Optimization principles and application performance evaluation of a multithreaded GPU using CUDA
Proceedings of the 13th ACM SIGPLAN Symposium on Principles and practice of parallel programming
Program optimization space pruning for a multithreaded gpu
Proceedings of the 6th annual IEEE/ACM international symposium on Code generation and optimization
Improved procedure placement for set associative caches
CASES '10 Proceedings of the 2010 international conference on Compilers, architectures and synthesis for embedded systems
An efficient compiler framework for cache bypassing on GPUs
Proceedings of the International Conference on Computer-Aided Design
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Real-time 3D sound localization is an important technology for various applications such as camera steering systems, robotics audition, and gunshot direction. 3D sound localization adds a new dimension, but also significantly increases the computational requirements. Real-time 3D sound localization continuously processes large volumes of data for each possible 3D direction and acoustic frequency range. Such highly demanding compute requirements outpace current CPU compute abilities. This paper develops a real-time implementation of 3D sound localization on Graphical Processing Units (GPUs). Massively parallel GPU architectures are shown to be well suited for 3D sound localization. We optimize various aspects of GPU implementation, such as number of threads per thread block, register allocation per thread, and memory data layout for performance improvement. Experiments indicate that our GPU implementation achieves 501X and 130X speedup compared to a single-thread and a multi-thread CPU implementation respectively, thus enabling real-time operation of 3D sound localization.