For the first time, data from LHCb, a major physics experiment, will be processed on a farm of GPUs. This solution is not only much cheaper, but it will help decrease the cluster size and process data at speeds up to 40 Tbit/s. The research paper has been published in Computing and Software for Big Science. https:/
An interdisciplinary task force of researchers from one of the biggest international collaborations in high energy physics LHC beauty at CERN has suggested a novel way to process enormous dataflow from the particle detector. The team consists of researchers from leading European and US universities. The Russian part of the team was represented by HSE and Yandex School of data analysis. The main goal of the proposal is to provide the collaboration with a robust, efficient and flexible solution that could deal with increased data flow expected during the upcoming data taking period. This solution is not only much cheaper, but it will help decrease the cluster size and process data at speeds up to 40 Tbit/s.
The LHC and LHCb in particular were created for the purpose of searching for 'new physics', something beyond the Standard Model. While the research has achieved moderate success, hopes of finding completely new particles, such as WIMPs, have failed. Many physicists believe that in order to achieve new results, statistics on particle collision at the LHC should be increased considerably. But this not only requires new accelerating equipment - upgrades are currently underway and due to be completed by 2021-2022 - but also brand-new systems to process particle collision data. To detect the events on LHCb as correctly registered, the reconstructed track must match the one modelled by the algorithm. If there is no match, the data are excluded. About 70% of all collisions in the LHC are excluded this way, which means that serious calculation capacities are required for this preliminary analysis.
A group of researchers, including Andrey Ustyuzhanin https:/
Unlike previous triggers, the new system transfers data from CPUs to GPUs. These may include both professional solutions (such as Tesla GPUs, the most advanced on the market) and ordinary 'gamer' GPUs by NVIDIA or AMD. Thanks to this, the Allen trigger does not depend on one specific equipment vendor, which makes it easier to create and reduces costs. With the highest-performance systems, the trigger can process data at up to 40 Tbit/s.
In a standard scheme, information on all events goes from the detector to a zero-level (L0) trigger, which consists of programmable chips (FPGA). They perform selection at the basic level. In the new scheme, there will be no L0 trigger. The data immediately go to the 'farm', where each of the 300 GPUs simultaneously processes millions of events per second.
After initial event registration and detection, only the selected data with valuable physical information go to ordinary x86 processors of second-level triggers (HLT2). This means that the main computational load related to event classification happens at the 'farm' by means of GPUs exceptionally.
This framework will help solve the event analysis and selection tasks more effectively: GPUs are initially created as a multi-channel system with multiple cores. And while CPUs are geared towards consecutive information processing, GPUs are used for massive simultaneous calculations. In addition, they have a more specific and limited set of tasks, which adds to performance.
According to Denis Derkach https:/
The long-term benefit of the new approach is particularly important. Equipment for many physics experiments is currently being upgraded all around the world. And virtually every such upgrade leads to a growing flow of processed information. Previously, experiments did not use systems based on GPUs exceptionally. But the advantages of Allen - a simpler architecture and lower cost - are so obvious that this approach will undoubtedly take the lead beyond the LHCb experiment.