A Hybrid CPU-GPU System for Stitching Large Scale Optical Microscopy Images

Abstract

Researchers in various fields are using optical microscopy to acquire very large images, 10000 - 200000 of pixels per side. Optical microscopes acquire these images as grids of overlapping partial images (thousands of pixels per side) that are then stitched together via software. Composing such large images is a compute and data intensive task even for modern machines. Researchers compound this difficulty further by obtaining time-series, volumetric, or multiple channel images with the resulting data sets now having or approaching terabyte sizes. We present a scalable hybrid CPU-GPU implementation of image stitching that processes large image sets at near interactive rates. Our implementation scales well with both image sizes and the number of CPU cores and GPU cards in a machine. It processes a grid of 42 × 59 tiles into a 17 k × 22 k pixels image in 43 s (end-to-end execution times) when using one NVIDIA Tesla C2070 card and two Intel Xeon E-5620 quad-core CPUs, and in 29 s when using two Tesla C2070 cards and the same two CPUs. It also composes and renders the composite image without saving it in 15 s. In comparison, ImageJ/Fiji, which is widely used by biologists, has an image stitching plugin that takes > 3.6 h for the same workload despite being multithreaded and executing the same mathematical operators, it composes and saves the large image in an additional 1.5 h. This implementation takes advantage of coarse-grain parallelism. It organizes the computation into a pipeline architecture that spans CPU and GPU resources and overlaps computation with data motion. The implementation achieves a nearly 10× performance improvement over our optimized non-pipeline GPU implementation and demonstrates near-linear speedup when increasing CPU thread count and increasing number of GPUs.