Journal article 1484 views 44 downloads
InK-Compact: In-Kernel Stream Compaction and Its Application to Multi-Kernel Data Visualization on General-Purpose GPUs
Computer Graphics Forum, Volume: 32, Issue: 6, Pages: 178 - 188
Swansea University Author: Mark Jones
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...
Published in: | Computer Graphics Forum |
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ISSN: | 0167-7055 |
Published: |
2013
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URI: | https://cronfa.swan.ac.uk/Record/cronfa15061 |
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2022-06-14T15:52:47.2561609 v2 15061 2013-06-13 InK-Compact: In-Kernel Stream Compaction and Its Application to Multi-Kernel Data Visualization on General-Purpose GPUs 2e1030b6e14fc9debd5d5ae7cc335562 0000-0001-8991-1190 Mark Jones Mark Jones true false 2013-06-13 SCS 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. Journal Article Computer Graphics Forum 32 6 178 188 0167-7055 31 12 2013 2013-12-31 10.1111/cgf.12083 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University Not Required 2022-06-14T15:52:47.2561609 2013-06-13T13:56:07.0614533 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science D. M Hughes 1 I. S Lim 2 M. W Jones 3 A Knoll 4 B Spencer 5 Mark Jones 0000-0001-8991-1190 6 15061__24315__4c137e94173f42fd80dccbb1a5684eb6.pdf 2013_CGF_FK-Compact.pdf 2022-06-14T15:52:02.2528698 Output 8564355 application/pdf Accepted Manuscript true Linking old record to accepted manuscript false |
title |
InK-Compact: In-Kernel Stream Compaction and Its Application to Multi-Kernel Data Visualization on General-Purpose GPUs |
spellingShingle |
InK-Compact: In-Kernel Stream Compaction and Its Application to Multi-Kernel Data Visualization on General-Purpose GPUs Mark Jones |
title_short |
InK-Compact: In-Kernel Stream Compaction and Its Application to Multi-Kernel Data Visualization on General-Purpose GPUs |
title_full |
InK-Compact: In-Kernel Stream Compaction and Its Application to Multi-Kernel Data Visualization on General-Purpose GPUs |
title_fullStr |
InK-Compact: In-Kernel Stream Compaction and Its Application to Multi-Kernel Data Visualization on General-Purpose GPUs |
title_full_unstemmed |
InK-Compact: In-Kernel Stream Compaction and Its Application to Multi-Kernel Data Visualization on General-Purpose GPUs |
title_sort |
InK-Compact: In-Kernel Stream Compaction and Its Application to Multi-Kernel Data Visualization on General-Purpose GPUs |
author_id_str_mv |
2e1030b6e14fc9debd5d5ae7cc335562 |
author_id_fullname_str_mv |
2e1030b6e14fc9debd5d5ae7cc335562_***_Mark Jones |
author |
Mark Jones |
author2 |
D. M Hughes I. S Lim M. W Jones A Knoll B Spencer Mark Jones |
format |
Journal article |
container_title |
Computer Graphics Forum |
container_volume |
32 |
container_issue |
6 |
container_start_page |
178 |
publishDate |
2013 |
institution |
Swansea University |
issn |
0167-7055 |
doi_str_mv |
10.1111/cgf.12083 |
college_str |
Faculty of Science and Engineering |
hierarchytype |
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facultyofscienceandengineering |
hierarchy_top_title |
Faculty of Science and Engineering |
hierarchy_parent_id |
facultyofscienceandengineering |
hierarchy_parent_title |
Faculty of Science and Engineering |
department_str |
School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science |
document_store_str |
1 |
active_str |
0 |
description |
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. |
published_date |
2013-12-31T03:17:12Z |
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1763750373866078208 |
score |
11.016235 |