CudaFilters: A SignalPlant library for GPU-accelerated FFT and FIR filtering

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081731%3A_____%2F18%3A00489552" target="_blank" >RIV/68081731:_____/18:00489552 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1002/spe.2507" target="_blank" >http://dx.doi.org/10.1002/spe.2507</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/spe.2507" target="_blank" >10.1002/spe.2507</a>

Jazyk výsledku

angličtina

Název v původním jazyce

CudaFilters: A SignalPlant library for GPU-accelerated FFT and FIR filtering

Popis výsledku v původním jazyce

Signal filtering is one of the essential tasks in signal processing. It may become an extremely time-consuming process, as in the case of intracranial electroencephalogram recordings (eg, 30-min records) with a large number of channels (up to 256) and high sampling frequencies (up to 5kHz in research related to ultra-high-frequency oscillations). The usual way of dealing with time consumption is process parallelization. Moreover, parallelization using graphic processing unit (GPU) allows further shortening of computing times thanks to the large number of GPU cores. This paper describes a library for GPU-accelerated finite impulse response (FIR) and fast Fourier transform (FFT) filteringCudaFilters. This library is designed for SignalPlant softwarea free tool for signal analysis. The resultant acceleration in computing times was 5x to 40x depending on the task, data, and hardware configuration. The results were also compared to computing speeds in Matlab.

Název v anglickém jazyce

CudaFilters: A SignalPlant library for GPU-accelerated FFT and FIR filtering

Popis výsledku anglicky

Signal filtering is one of the essential tasks in signal processing. It may become an extremely time-consuming process, as in the case of intracranial electroencephalogram recordings (eg, 30-min records) with a large number of channels (up to 256) and high sampling frequencies (up to 5kHz in research related to ultra-high-frequency oscillations). The usual way of dealing with time consumption is process parallelization. Moreover, parallelization using graphic processing unit (GPU) allows further shortening of computing times thanks to the large number of GPU cores. This paper describes a library for GPU-accelerated finite impulse response (FIR) and fast Fourier transform (FFT) filteringCudaFilters. This library is designed for SignalPlant softwarea free tool for signal analysis. The resultant acceleration in computing times was 5x to 40x depending on the task, data, and hardware configuration. The results were also compared to computing speeds in Matlab.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20602 - Medical laboratory technology (including laboratory samples analysis; diagnostic technologies) (Biomaterials to be 2.9 [physical characteristics of living material as related to medical implants, devices, sensors])

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Software-Practice & Experience

ISSN

0038-0644

e-ISSN

—

Svazek periodika

48

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

7

Strana od-do

3-9

Kód UT WoS článku

000417617500001

EID výsledku v databázi Scopus

2-s2.0-85019946465