# Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Processors and GPUs

## TL;DR

This paper discusses the development and optimization of parallelized Kalman filter algorithms for particle track reconstruction on many-core processors and GPUs, addressing computational challenges in high-energy physics experiments.

## Contribution

It introduces new methods for porting and optimizing Kalman filter-based track reconstruction algorithms on GPUs and many-core processors, enhancing performance for high-energy physics applications.

## Key findings

- Achieved significant parallel speedups on Intel Xeon and Xeon Phi.
- Developed new GPU implementations of Kalman filter algorithms.
- Improved understanding of processor architectures for physics computations.

## Abstract

For over a decade now, physical and energy constraints have limited clock speed improvements in commodity microprocessors. Instead, chipmakers have been pushed into producing lower-power, multi-core processors such as GPGPU, ARM and Intel MIC. Broad-based efforts from manufacturers and developers have been devoted to making these processors user-friendly enough to perform general computations. However, extracting performance from a larger number of cores, as well as specialized vector or SIMD units, requires special care in algorithm design and code optimization. One of the most computationally challenging problems in high-energy particle experiments is finding and fitting the charged-particle tracks during event reconstruction. This is expected to become by far the dominant problem in the High-Luminosity Large Hadron Collider (HL-LHC), for example. Today the most common track finding methods are those based on the Kalman filter. Experience with Kalman techniques on real tracking detector systems has shown that they are robust and provide high physics performance. This is why they are currently in use at the LHC, both in the trigger and offline. Previously we reported on the significant parallel speedups that resulted from our investigations to adapt Kalman filters to track fitting and track building on Intel Xeon and Xeon Phi. Here, we discuss our progresses toward the understanding of these processors and the new developments to port Kalman filter to NVIDIA GPUs.