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

Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/GutachtenBeitrag in KonferenzbandBeigetragenBegutachtung

Beitragende

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

OriginalspracheEnglisch
TitelAdvances in Databases and Information Systems - 28th European Conference, ADBIS 2024, Proceedings
Redakteure/-innenJoe Tekli, Johann Gamper, Richard Chbeir, Yannis Manolopoulos
Herausgeber (Verlag)Springer Science and Business Media B.V.
Seiten107-120
Seitenumfang14
ISBN (Print)9783031706288
PublikationsstatusVeröffentlicht - 1 Sept. 2024
Peer-Review-StatusJa

Publikationsreihe

ReiheLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band14918 LNCS
ISSN0302-9743

Konferenz

Titel28th European Conference on Advances in Databases and Information Systems
KurztitelADBIS 2024
Veranstaltungsnummer28
Dauer28 - 31 August 2024
Webseite
OrtChâteau-Neuf of Bayonne
StadtBayonne
LandFrankreich

Externe IDs

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

Schlagworte

Forschungsprofillinien der TU Dresden

Schlagwörter

  • Compression, GPU, Null Suppression, Performance Optimization