  High-performance computing of array signal processing problems is a critical
task as real-time system performance is required for many applications. Noise
subspace-based Direction-of-Arrival (DOA) estimation algorithms are popular in
the literature since they provide higher angular resolution and higher
robustness. In this study, we investigate various optimization strategies for
high-performance DOA estimation on GPU and comparatively analyze alternative
implementations (MATLAB, C/C++ and CUDA). Experiments show that up to 3.1x
speedup can be achieved on GPU compared to the baseline multi-threaded CPU
implementation. The source code is publicly available at the following link:
https://github.com/erayhamza/NssDOACuda
