Speaker
Abstract content <br> (Max 300 words)<br><a href="http://events.saip.org.za/getFile.py/access?resId=0&materialId=0&confId=34" target="_blank">Formatting &<br>Special chars</a>
Modern Big Science projects such as the Large Hadron Collider at CERN generate enormous amounts of raw data which presents a serious computing challenge. After planned upgrades in 2022, the data output from the ATLAS Hadronic Tile Calorimeter (TileCal) will increase by 200 times to over 40 Tb/s. This increase requires more advanced processing on the raw data in order to harness a larger quantity of good quality physics data. An algorithm called optimal filtering is currently used in the TileCal front-end for online energy reconstruction of the digitised photo-multiplier tube signals and is currently implemented on Digital Signal Processors (DSPs) and Field Programmable Gate Arrays (FPGAs) which are difficult to program and expensive. It is proposed that a cost-effective, high data throughput and general purpose Processing Unit (PU) can be developed by using several commodity ARM processors while maintaining minimal software design difficulty for the end-user. This PU could be used for a variety of high-level algorithms other than optimal filtering on the high data throughput raw data to combat the issue of out of time pile-up and for online data quality testing. Optimal filtering and histogram algorithms have been implemented in C++ and several ARM platforms have been tested and shown to have good CPU to external I/O balance.
Level for award<br> (Hons, MSc, <br> PhD, N/A)?
msc
Apply to be<br> considered for a student <br> award (Yes / No)?
yes
Would you like to <br> submit a short paper <br> for the Conference <br> Proceedings (Yes / No)?
yes
Main supervisor (name and email)<br>and his / her institution
Bruce Mellado (bruce.mellado@wits.ac.za) Wits
Please indicate whether<br>this abstract may be<br>published online<br>(Yes / No)
yes
