Volume reconstruction by backprojection is the computational bottleneck in many interventional clinical computed tomography (CT) applications. Today vendors in this field replace special purpose hardware accelerators by standard hardware like multicore chips and GPGPUs.  This paper presents low-level optimizations for the backprojection algorithm, guided by a thorough performance analysis on four generations of Intel multicore processors (Harpertown, Westmere, Nehalem EX, and Sandy Bridge). We choose the RabbitCT benchmark, a standardized testcase well supported in industry, to ensure transparent and comparable results. Our aim is to provide not only the fastest  implementation but also compare to performance models and hardware counter data in order to fully understand the results. We separate the influence of algorithmic optimizations, parallelization, SIMD vectorization, and microarchitectural issues on performance and pinpoint problems with current  instruction set extensions on standard CPUs (SSE, AVX)  . Finally we compare our results to the best GPGPU implementations available for this open competition benchmark.
Volume reconstruction by backprojection is the computational bottleneck in many interventional clinical computed tomography (CT) applications. Today vendors in this field replace special purpose hardware accelerators by standard hardware like multicore chips and GPGPUs. Medical imaging algorithms are on the verge of employing High Performance Computing (HPC) technology, and are therefore an interesting new candidate for optimization. This paper presents low-level optimizations for the backprojection algorithm, guided by a thorough performance analysis on four generations of Intel multicore processors (Harpertown, Westmere, Westmere EX, and Sandy Bridge). We choose the RabbitCT benchmark, a standardized testcase well supported in industry, to ensure transparent and comparable results. Our aim is to provide not only the fastest possible implementation but also compare to performance models and hardware counter data in order to fully understand the results. We separate the influence of algorithmic optimizations, parallelization, SIMD vectorization, and microarchitectural issues and pinpoint problems with current SIMD instruction set extensions on standard CPUs (SSE, AVX) . The use of assembly language is mandatory for best performance . Finally we compare our results to the best GPGPU implementations available for this open competition benchmark.
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Medical imaging algorithms are on the verge of employing High Performance Computing (HPC) technology, and are therefore an interesting new candidate for optimization.
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Nehalem
Westmere
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possible
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on performance
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SIMD
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. The use of assembly language is mandatory for best performance