel Pathfinding and Architecture Group.
Collaborator Contribution We have performed detailed optimisation of QCD codes (Wilson, Domain Wall, Staggered) on Intel many core architectures. We have investigated the memory system and interconnect performance, particularly on Intel's latest interconnect hardware called Omnipath. We found serious performance issues and worked with Intel to plan a solution and this has been verified and is available as beta software. It will reach general availability in the Intel MPI 2019 release, and allow threaded concurrent communications in MPI for the first time. A joint paper on the resolution to this was written with the Intel MPI team, and the application of the same QCD programming techniques to machine learning gradient reduction was applied in the paper to the Baidu Research all reduce library, demonstrating a 10x gain for this critical step in machine learning in clustered environments. We are also working with Intel verifying future architectures that will deliver the exascale performance in 2021.
Impact We have performed detailed optimisation of QCD codes (Wilson, Domain Wall, Staggered) on Intel many core architectures. We have investigated the memory system and interconnect performance, particularly on Intel's latest interconnect hardware called Omnipath. We found serious performance issues and worked with Intel to plan a solution and this has been verified and is available as beta software. It will reach general availability in the Intel MPI 2019 release, and allow threaded concurrent communications in MPI for the first time. A joint paper on the resolution to this was written with the Intel MPI team, and the application of the same QCD programming techniques to machine learning gradient reduction was applied in the paper to the Baidu Research all reduce library, demonstrating a 10x gain for this critical step in machine learning in clustered environments. This collaboration has been renewed annually in 2018, 2019, 2020. Two DiRAC RSE's were hired by Intel to work on the Turing collaboration.
Start Year 2016
 
Title FP16-S7E8 MIXED PRECISION FOR DEEP LEARNING AND OTHER ALGORITHMS 
Description We demonstrated that a new non-IEEE 16 bit floating point format is the optimal choice for machine learning training and proposed instructions. 
IP Reference US20190042544 
Protection Patent application published
Year Protection Granted 2019
Licensed Yes
Impact We demonstrated that a new non-IEEE 16 bit floating point format is the optimal choice for machine learning training and proposed instructions. Intel filed this with US patent office. This IP is owned by Intel under the terms of the Intel Turing strategic partnership contract. As a co-inventor I have been named on the patent application. The proposed format has been announced as planned for use in future Intel architectures. This collaboration with Turing emerged out of an investment in Edinburgh by Intel Pathfinding and Architecture Group in codesign with lattice gauge theory simulations. Intel hired DiRAC RSE's Kashyap and Lepper and placed them in Edinburgh to work with me on Machine Learning codesign through the Turing programme.
 
Title Symplectic lattice gauge theories on Grid: approaching the conformal window-analysis code 
Description This is the analysis code that has been used to analyse and plot the data for the paper 'Symplectic lattice gauge theories on Grid: approaching the conformal window' (arXiv:2306.11649). 
Type Of Technology Software 
Year Produced 2023 
Open Source License? Yes  
URL https://zenodo.org/record/8136513