  GPU-based beamforming is a relatively unexplored area in radio astronomy,
possibly due to the assumption that any such system will be severely limited by
the PCIe bandwidth required to transfer data to the GPU. We have developed a
CUDA-based GPU implementation of a coherent beamformer, specifically designed
and optimised for deployment at the BEST-2 array which can generate an
arbitrary number of synthesized beams for a wide range of parameters. It
achieves $\sim$1.3 TFLOPs on an NVIDIA Tesla K20, approximately 10x faster than
an optimised, multithreaded CPU implementation. This kernel has been integrated
into two real-time, GPU-based time-domain software pipelines deployed at the
BEST-2 array in Medicina: a standalone beamforming pipeline and a transient
detection pipeline. We present performance benchmarks for the beamforming
kernel as well as the transient detection pipeline with beamforming
capabilities as well as results of test observation.
