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Computed Tomography (CT) is a key 3D imaging technology that fundamentallyrelies on the compute-intense back-projection operation to generate 3D volumes.GPUs are typically used for back-projection in production CT devices. However,with the rise of power-constrained micro-CT devices, and also the emergence ofCPUs comparable in performance to GPUs, back-projection for CPUs could becomefavorable. Unlike GPUs, extracting parallelism for back-projection algorithmson CPUs is complex given that parallelism and locality are not explicitlydefined and controlled by the programmer, as is the case when using CUDA forinstance. We propose a collection of novel back-projection algorithms thatreduce the arithmetic computation, robustly enable vectorization, enforce aregular memory access pattern, and maximize the data locality. We alsoimplement the novel algorithms as efficient back-projection kernels that areperformance portable over a wide range of CPUs. Performance evaluation using avariety of CPUs from different vendors and generations demonstrates that ourback-projection implementation achieves on average 5.2x speedup over themulti-threaded implementation of the most widely used, and optimized, openlibrary. With a state-of-the-art CPU, we reach performance that rivalstop-performing GPUs.

Performance Portable Back-projection Algorithms on CPUs: Agnostic Data Locality and Vectorization Optimizations