Multi-Core Platforms for Beamforming and Wave Field Synthesis
Multi-Core Platforms for Beamforming and Wave Field Synthesis
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DOI：
10.1109/tmm.2010.2098397
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发表时间：
2011-04
影响因子：
7.3
通讯作者：
D. Theodoropoulos;G. Kuzmanov;G. Gaydadjiev
中科院分区：
文献类型：
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作者：
D. Theodoropoulos;G. Kuzmanov;G. Gaydadjiev
沉浸式音频技术广泛用于构建实验和商业音频系统。然而，它们中的大多数基于标准PC，这引入了性能限制和过度功耗。为了解决这些缺点，我们探讨了两种沉浸式音频技术的实现前景：波束形成（BF）和波场合成（WFS）。我们的目标是两个流行的多核平台，即图形处理器单元（GPU）和现场可编程门阵列（FPGA）。我们确定了这两个应用程序的计算最密集的部分，并采用CUDA环境将它们映射到Quadro FX 1700，GeForce 8600 GT，GTX 275和GTX 460 GPU上。此外，我们为这两种算法设计了定制的多核硬件加速器，并将其映射到Virtex6 FPGA上。GPU和FPGA的实现进行了比较，在3.0 GHz的Core2 Duo上运行的OpenMP注释的软件。实验结果表明，中档GPU处理数据的能力与BF的Core2 Duo相当，而WFS的速度约为两倍。然而，高端GPU和FPGA解决方案为BF提供了一个数量级更好的性能，为WFS提供了大约两个数量级更好的性能。最终，单芯片GPU和FPGA实现可以提供更节能的解决方案，因为它们可以驱动比基于PC的方法更复杂的麦克风和扬声器设置。
Immersive-Audio technologies are widely used to build experimental and commercial audio systems. However, most of them are based on standard PCs, which introduce performance limitations and excessive power consumption. To address these drawbacks, we explore the implementation prospectives of two Immersive-Audio technologies: the beamforming (BF) and the wave field synthesis (WFS). We target two popular multi-core platforms, namely graphic processor units (GPUs) and field programmable gate arrays (FPGAs). We identify the most computationally intensive parts of both applications and employ the CUDA environment to map them onto a Quadro FX1700, a GeForce 8600GT, a GTX275, and a GTX460 GPU. Furthermore, we design our custom multi-core hardware accelerators for both algorithms and map them onto Virtex6 FPGAs. Both GPU and FPGA implementations are compared against OpenMP-annotated software running on a Core2 Duo at 3.0 GHz. Experimental results suggest that middle-range GPUs process data equally well as the Core2 Duo for the BF, and approximately two times faster for the WFS. However, high-end GPU and FPGA solutions provide an order of magnitude better performance for BF, and approximately two orders of magnitude better performance for WFS than the Core2 Duo. Ultimately, single-chip GPU and FPGA implementations can provide more power-effective solutions, since they can drive more complex microphone and loudspeaker setups than PC-based approaches.