| People | Locations | Statistics |
|---|---|---|
| Naji, M. |
| |
| Motta, Antonella |
| |
| Aletan, Dirar |
| |
| Mohamed, Tarek |
| |
| Ertürk, Emre |
| |
| Taccardi, Nicola |
| |
| Kononenko, Denys |
| |
| Petrov, R. H. | Madrid |
|
| Alshaaer, Mazen | Brussels |
|
| Bih, L. |
| |
| Casati, R. |
| |
| Muller, Hermance |
| |
| Kočí, Jan | Prague |
|
| Šuljagić, Marija |
| |
| Kalteremidou, Kalliopi-Artemi | Brussels |
|
| Azam, Siraj |
| |
| Ospanova, Alyiya |
| |
| Blanpain, Bart |
| |
| Ali, M. A. |
| |
| Popa, V. |
| |
| Rančić, M. |
| |
| Ollier, Nadège |
| |
| Azevedo, Nuno Monteiro |
| |
| Landes, Michael |
| |
| Rignanese, Gian-Marco |
|
Wang, Jingbo
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (7/7 displayed)
- 2021Macromolecular Chemistry and Physics / Catalyst type effects on structure/property relations of polypropylene random copolymerscitations
- 2020Systematic Investigation on the Structure-Property Relationship in Isotactic Polypropylene Films Processed via Cast Film Extrusioncitations
- 2020Systematic Investigation on the Structure-Property Relationship in Isotactic Polypropylene Films Processed via Cast Film Extrusioncitations
- 2020New insights into crystallization of heterophasic isotactic polypropylene by fast scanning chip calorimetry
- 2017Processing public pulsar astronomy data in the Amazon Cloud
- 2013Concurrent Order in a Semi-Crystalline Diblock Copolymer Involving Complexation with a Mesogencitations
- 2013Biaxial alignment of block copolymer-complex lamellaecitations
Places of action
| Organizations | Location | People |
|---|
report
Processing public pulsar astronomy data in the Amazon Cloud
Abstract
The primary goal of the Amazon Web Services (AWS) and Square Kilometre Array (SKA) Organisation ``AstroCompute in the Cloud Grants Program'' is to develop the skills and techniques needed to create, store, process, and manage extremely large (Terabyte-scale) data sets.As reported here we have analysed how pulsar data sets can be stored and processed within the Amazon Cloud framework.We have processed various types of pulsar data sets and considered use-cases from simple processing required by an individual to the analysis of a huge data volume as part of a project team.We find that AWS infrastructure is ideal for the processing of high volumes of pulsar astronomy data, but also highlight some challenges that will need to be faced in the coming SKA era. Such challenges include data transfer issues, reproducibility of science results, software licensing issues, costs and having versatile processing packages that can easily be upgraded by individual users.AWS infrastructure provided all the functionality that we required, such as highly-configurable compute and storage, together with ease of deployment. However we ran intensive machine-learning algorithms on Graphics Processing Units (GPUs) on CSIRO's High Performance Computing system purely because it was more cost effective.We also found software that using graphical user interfaces for manual control and processing showed reduced performance on Amazon servers compared with a computer on the local network.This was not a major problem apart from the perspective of users in China who found the network speeds prohibitive (note that China is a member of the SKA).Another issue with the current AWS system is that the policy prohibits the export or publication of virtual machine (VM) images amended with the AWS infrastructure.It is common for astronomers to start with existing software, then develop that software, process their data and then wish to publish their amended VM image.All the team members enjoyed using the AWS systems and would seriously consider using AWS for both small-scale and large-scale pulsar processing projects in the future.We thank the AWS/SKA team for supporting this project and we hope that this report is found to be useful for future planning.