Comparing dataflow and OpenMP programming for speaker recognition applications

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

Beitragende

Abstract

The still increasing number of transistors per chip oered by Moore’s law, together with the Post-Dennard scaling era shifted the performance gain from frequency increase to multi-core processing. Consequently, the support of parallel execution of applications is becoming mandatory. Furthermore, the need for ecient parallel models and languages is more critical for the embedded domain, due to power consumption and memory constraints, among others. This work focuses on parallelizing an embedded speaker recognition application, which is a biometric technique for identication. While a lot of work has been done for speech recognition, fewer eorts have focused on recognizing who the speaker is. In this paper, we analyze two implementations for speaker recognition applications (SRA), namely dataow and shared memory programming models. More precisely, we use Process Networks (PNs) as a dataow representation, which is an intuitive way to design streaming applications. We use the language “C for Process Networks” for the dataow implementation and OpenMP for the shared memory one. For two dierent target platforms, we compared two implementations using OpenMP (exploring data-level parallelism only and with pipelining) against a dataow-based compiled implementation that allows for functional optimization. Despite faster communication over shared memory, we show that the dataow model is superior in terms of performance (up to twice as fast).

Details

OriginalspracheEnglisch
TitelPARMA-DITAM 2019 - Proceedings
Herausgeber (Verlag)Association for Computing Machinery (ACM), New York
ISBN (elektronisch)9781450363211
PublikationsstatusVeröffentlicht - 21 Jan. 2019
Peer-Review-StatusJa

Konferenz

Titel10th Workshop on Parallel Programming and Run-Time Management Techniques for Many-Core Architectures and 8th Workshop on Design Tools and Architectures For Multicore Embedded Computing Platforms
KurztitelPARMA-DITAM 2019
Dauer21 Januar 2019
StadtValencia
LandSpanien

Externe IDs

ORCID /0000-0002-5007-445X/work/141545542

Schlagworte

Forschungsprofillinien der TU Dresden

Schlagwörter

  • Dataow models, Multicore programming, Shared memory programming, Speaker recognition