2013/12/17 by Johannes Hofmann, Jan Treibig, Hofmann, Johannes +5
Computer Science · Medicine · #Advanced MRI Techniques and Applications #Computer Vision and Pattern Recognition (cs.CV) #Distributed #FOS: Computer and information sciences #Medical Imaging Techniques and Applications #Parallel #Parallel Computing and Optimization Techniques #Performance (cs.PF) #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.1401.3615
openalex publication_date 2013/12/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.