1. Acceleration on many-cores CPUs and GPUs Dinesh Manocha Lauri Savioja

2. Leveraging multi-core acceleration  Available on both CPUs and GPUs  Can be used for signal processing, numerical calculations and geometry processing

3. Frustum Tracing Pipeline Frustum Triangle Intersection

4. Frustum tracing using multiple cores  Multiple frusta and rays can be easily traced in parallel  Frusta tracing: specular reflections and edge diffractions  Ray tracing: diffuse reflections  Can scale linearly with the number of cores (tested up to 16 cores)

5. Frustum Tracing Pipeline Frustum Triangle Intersection

6. Frustum Tracing Results (7 cores) Theater 54 ∆s Factory 174 ∆s Game 14K ∆s Sibenik 71K ∆s City 72K ∆s SodaHa ll 1.5M ∆s diffraction NO YES #frusta56K40K206K198K80K108K time (msec) 3327273598206373

7. Frustum Tracing Results (7 cores) Interactive geometric propagation on complex scenes [Chandak et al. 2008]

8. Scaling of FastV (Scaling with #cores) Fastest, accurate geometric propagation algorithm [Chandak et al. 2009]

9. Numerical Acoustics with Adaptive Rectangular Decomposition on the GPU Nikunj Raghuvanshi +, Brandon Lloyd*, Naga K. Govindaraju*, Ming C. Lin + + Department of Computer Science, UNC Chapel Hill * Microsoft Corporation

10. Rectangular Decomposition  Numerical Acoustics can be solved very efficiently on a rectangular domain  Decompose complex domains into rectangles

11. Leveraging GPU for acoustics  Solution of Wave Equation within each rectangle can be done using a Discrete Cosine Transform (DCT)  DCTs can be done using FFT  Use an efficient FFT implementation on the GPU  Govindaraju, N. K., Lloyd, B., Dotsenko, Y., Smith, B., and Manferdelli, J. 2008. High performance discrete Fourier transforms on graphics processors. In Proceedings of the 2008 ACM/IEEE Conference on Supercomputing

12. FFT on the GPU

13. Performance Scene Name Volume (m 3 ) Time: FDTD (CPU) Time: Our Technique (GPU) Speedup Corridor375365 min4 min~ 90x House1,2752718 min13 min~ 200x Cathedral13,650~1 week (projected) 30 min~ 300 x

14.  Rectangular decomposition leverages GPU FFT combined with algorithmic improvements leading to ~100x improvement in performance for numerical acoustics Conclusion

15. GPU-based occlusion and scattering  Use techniques similar to (reflective) shadow mapping  Compute qualitative occlusion or more physically grounded surface integral

16. GPU audio processing  GPUs can be used for audio processing and filtering

17. Case: Real-time acoustic radiance transfer

19. Case continued  More information in: S. Siltanen, T. Lokki, and L. Savioja, `Frequency domain acoustic radiance transfer for real-time auralization,' Acta Acustica united with Acustica, vol. 95, no. 1, pp. 106-117, 2009.

20. Conclusions  Multi-core CPUs and many-core GPUs can be used to accelerate sound rendering  It is possible to develop interactive sound rendering systems by exploiting the commodity parallel hardware