부산대학교 도서관

Conventional CPU-based algorithms for Computed Tomography reconstruction lack the computational efficiency necessary to process large, industrial datasets in a reasonable amount of time. Specifically, processing time for a single-pass, trillion volumetric pixel (voxel) reconstruction requires months to reconstruct using a high performance CPU-based workstation. An optimized, single workstation multi-GPU approach has shown performance increases by 2–3 orders-of-magnitude; however, reconstruction of future-size, trillion voxel datasets can still take an entire day to complete. This paper details an approach that further decreases runtime and allows for more diverse workstation environments by using a cluster of GPU-capable workstations. Due to the irregularity of the reconstruction tasks throughout the volume, using a cluster of multi-GPU nodes requires inventive topological structuring and data partitioning to avoid network bottlenecks and achieve optimal GPU utilization. This paper covers the cluster layout and non-linear weighting scheme used in this high-performance multi-GPU CT reconstruction algorithm and presents experimental results from reconstructing two large-scale datasets to evaluate this approach's performance and applicability to future-size datasets. Specifically, our approach yields up to a 20 percent improvement for large-scale data.