Most systems for simulating sound propagation in a virtual environment for interactive applications use ray- or path-based models of sound. With these models, the "early" (low-order) specular reflection paths play a key role in defining the "sound" of the environment. However, the wave nature of sound, and the fact that smooth objects are approximated by triangle meshes, pose challenges for creating realistic approximations of the reflection results. Existing methods which produce accurate results are too slow to be used in most interactive applications with dynamic scenes. This paper presents a method for reflections modeling called spatially sampled near-reflective diffraction (SSNRD), based on an existing approximate diffraction model, Volumetric Diffraction and Transmission (VDaT). The SSNRD model addresses the challenges mentioned above, produces results accurate to within 1-2 dB on average compared to edge diffraction, and is fast enough to generate thousands of paths in a few milliseconds in large scenes. This method encompasses scene geometry processing, path trajectory generation, spatial sampling for diffraction modeling, and a small deep neural network (DNN) to produce the final response of each path. All steps of the method are GPU-accelerated, and NVIDIA RTX real-time ray tracing hardware is used for spatial computing tasks beyond just traditional ray tracing.

Fourier transforms whose sizes are powers of two or have only small prime factors have been extensively studied, and optimized implementations are typically memory-bound. However, handling arbitrary transform sizes-which may be prime or have large prime factors-is difficult. Direct discrete Fourier transform (DFT) implementations involve extra computation, while fast Fourier transform (FFT)-style factorized decompositions introduce additional overheads in register use, multiprocessor occupancy, and memory traffic. Tensor Cores are hardware units included in modern GPUs which perform matrix multiply-adds at a much higher throughput than normal GPU floating-point instructions. Because of their higher throughput and better uniformity across sizes, DFT/FFT implementations using Tensor Cores can surpass the performance of existing DFT/FFT implementations for difficult sizes. We present key insights in this approach, including complex number representation, efficient mapping of odd sizes to Tensor Cores (whose dimensions are all powers of 2), and adding a size 2 or size 4 epilogue transform at very low cost. Furthermore, we describe a method for emulating FP32 precision while using lower-precision Tensor Cores to accelerate the computation. For large batch sizes, our fastest Tensor Core implementation per size is at least 10% faster than the state-of-the-art cuFFT library in 49% of supported sizes for FP64 (double) precision and 42% of supported sizes for FP32 precision. The numerical accuracy of the results matches that of cuFFT for FP64 and is degraded by only about 0.3 bits on average for emulated FP32. To our knowledge, this is the first application of Tensor Cores to FFT computation which meets the accuracy and exceeds the speed of the state of the art.