Accounting Module
To investigate XDMOD to assess if it is suitable as a DiRAC wide Accounting tool.
A demonstrator application was set up at DiRAC Cambridge, and basic functions then tested. A Summary of conclusions and next steps is in the report below.
MPI Library
Produce a digital repository for the sharing and archiving of benchmarking data for key DiRAC codes.
A wiki was created within the DiRAC instance of the confluence package, currently hosted at the University of Edinburgh. This was created as a long-term repository for benchmarking data from the DiRAC systems. The wiki is intended to be updated as and when new benchmark results are available, for example during procurement activities or when new versions of applications are introduced. The repository has space for detailed information and comments from the benchmark runners to highlight special features of the run. It also has space for MPI profiling information from the run.
Initial data from existing benchmark runs has been loaded into the wiki.
| Time | Speaker | Title |
|---|---|---|
| 09:00 | Simon Hands (Swansea) | Welcome (pdf) |
| 09:10 | Mark Wilkinson (Dirac) | Director’s Report (pdf) |
| 09:30 | Debora Sijacki (Cambridge) | Simulating the most energetic events in the Universe (pdf) |
| 10:00 | Jacek Dobaczewski (York) | Computing Atomic Nuclei (pdf) |
| 10:20 | Coffee | |
| 11:00 | David Wilson (TC Dublin) | Hadron resonances from lattice QCD (pdf) |
| 11:30 | Mark Hannam (Cardiff) | How DiRAC helps us measure black holes (pdf) |
| 12:00 | Vera Guelpers (Southampton) | Lattice QCD calculations for high-precision tests of the Standard Model of particle physics (pdf) |
| 12:30 | David Britton (Glasgow) / Peter Clarke (Edinburgh) | GridPP (pdf) / IRIS (pdf) |
| 13:00 | Lunch, (Room 218, Wallace Bulding) | |
| 14:30 | Parallel Sessions | |
| 17:00 | Poster Presentations, (Room 218, Wallace Bulding) | |
| 18:00 | Close |
| Time | Session | Location |
| 9:00 – 9:30 | Welcome and Director’s report | Oak Room |
| 9:30 – 10:30 | Morning session | Oak Room |
| 10:30 – 11:00 | Coffee | |
| 11:00 – 13:00 | Morning session (cont.) | Oak Room |
| 13:00 – 14:30 | Lunch | |
| 14:30 – 17:15 | Parallel Session 1 | Left hand side of Oak Room |
| Parallel Session 2 | Right hand side of Oak Room | |
| 17:15 – 18:00 | Poster session, prizes and reception | Chestnut and Birch Rooms |
| 18:00 | Close |
| Time | Session |
| 10:00 | Practicalities, Welcome and Introduction Adrian Jenkins/Alastair Basden |
| 10:10 | Introduction to The DIRAC3 Technical Case Jeremy Yates |
| 10:25 | The new Data Intensive Service Paul Calleja |
| 10:35 | The new Extreme Scaling Service Antonin Portelli |
| 10:45 | The new Memory Intensive Service Alastair Basden |
| 10:55 | Questions |
| 11:00 | Break |
| 11:15 | The new Research Software Engineer Service Andy Turner |
| 11:25 | The new Data Curation Service Alastair Basden |
| 11:35 | The ExCALIBUR Hardware and Enabling Software Programme Martin Hamilton |
| 11:45 | Multi-factor authentication on DiRAC resources Jon Wakelin |
| 12:00 | Break |
| 12:15 | Meet the technical team All teams |
| 12:25 | Student Cluster Competition Mark Wilkinson |
| 12:35 | Innovation Placement outputs Mark Wilkinson |
| 12:45 | Suggestions for follow up meetings and projects Alastair Basden |
| 13:00 | Lunch |
| 14:00 | Director’s talk |
| 14:20 | A steeply-inclined trajectory for the Chicxulub impact. Thomas Davison |
| 14:45 | B Meson Oscillations Christine Davies |
| 15:00 | The external photo-evaporation of planet-forming discs Tom Haworth |
| 15:15 | Break |
| 15:30 | DiRAC enables prediction for matter-anti-matter asymmetry in the Standard Model. Christopher Sachrajda |
| 15:55 | Identifying and Quantifying the Role of Magnetic Reconnection in Space Plasma Turbulence. Jeffersson Andres Agudelo Rueda |
| 16:10 | Extreme QCD: Quantifying the QCD Phase Diagram Aleksandr Nikolaev |
| 16:25 | Hackathon Presentations |
| 16:50 | Poster Competition results |
| 16:55 | Closing Talk |
| 17:00 | End |
Welcome: Jeremy Yates, Chair of DiRAC Technical Directorate
Overview of the new HPC Systems and hardware across all services: Mark Wilkinson, Director
Overview of User Guide: Anushka Sharma, Senior Technical Programme Coordinator
Overview of DiRAC Training: Richard Regan, Training Manager
TauREx on DiRAC-3: Dr. Ahmed Faris Al-Refaie, University College London
A Lattice Field Theory Ecosystem for DiRAC-3: L Del Debbio, University of Edinburgh
Large cosmological runs on the new DiRAC MI “Cosma-8” facility: Matthieu Schaller, Lorentz Institute, on behalf of the Virgo Consortium
The Technical Working Group experience of deploying DiRAC3: Alastair Basden, Technical Manager
The Research Software Engineer Experience: James Richings, Edinburgh and Athena Elafrou, Cambridge
Closing remarks: Simon Hands, DiRAC Community Development Director
Welcome: Jeremy Yates, Chair of DiRAC Technical Directorate
Research Image Competition 2022
DiRAC is excited to launch our inaugural research image competition and encourage our past and present users to submit aesthetically inspiring and scientifically interesting imagery which has been generated using the DiRAC facility over the past three years.
