We demonstrate Gpufit’s performance by comparing Gpufit’s speed to other state-of-the-art nonlinear curve fitting packages for execution on an NVIDIA Tesla K20c GPU. We benchmark the time it takes to fit the same data set using available open source software, including the C programming language, Matlab, and the GPU-based software packages NiftyReg (Gpufit’s CPU-based counterpart) and pynifti (Gpufit’s GPU-based counterpart). We use a representative small-scale image dataset, representing a single confocal image for one channel, to indicate the gains in speed. Both Gpufit and NiftyReg produce a nonlinear curve fit, whereas pynifti produces a linear fit. We find that Gpufit outperforms NiftyReg, by a factor of up to 42, and we find that Gpufit outperforms pynifti by a factor of up to 36. The comparisons of performance are shown in Table 1. This performance difference is due to two factors. First, the nature of a GPU is ideally suited to parallel computing, making the execution of the LMA on a GPU more efficient than on a CPU. Second, Gpufit is optimized to take full advantage of the GPU’s resources, including the number of threads and their size, and the memory bandwidth between the GPU and the host’s CPU. Our software produces speedups because it is written to account for the nature of the GPU architecture (Supplementary Fig. S1), and it carefully tailors the algorithm to the architecture, resulting in a relatively simple, straightforward-to-use package.
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