  We examine the Xeon Phi, which is based on Intel's Many Integrated Cores
architecture, for its suitability to run the FDK algorithm--the most commonly
used algorithm to perform the 3D image reconstruction in cone-beam computed
tomography. We study the challenges of efficiently parallelizing the
application and means to enable sensible data sharing between threads despite
the lack of a shared last level cache. Apart from parallelization, SIMD
vectorization is critical for good performance on the Xeon Phi; we perform
various micro-benchmarks to investigate the platform's new set of vector
instructions and put a special emphasis on the newly introduced vector gather
capability. We refine a previous performance model for the application and
adapt it for the Xeon Phi to validate the performance of our optimized
hand-written assembly implementation, as well as the performance of several
different auto-vectorization approaches.
