Acceleration of Particle Swarm Optimization with AVX Instructions
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10251367" target="_blank" >RIV/61989100:27240/23:10251367 - isvavai.cz</a>
Nalezeny alternativní kódy
RIV/61989100:27740/23:10251367
Výsledek na webu
<a href="https://www.mdpi.com/2076-3417/13/2/734" target="_blank" >https://www.mdpi.com/2076-3417/13/2/734</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/app13020734" target="_blank" >10.3390/app13020734</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Acceleration of Particle Swarm Optimization with AVX Instructions
Popis výsledku v původním jazyce
Parallel implementations of algorithms are usually compared with single-core CPU performance. The advantage of multicore vector processors decreases the performance gap between GPU and CPU computation, as shown in many recent pieces of research. With the AVX-512 instruction set, there will be another performance boost for CPU computations. The availability of parallel code running on CPUs made them much easier and more accessible than GPUs. This article compares the performances of parallel implementations of the particle swarm optimization algorithm. The code was written in C++, and we used various techniques to obtain parallel execution through Advanced Vector Extensions. We present the performance on various benchmark functions and different problem configurations. The article describes and compares the performance boost gained from parallel execution on CPU, along with advantages and disadvantages of parallelization techniques.
Název v anglickém jazyce
Acceleration of Particle Swarm Optimization with AVX Instructions
Popis výsledku anglicky
Parallel implementations of algorithms are usually compared with single-core CPU performance. The advantage of multicore vector processors decreases the performance gap between GPU and CPU computation, as shown in many recent pieces of research. With the AVX-512 instruction set, there will be another performance boost for CPU computations. The availability of parallel code running on CPUs made them much easier and more accessible than GPUs. This article compares the performances of parallel implementations of the particle swarm optimization algorithm. The code was written in C++, and we used various techniques to obtain parallel execution through Advanced Vector Extensions. We present the performance on various benchmark functions and different problem configurations. The article describes and compares the performance boost gained from parallel execution on CPU, along with advantages and disadvantages of parallelization techniques.
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Návaznosti výsledku
Projekt
<a href="/cs/project/EF15_003%2F0000466" target="_blank" >EF15_003/0000466: Umělá inteligence a uvažování</a><br>
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Ostatní
Rok uplatnění
2023
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Applied Sciences
ISSN
2076-3417
e-ISSN
2076-3417
Svazek periodika
13
Číslo periodika v rámci svazku
2
Stát vydavatele periodika
CH - Švýcarská konfederace
Počet stran výsledku
10
Strana od-do
—
Kód UT WoS článku
000916667400001
EID výsledku v databázi Scopus
2-s2.0-85146721500