Kalman Filter Tracking on Parallel Architectures

Abstract

Power density constraints are limiting the performance improvements of modern CPUs. To address this we have seen the introduction of lower-power, multi-core processors such as GPGPU, ARM and Intel MIC. To stay within the power density limits but still obtain Moore's Law performance/price gains, it will be necessary to parallelize algorithms to exploit larger numbers of lightweight cores and specialized functions like large vector units. Track finding and fitting is one of the most computationally challenging problems for event reconstruction in particle physics. At the High-Luminosity Large Hadron Collider (HL-LHC), for example, this will be by far the dominant problem. The need for greater parallelism has driven investigations of very different track finding techniques such as Cellular Automata or Hough Transforms. The most common track finding techniques in use today, however, are those based on the Kalman Filter. Significant experience has been accumulated with these techniques on real tracking detector systems, both in the trigger and offline. They are known to provide high physics performance, are robust, and are in use today at the LHC. We report on porting these algorithms to new parallel architectures. Our previous investigations showed that, using optimized data structures, track fitting with a Kalman Filter can achieve large speedups both with Intel Xeon and Xeon Phi. Additionally, we have previously shown first attempts at track building with some speedup. We report here our progress towards an end-to-end track reconstruction algorithm fully exploiting vectorization and parallelization techniques in a simplified experimental environment.

Submitted to proceedings of Connecting The Dots 2016 (CTD2016), Vienna. arXiv admin note: text overlap with arXiv:1601.08245

References in corpus (4)

• Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC
• Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC
• First Evaluation of the CPU, GPGPU and MIC Architectures for Real Time Particle Tracking based on Hough Transform at the LHC
• Kalman Filter Tracking on Parallel Architectures

Cited by in corpus (6)

• Kalman Filter Tracking on Parallel Architectures
• Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Processors and GPUs
• Kalman filter tracking on parallel architectures
• Reconstruction of Charged Particle Tracks in Realistic Detector Geometry Using a Vectorized and Parallelized Kalman Filter Algorithm
• Parallelized and Vectorized Tracking Using Kalman Filters with CMS Detector Geometry and Events
• Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Architectures with the CMS Detector