The Megapixel Approach for Efficient Execution of Irregular Wavefront Algorithms on GPUs

Abstract

Morphological operations are critical in high-resolution biomedical image processing. Their efficient execution relies on an irregular flood-filling strategy consolidated in the Irregular Wavefront Propagation Pattern (IWPP). IWPP was designed for GPUs and achieved significant gains compared to previous work. Here, however, we have revisited IWPP to identify the key limitations of its GPU implementation and proposed a novel more efficient strategy. In particular, the IWPP most demanding phase consists of tracking active pixels, those contributing to the output, that are the ones processed during the execution. This computational strategy leads to irregular memory access, divergent execution, and high storage (queue) management costs. To address these aspects, we have proposed the novel execution strategy called Irregular Wavefront Megapixel Propagation Pattern (IWMPP). IWMPP introduces a coarse-grained execution approach based on fixed-size square regions (instead of pixels in IWPP), referred to as megapixels (MPs). This design reduces the number of elements tracked and enables a regular processing within MPs that, in turn, improves thread divergence and memory accesses. IWMPP introduces optimizations, such as Duplicate Megapixel Removal (DMR) to avoid MPs recomputation and Tiled-Ordered (TO) execution that enforces a semistructured MPs execution sequence to improve data propagation efficiency. Experimental results using large tissue cancer images demonstrated that the IWMPP GPU attains significant gains over the state-of-the-art (IWPP). For morphological reconstruction, fill holes, and h-maxima operations, on the RTX 4090, the IWMPP GPU is up to 17.9×, 45.6×, and 14.9× faster than IWPP GPU, respectively, while at the same time reducing memory demands. IWMPP is an important step to enable quick processing of large imaging datasets.