  Graphical Processing Units (GPUs) have recently become a valuable computing
tool for the acquisition of data at high rates and for a relatively low cost.
The devices work by parallelizing the code into thousands of threads, each
executing a simple process, such as identifying pulses from a waveform
digitizer. The CUDA programming library can be used to effectively write code
to parallelize such tasks on Nvidia GPUs, providing a significant upgrade in
performance over CPU based acquisition systems.
  The muon $g$-$2$ experiment at Fermilab is heavily relying on GPUs to process
its data. The data acquisition system for this experiment must have the ability
to create deadtime-free records from 700 $\mu$s muon spills at a raw data rate
18 GB per second. Data will be collected using 1296 channels of $\mu$TCA-based
800 MSPS, 12 bit waveform digitizers and processed in a layered array of
networked commodity processors with 24 GPUs working in parallel to perform a
fast recording of the muon decays during the spill. The described data
acquisition system is currently being constructed, and will be fully
operational before the start of the experiment in 2017.
