Abstract
High-performance GPU-accelerated particle filter methods are critical for object detection applications, ranging from autonomous driving, robot localization, to time-series prediction. In this work, we investigate the design, development and optimization of particle-filter using half-precision on CUDA cores and compare their performance and accuracy with single- and double-precision baselines on Nvidia V100, A100, A40 and T4 GPUs. To mitigate numerical instability and precision losses, we introduce algorithmic changes in the particle filters. Using half-precision leads to a performance improvement of 1.5–2 \(\times \) and 2.5–4.6 \(\times \) with respect to single- and double-precision baselines respectively, at the cost of a relatively small loss of accuracy.
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Acknowledgements
This work is funded by the European Union. This work has received funding from the European High Performance Computing Joint Undertaking (JU) and Sweden, Finland, Germany, Greece, France, Slovenia, Spain, and the Czech Republic under grant agreement No 101093261. The computations were enabled by resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) at KTH, partially funded by the Swedish Research Council through grant agreement no. 2022-06725.
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Schieffer, G., Pornthisan, N., Medeiros, D., Markidis, S., Wahlgren, J., Peng, I. (2024). Boosting the Performance of Object Tracking with a Half-Precision Particle Filter on GPU. In: Zeinalipour, D., et al. Euro-Par 2023: Parallel Processing Workshops. Euro-Par 2023. Lecture Notes in Computer Science, vol 14351. Springer, Cham. https://doi.org/10.1007/978-3-031-50684-0_23
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