Large-scale Tomography Computation

我们提出了一种新的方法来解决层析图像重建中的存储和计算挑战，这是一种高分辨率的显微成像技术。我们的方法包括将图像梯度和衍射测量分解为平铺，从而减少了内存占用。此外，我们提出了一种并行处理技术来促进大量GPU之间的高效通信和并行流水线，例如多达4000个GPU。除了GPU加速之外，我们还探索了在CT图像重建中使用FPGA加速器来实现更高的计算和能量效率。

We present a novel approach to address the memory and computation challenges in ptychographic image reconstruction, a high-resolution microscopic imaging technique. Our method involves decomposing image gradients and diffraction measurements into tiles, resulting in a reduced memory footprint. Furthermore, we propose a parallel processing technique that facilitates efficient communication and parallel pipelining across a large number of GPUs, such as up to 4000 GPUs.In addition to GPU acceleration, we explore the use of FPGA accelerators to achieve improved computing and energy efficiency in Computed Tomography image reconstruction.