2011/11/06 by Spencer, Jason
#Computer Vision and Pattern Recognition (cs.CV) #Distributed #FOS: Computer and information sciences #Parallel #and Cluster Computing (cs.DC)
paper · doi:10.48550/arxiv.1111.1373
We examine the problem of optimizing classification tree evaluation for on-line and real-time applications by using GPUs. Looking at trees with continuous attributes often used in image segmentation, we first put the existing algorithms for serial and data-parallel evaluation on solid footings. We then introduce a speculative parallel algorithm designed for single instruction, multiple data (SIMD) architectures commonly found in GPUs. A theoretical analysis shows how the run times of data and speculative decompositions compare assuming independent processors. To compare the algorithms in the SIMD environment, we implement both on a CUDA 2.0 architecture machine and compare timings to a serial CPU implementation. Various optimizations and their effects are discussed, and results are given for all algorithms. Our specific tests show a speculative algorithm improves run time by 25% compared to a data decomposition.