Exploration of Optimization Options for Increasing Performance of a GPU Implementation of a Three-Dimensional Bilateral Filter
Lawrence Berkeley National Laboratory, Berkeley CA, USA, 94720
Technical Report LBNL-5406E, Lawrence Berkeley National Laboratory, 2012
@techreport{Bethel:GPUOptimization:2012,
author={E. Wes Bethel},
title={Exploration of Optimization Options for Increasing Performance of a GPU Implementation of a Three-Dimensional Bilateral Filter},
institution={Lawrence Berkeley National Laboratory},
address={Berkeley, CA, USA, 94720},
year={2012},
number={LBNL-5406E}
}
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.
May 9, 2012 by hgpu
