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Enabling heterogeneous ray‐tracing acceleration in edge/cloud architectures
Concurrency and Computation: Practice and Experience ( IF 2 ) Pub Date : 2020-07-16 , DOI: 10.1002/cpe.5822
Adrianno A. Sampaio 1 , Alexandre C. Sena 1 , Alexandre S. Nery 2
Affiliation  

The ray‐tracing algorithm is very costly regarding time complexity and while many techniques have been conceived over the years with the purpose of accelerating its execution, one stands out: parallelism exploitation of ray‐triangle intersection operations. In this sense, field‐programmable gate arrays (FPGAs) have plenty resources to run specialized accelerators that execute multiple operations in parallel. Moreover, modern FPGAs are embedded with multiprocessor systems‐on‐chip based on ARM architecture, which can be used simultaneously with the FPGA programmable logic to further accelerate the application execution. In this work, we present and analyze a reconfigurable accelerator for ray‐tracing specialized in computing ray‐triangle intersections at the network edge of a heterogeneous cloud computing environment. The accelerator is specified using Xilinx high‐level synthesis and is implemented in a Xilinx Zynq FPGA (XC7Z020‐1CLG400C). We also present an execution model which enables the exploitation of the available computing elements of the heterogeneous system: ARM Cortex‐A53, FPGA programmable logic, and cloud machines. Experimental performance and synthesis results show that the heterogeneous system can efficiently render a simplified version of the Stanford Bunny model when using the hardware accelerator with up to six instances of a ray‐triangle intersection unit together with the other computing resources.

中文翻译:

在边缘/云架构中启用异构光线跟踪加速

射线追踪算法在时间复杂度方面非常昂贵,尽管多年来为了加速其执行而构想了许多技术,但其中一个引人注目:利用射线三角相交操作的并行性。从这个意义上讲,现场可编程门阵列(FPGA)具有足够的资源来运行专门的加速器,这些加速器可以并行执行多个操作。此外,现代FPGA嵌入了基于ARM体系结构的多处理器片上系统,可以与FPGA可编程逻辑同时使用,以进一步加快应用程序的执行速度。在这项工作中,我们介绍并分析了可重配置的加速器,该加速器专门用于在异构云计算环境的网络边缘计算射线三角形交叉点的射线跟踪。该加速器是使用Xilinx高级综合指定的,并在Xilinx Zynq FPGA(XC7Z020-1CLG400C)中实现。我们还提供了一个执行模型,该模型可以利用异构系统的可用计算元素:ARM Cortex-A53,FPGA可编程逻辑和云计算机。实验性能和综合结果表明,当将硬件加速器与多达六个射线三角相交单元的实例以及其他计算资源一起使用时,异构系统可以有效地呈现Stanford Bunny模型的简化版本。FPGA可编程逻辑和云机器。实验性能和综合结果表明,当将硬件加速器与多达六个射线三角相交单元的实例以及其他计算资源一起使用时,异构系统可以有效地呈现Stanford Bunny模型的简化版本。FPGA可编程逻辑和云机器。实验性能和综合结果表明,当将硬件加速器与多达六个射线三角相交单元的实例以及其他计算资源一起使用时,异构系统可以有效地呈现Stanford Bunny模型的简化版本。
更新日期:2020-07-16
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