Re: [LAD] Linux-audio-dev Digest, Vol 44, Issue 6

On Tue, Oct 12, 2010 at 10:09 PM, Stéphane Letz <letz(a)grame.fr> wrote: ): GPU processing for sound via CUDA has already been done a little bit in the windows/mac world: http://www.acusticaudio.net/modules.php?name=Products&file=nebula3 http://www.kvraudio.com/forum/viewtopic.php?t=222978 http://www.kvraudio.com/forum/viewtopic.php?t=240824 http://www.nvidia.com/content/GTC/posters/2010/C01-Exploring-Recognition-Ne… C01 - Exploring Recognition Network Representations for Efficient Speech Inference on the GPU We explore two contending recognition network representations for speech inference engines: the linear lexical model (LLM) and the weighted finite state transducer (WFST) on NVIDIA GTX285 and GTX480 GPUs. We demonstrate that while an inference engine using the simpler LLM representation evaluates 22x more transitions per second than the advanced WFST representation, the simple structure of the LLM representation allows 4.7-6.4x faster evaluation and 53-65x faster operands gathering for each state transition. We illustrate that the performance of a speech inference engine based on the LLM representation is competitive with the WFST representation on highly parallel GPUs. Author: Jike Chong (Parasians, LLC) http://www.nvidia.com/content/GTC/posters/2010/C02-Efficient-Automatic-Spee… C02 - Efficient Automatic Speech Recognition on the GPU Automatic speech recognition (ASR) technology is emerging as a critical component in data analytics for a wealth of media data being generated everyday. ASR-based applications contain fine-grained concurrency that has great potential to be exploited on the GPU. However, the state-of-art ASR algorithm involves a highly parallel graph traversal on an irregular graph with millions of states and arcs, making efficient parallel implementations highly challenging. We present four generalizable techniques including: dynamic data-gather buffer, find-unique, lock-free data structures using atomics, and hybrid global/local task queues. When used together, these techniques can effectively resolve ASR implementation challenges on an NVIDIA GPU. Author: Jike Chong (Parasians, LLC) -- Niels http://nielsmayer.com