Modern synchrotron light sources and detectors produce data at such scale and complexity that large-scale computation is required to unleash their full power. One of the widely used imaging techniques that generates data at tens of gigabytes per second is computed tomography (CT). Although CT experiments result in rapid data generation, the analysis and reconstruction of the collected data may require hours or even days of computation time with a medium-sized workstation, which hinders the scientific progress that relies on the results of analysis. We present Trace, a data-intensive computing engine that we have developed to enable high-performance implementation of iterative tomographic reconstruction algorithms for parallel computers. Trace provides fine-grained reconstruction of tomography datasets using both (thread-level) shared memory and (process-level) distributed memory parallelization. Trace utilizes a special data structure called replicated reconstruction object to maximize application performance. We also present the optimizations that we apply to the replicated reconstruction objects and evaluate them using tomography datasets collected at the Advanced Photon Source. Our experimental evaluations show that our optimizations and parallelization techniques can provide 158× speedup using 32 compute nodes (384 cores) over a single-core configuration and decrease the end-to-end processing time of a large sinogram (with 4501 × 1 × 22,400 dimensions) from 12.5 h to <5 min per iteration. The proposed tomographic reconstruction engine can efficiently process large-scale tomographic data using many compute nodes and minimize reconstruction times.

中文翻译：

---

Trace：用于大规模数据集的高通量断层扫描重建引擎。

现代同步加速器光源和探测器产生的数据规模和复杂性如此之高，以至于需要大规模计算才能发挥其全部威力。计算机断层扫描 (CT) 是广泛使用的成像技术之一，每秒生成数十 GB 的数据。虽然CT实验可以快速生成数据，但对收集到的数据进行分析和重建可能需要中型工作站数小时甚至数天的计算时间，这阻碍了依赖于分析结果的科学进步。我们推出了 Trace，这是我们开发的一种数据密集型计算引擎，旨在实现并行计算机的迭代断层重建算法的高性能实现。Trace 使用（线程级）共享内存和（进程级）分布式内存并行化提供断层扫描数据集的细粒度重建。Trace 利用称为复制重建对象的特殊数据结构来最大限度地提高应用程序性能。我们还介绍了应用于复制重建对象的优化，并使用在高级光子源收集的断层扫描数据集对其进行评估。我们的实验评估表明，我们的优化和并行化技术可以使用 32 个计算节点（384 个核心）在单核配置上提供 158 倍的加速，并减少大型正弦图（4501 × 1 × 22,400每次迭代从 12.5 小时缩短到 <5 分钟。所提出的断层扫描重建引擎可以使用许多计算节点有效地处理大规模断层扫描数据并最大限度地减少重建时间。