The Image Source Method (ISM) is one of the most employed techniques to calculate acoustic Room Impulse Responses (RIRs), however, its computational complexity grows fast with the reverberation time of the room and its computation time can be prohibitive for some applications where a huge number of RIRs are needed. In this paper, we present a new implementation that dramatically improves the computation speed of the ISM by using Graphic Processing Units (GPUs) to parallelize both the simulation of multiple RIRs and the computation of the images inside each RIR. Additional speedups were achieved by exploiting the mixed precision capabilities of the newer GPUs and by using lookup tables. We provide a Python library under GNU license that can be easily used without any knowledge about GPU programming and we show that it is about 100 times faster than other state of the art CPU libraries. It may become a powerful tool for many applications that need to perform a large number of acoustic simulations, such as training machine learning systems for audio signal processing, or for real-time room acoustics simulations for immersive multimedia systems, such as augmented or virtual reality.

图像源方法（ISM）是计算声学房间冲激响应（RIR）的最常用技术之一，但是，其计算复杂度随着房间的混响时间而快速增长，并且对于某些应用场合，其计算时间可能会受到限制。需要大量的RIR。在本文中，我们提出了一种新的实现，该实现通过使用图形处理单元（GPU）并行化多个RIR的仿真和每个RIR内部的图像的计算来极大地提高ISM的计算速度。通过利用较新的GPU的混合精度功能以及使用查找表，可以进一步提高速度。我们提供了GNU许可下的Python库，可以在不了解GPU编程的情况下轻松使用它，并且证明它比其他先进的CPU库快100倍。对于许多需要执行大量声学模拟的应用程序（例如用于音频信号处理的训练机学习系统）或用于沉浸式多媒体系统（例如增强现实或虚拟现实）的实时室内声学模拟，它可能成为功能强大的工具。 。