  The convex hull is a fundamental geometrical structure for many applications
where groups of points must be enclosed or represented by a convex polygon.
Although efficient sequential convex hull algorithms exist, and are constantly
being used in applications, their computation time is often considered an issue
for time-sensitive tasks such as real-time collision detection, clustering or
image processing for virtual reality, among others, where fast response times
are required. In this work we propose a parallel GPU-based adaptation of
heaphull, which is a state of the art CPU algorithm that computes the convex
hull by first doing a efficient filtering stage followed by the actual convex
hull computation. More specifically, this work parallelizes the filtering
stage, adapting it to the GPU programming model as a series of parallel
reductions. Experimental evaluation shows that the proposed implementation
significantly improves the performance of the convex hull computation, reaching
up to $4\times$ of speedup over the sequential CPU-based heaphull and between
$3\times \sim 4\times$ over existing GPU based approaches.
