  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.
