Exploration of Optimization Options for Increasing Performance of a GPU Implementation of a Three-dimensional Bilateral Filter

Bethel, E. Wes ; Bethel, E. Wes

Lawrence Berkeley National Laboratory

关键词: 97 Mathematics And Computing Gpu Computing, Performance Optimization, Bilateral Filter;    Gpu Computing, Performance Optimization, Bilateral Filter;   

DOI  :  10.2172/1082192
RP-ID  :  LBNL-5406E
RP-ID  :  DE-AC02-05CH11231
RP-ID  :  1082192

美国|英语

来源: UNT Digital Library

【 摘 要 】

This report explores using GPUs as a platform for performing high performance medical image data processing, specifically smoothing using a 3D bilateral filter, which performs anisotropic, edge-preserving smoothing. The algorithm consists of a running a specialized 3D convolution kernel over a source volume to produce an output volume. Overall, our objective is to understand what algorithmic design choices and configuration options lead to optimal performance of this algorithm on the GPU. We explore the performance impact of using different memory access patterns, of using different types of device/on-chip memories, of using strictly aligned and unaligned memory, and of varying the size/shape of thread blocks. Our results reveal optimal configuration parameters for our algorithm when executed sample 3D medical data set, and show performance gains ranging from 30x to over 200x as compared to a single-threaded CPU implementation.