Signal processing for a reverse-GPS wildlife tracking system: CPU and GPU implementation experiences

Yaniv Rubinpur, Sivan Toledo*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


We present robust high-performance implementations of signal-processing tasks performed by a high-throughput wildlife tracking system called ATLAS. The system tracks radio transmitters attached to wild animals by estimating the time of arrival of radio packets to multiple receivers (base stations). Time-of-arrival estimation of wideband radio signals is computationally expensive, especially in acquisition mode (when the time of transmission is not known, not even approximately). These computations are a bottleneck that limits the throughput of the system. We developed a sequential high-performance CPU implementation of the computations a few years back, and more recently a GPU implementation. Both strive to balance performance with simplicity, maintainability, and development effort, as most real-world codes do. The article reports on the two implementations and carefully evaluates their performance. The evaluations indicates that the GPU implementation dramatically improves performance and power-performance relative to the sequential CPU implementation running on a desktop CPU typical of the computers in current base stations. Performance improves by more than 50X on a high-end GPU and more than 4X with a GPU platform that consumes almost 5 times less power than the CPU platform. Performance-per-Watt ratios also improve (by more than 16X), and so do the price-performance ratios.

Original languageEnglish
Article numbere6506
JournalConcurrency Computation Practice and Experience
Issue number14
StatePublished - 25 Jun 2022


  • CUDA
  • GPU
  • arrival time estimation
  • digital signal processing


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