TY - GEN
T1 - A tutorial on the implementations of linear image filters in CPU and GPU
AU - Pardo, Alvaro
N1 - Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.
PY - 2018
Y1 - 2018
N2 - This article presents an overview of the implementation of linear image filters in CPU and GPU. The main goal is to present a self contained discussion of different implementations and their background using tools from digital signal processing. First, using signal processing tools, we discuss different algorithms and estimate their computational cost. Then, we discuss the implementation of these filters in CPU and GPU. It is very common to find in the literature that GPUs can easily reduce computational times in many algorithms (straightforward implementations). In this work we show that GPU implementations not always reduce the computational time but also not all algorithms are suited for GPUs. We believe this is a review that can help researchers and students working in this area. Although the experimental results are not meant to show which is the best implementation (in terms of running time), the main results can be extrapolated to CPUs and GPUs of different capabilities.
AB - This article presents an overview of the implementation of linear image filters in CPU and GPU. The main goal is to present a self contained discussion of different implementations and their background using tools from digital signal processing. First, using signal processing tools, we discuss different algorithms and estimate their computational cost. Then, we discuss the implementation of these filters in CPU and GPU. It is very common to find in the literature that GPUs can easily reduce computational times in many algorithms (straightforward implementations). In this work we show that GPU implementations not always reduce the computational time but also not all algorithms are suited for GPUs. We believe this is a review that can help researchers and students working in this area. Although the experimental results are not meant to show which is the best implementation (in terms of running time), the main results can be extrapolated to CPUs and GPUs of different capabilities.
KW - CUDA
KW - GPU
KW - Linear image filtering
UR - http://www.scopus.com/inward/record.url?scp=85041836726&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-75214-3_11
DO - 10.1007/978-3-319-75214-3_11
M3 - Contribución a la conferencia
AN - SCOPUS:85041836726
SN - 9783319752136
T3 - Communications in Computer and Information Science
SP - 111
EP - 121
BT - Computer Science – CACIC 2017 - 23rd Argentine Congress, Revised Selected Papers
A2 - De Giusti, Armando Eduardo
PB - Springer Verlag
T2 - 23rd Argentine Congress of Computer Science, CACIC 2017
Y2 - 9 October 2017 through 13 October 2017
ER -