arXiv:2610.33932 · v2
FastSAR: Open-Source SAR Image Formation on CPUs, GPUs, and TPUs, and a Matched Comparison of Algorithms, Precisions, and Processors
摘要
The number of spaceborne synthetic aperture radar (SAR) collectors is growing exponentially, each returning raw collections that must be formed into images. Exact backprojection needs 1.6 trillion interpolated reads and 90 trillion floating-point operations for a 108-megapixel Umbra image. How long it takes to form an image, and at what price, depends on the algorithm, the arithmetic, and the hardware. Open-source implementations have never been compared on matched hardware against a common reference, and none run on Google tensor processing units (TPUs). This paper presents FastSAR, an open-source SAR image formation library for central processing units (CPUs), NVIDIA graphics processing units (GPUs), and TPUs. With it, we compare three SAR image formation algorithms, four floating-point formats, and six cloud devices in every supported combination, with cost per image at on-demand cloud prices as the fair common measure. FastSAR is 1.06 to 22,000 times faster than the tested open-source implementations at equal or better accuracy, and the full suite of hardware, algorithm, and floating-point combinations lets a user choose the Pareto-optimal configuration for their balance of accuracy, speed, and cost. The L4 GPU forms images the cheapest overall. Larger GPUs are faster but cost more per image because their price rises faster than their speed. The TPUs come close to the GPUs in cost, but only for the factorized algorithm, and the CPU is far less efficient in both speed and cost. Lower precision formats buy less of a speedup than expected, and each step down in precision costs accuracy.
作者备注：39 pages, 8 figures. Submitted to IEEE Transactions on Geoscience and Remote Sensing. Code: https://github.com/saulpingerman/FastSAR (pip install fastsar); measurements and paper source: https://github.com/saulpingerman/sar-accel-study