Traditional Tracking with Kalman Filter on Parallel Architectures

TL;DR

This paper explores the adaptation of Kalman Filter-based track finding algorithms for parallel hardware architectures like multi-core CPUs, GPUs, and Xeon Phi to improve computational efficiency in particle physics event reconstruction.

Contribution

It investigates the potential and limitations of implementing Kalman Filter algorithms on modern parallel hardware architectures for particle tracking.

Findings

Kalman Filter algorithms can be adapted for parallel architectures.

Parallel implementations show promising performance gains.

Limitations include challenges in data synchronization and algorithm complexity.

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, but the future will be even more exciting. In order 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. Example technologies today include Intel's Xeon Phi and GPGPUs. Track finding and fitting is one of the most computationally challenging problems for event reconstruction in particle physics. At the High Luminosity LHC, for example, this will be by far the dominant problem. The most common track finding techniques in use today are however 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. We report the results of our investigations into the potential and limitations of these algorithms on the new parallel hardware.