  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.
