No Cover Image

Journal article 1039 views

InK-Compact: In-Kernel Stream Compaction and Its Application to Multi-Kernel Data Visualization on General-Purpose GPUs

D. M Hughes, I. S Lim, M. W Jones, A Knoll, B Spencer, Mark Jones Orcid Logo

Computer Graphics Forum, Volume: 32, Issue: 6, Pages: 178 - 188

Swansea University Author: Mark Jones Orcid Logo

Check full text

DOI (Published version): 10.1111/cgf.12083

Abstract

Stream compaction is an important parallel computing primitive that produces a reduced (compacted) output stream consisting of only valid elements from an input stream containing both invalid and valid elements. Computing on this compacted stream rather than the mixed input stream leads to improveme...

Full description

Published in: Computer Graphics Forum
ISSN: 0167-7055
Published: 2013
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa15061
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract: Stream compaction is an important parallel computing primitive that produces a reduced (compacted) output stream consisting of only valid elements from an input stream containing both invalid and valid elements. Computing on this compacted stream rather than the mixed input stream leads to improvements in performance, load balancing and memory footprint. Stream compaction has numerous applications in a wide range of domains: e.g. deferred shading, isosurface extraction and surface voxelization in computer graphics and visualization. We present a novel In-Kernel stream compaction method, where compaction is completed before leaving an operating kernel. This contrasts with conventional parallel compaction methods that require leaving the kernel and running a prefix sum kernel followed by a scatter kernel. We apply our compaction methods to ray-tracing-based visualization of volumetric data. We demonstrate that the proposed In-Kernel compaction outperforms the standard out-of-kernel Thrust parallel-scan method for performing stream compaction in this real-world application. For the data visualization, we also propose a novel multi-kernel ray-tracing pipeline for increased thread coherency and show that it outperforms a conventional single-kernel approach.
College: Faculty of Science and Engineering
Issue: 6
Start Page: 178
End Page: 188