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We examine the problem of optimizing classification tree evaluation foron-line and real-time applications by using GPUs. Looking at trees withcontinuous attributes often used in image segmentation, we first put theexisting algorithms for serial and data-parallel evaluation on solid footings.We then introduce a speculative parallel algorithm designed for singleinstruction, multiple data (SIMD) architectures commonly found in GPUs. Atheoretical analysis shows how the run times of data and speculativedecompositions compare assuming independent processors. To compare thealgorithms in the SIMD environment, we implement both on a CUDA 2.0architecture machine and compare timings to a serial CPU implementation.Various optimizations and their effects are discussed, and results are givenfor all algorithms. Our specific tests show a speculative algorithm improvesrun time by 25% compared to a data decomposition.

Speculative Parallel Evaluation Of Classification Trees On GPGPU Compute Engines