The competition is a wonderful opportunity to have your research imagery displayed across the DiRAC platform, on our website, on social media, and in print media (see our 2022 calendar, right) and will help promote interest in your area of scientific study.
There are two categories for submission and the winners of each category will be selected by a panel of experts.
The top image in each category being awarded a £250 Amazon e-voucher, kindly donated by Q Associates, a Logicalis company.
Details on how to submit can be found below. Submission deadline is 5pm on 14th October.
Prize winners will be notified on 1st November and results annouced on our website and social media channels.
DiRAC 2022 Calendar comprising previously submitted images generated on DiRAC.
Theme 1: Particle and Nuclear Physics
Theme 2: Astronomy, Cosmology and Solar & Planetary Science
Any imagery submitted to this competition could be used in future marketing/publicity materials relating to DiRAC in either digital or print and as such, should have visual impact and scientific interest. We will be producing a 2023 DiRAC image calendar, the imagery for which will be drawn from the submissions to this competition and a selection of the top images will be displayed in print at our annual DiRAC Science Day event.
Queries can be directed to Simon Hands, DiRAC Community Director
Automated Benchmarks (Reframe)
To utilise Reframe as a single wrapper for the suite of existing DiRAC and UCL Tier2 benchmarks – with the aim of providing a single set of benchmarks that can be run as needed following system upgrades.
The following were successfully added to Reframe:
Work on benchmarks that has been progressed, but as yet not completed due to the technical challenges:
AI/ML Benchmark
To create a self-contained AI Benchmark/workflow in the domain of synthetic brain imaging.
Training epochs were run for three provided model configurations on the UCL AI platform on both a single GPU and multiple GPU devices. Several multiple day runs of ~100 epochs were run. As training scripts were configured to run for 100,000 epochs and each epoch takes around an hour or more to run, ‘full’ runs of the model were not performed.
Python requirements and the package associated with the code were installed on the Cambridge HPC service (following the same set up process documented in the repository README below).
A Public GitHub repository containing the open-source (GPL v3) release of the research code developed by Kings College London. The GitHub repository (https://github.com/r-gray/3d_very_deep_vae) has a GPL v3 license file included.
The README file in the repository contains full details of how to install the dependencies and Python package, and includes platform dependent requirement specifications with pinned versions for the support operating system and Python version combinations. There is also documentation on how to run the model training with the example configurations provided.
Development of an Artificial Intelligence-Machine Learning Benchmark – Final Report
Advanced Application & Systems Performance Analysis Tools
To produce an application that monitors workload usage of hardware components.
The assets from the Cloud Road-testing for UKRI Workloads work package were extended to ensure every platform has monitoring to get visibility into how well a workload is making use of the hardware assigned to that specific platform.
The Jupyter Notebook and Linux machine platforms both ran an isolated Prometheus Node Exporter and Grafana stack. A similar stack was run on Slurm, alongside Slurm specific information, such as the current jobs being run.
OpenStack Cloud Dashboard – Azimuth was modified to link to a Grafana that can provide insights into the users current usage and resource allocations. (Although, there are still some missing links in making a full end-to-end prototype.)
DiRAC Wide Dashboard – there is no working prototype for a DiRAC wide dashboard, but architecturally it was shown how this could be adopted for each site, and then aggregated centrally, using the same technologies.
Authorisation Module
To re-engineer an existing Authorisation application to be suitable for use within the DiRAC ecosphere.
A Data Access Controller was developed with the intention of each HPC service hosting an instance of this service. It will be responsible for querying instances of the IG App to enable local users to prove that they have permission to access a locally stored dataset by virtue of their participation in the project owning it.
An end-to-end workflow was successfully demonstrated, whereby users were able to register datasets from the Information Governance app and create new shared directories on the local filesystem, and that permissions on those directories were automatically updated in response to changes in the IG App.
One API
To explore and document the experiences of porting some representative codes to a OneAPI programming module.
The project documented the experiences of porting some representative codes to one or other of two promising programming models: SYCL or OpenMP offload. The programming models are supported by Intel OneAPI and other commercial and open source compilers.
Five candidate codes (OpenQCD, OpenMM, HemeLB, dGpoly3D, and AREPO) were selected, profiled and kernels were ported. (An absolute performance comparison between programming models was not a goal of this work.)
The experience of a group of research software engineers, most of whom were novices in SYCL or OpenMP GPU offload programming was examined.
The final report from this piece of work is expected in Autumn 2022.
Closer
To understand the multiple dimensions of prediction of concepts in social and biomedical science questionnaires.
This work package extended the scope of the research tackled in the RCNIC project to:
Applying machine learning models to social or biomedical science questionnaires – Final Report