Amethyst - A Generalized on-the-Fly De/Re-compression Framework to Accelerate Data-Intensive Integer Operations on GPUs

Research output: Contribution to book/Conference proceedings/Anthology/ReportConference contributionContributedpeer-review

Contributors

Abstract

In this paper, we present Amethyst, a generalized on-the-fly de/re-compression framework for GPUs. Our developed framework allows to execute every computational function on every (un)compressed input format and to output the result in an arbitrary (un)compressed format. Thus, our on-the-fly de/re-compression framework approach automatically conducts a decompression on the input side and compression on the output side. To show the feasibility and applicability of our framework, we developed a prototype for integer data types using different integer compression formats. We use this prototype to present exhaustive evaluation results using a great variety of data-intensive operations ranging from simple additions up to compaction and compute-intensive matrix multiplications to validate the efficiency of Amethyst.

Details

Original languageEnglish
Title of host publicationAdvances in Databases and Information Systems - 28th European Conference, ADBIS 2024, Proceedings
EditorsJoe Tekli, Johann Gamper, Richard Chbeir, Yannis Manolopoulos
PublisherSpringer Science and Business Media B.V.
Pages107-120
Number of pages14
ISBN (print)9783031706288
Publication statusPublished - 1 Sept 2024
Peer-reviewedYes

Publication series

SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14918 LNCS
ISSN0302-9743

Conference

Title28th European Conference on Advances in Databases and Information Systems
Abbreviated titleADBIS 2024
Conference number28
Duration28 - 31 August 2024
Website
LocationChâteau-Neuf of Bayonne
CityBayonne
CountryFrance

External IDs

ORCID /0000-0001-8107-2775/work/176861682

Keywords

Research priority areas of TU Dresden

Keywords

  • Compression, GPU, Null Suppression, Performance Optimization