Pregled bibliografske jedinice broj: 846794
Optimization of losless image compression method for GPGPU
Optimization of losless image compression method for GPGPU // Proc. 2016 18th Mediterranean Electrotechnical Conference (MELECON)
Limassol: Institute of Electrical and Electronics Engineers (IEEE), 2016. str. 1-8 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 846794 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Optimization of losless image compression method for GPGPU
Autori
Strižić, Luka ; Knezović, Josip
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proc. 2016 18th Mediterranean Electrotechnical Conference (MELECON)
/ - Limassol : Institute of Electrical and Electronics Engineers (IEEE), 2016, 1-8
ISBN
978-1-5090-0057-9
Skup
2016 18th Mediterranean Electrotechnical Conference (MELECON)
Mjesto i datum
Limassol, Cipar, 18.04.2016. - 20.04.2016
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
parallel architectures; data compression; energy conservation; graphics processing units; image coding
(paralelne arhitekture; kompresija podataka; ušteda energije; grafički koprocesori; kodiranje slikovnih podataka)
Sažetak
This paper presents power and execution time efficient implementation of highly adaptive lossless image compression method based on predictor classification and blending, denoted as CBPC coder. Power efficiency is becoming increasingly important in both: datacenters and consumer electronics. This is why we aimed to target its optimization, as well as throughput, of CBPC coder on a CPU and a GPU, using CUDA for the latter. Tests were conducted using mainstream components in a desktop PC and high end components in a server. The CUDA version proved to be both more efficient and faster than a singlethreaded CPU version, however, further tests should be done comparing even more optimized CUDA version against multithreaded CPU implementation to cover the whole spectrum and to achieve better performance per watt for both consumer desktop and server system running the method. Finally, we demonstrate the benefits of GPGPU approach for compute-intensive, fine grained data-parallel parts of the algorithm, notably predictor classification and blending computations.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb
Profili:
Josip Knezović
(autor)