R-CCS Cafe 第223回 第3部

Tomographic imaging is a widely used technology that requires intensive computations. We will introduce our activity on high-performance image reconstruction using distributed systems. We take advantage of the heterogeneity of GPU-accelerated systems by overlapping the filtering computation and back-projection on CPUs and GPUs, respectively. We propose a novel decomposition scheme and reconstruction algorithm for distributed image reconstruction. This scheme enables arbitrarily large input/output sizes, eliminates the redundancy arising in the end-to-end pipeline, and improves the scalability by replacing two communication collectives with only one segmented reduction. More specifically, we propose a distributed framework for high-resolution image reconstruction on state-of-the-art GPU-accelerated supercomputers and implement the proposed decomposition scheme in a framework that is useful for all current-generation tomographic imaging devices.