自动光线追踪云卸载在OPENMP

M. Mortatti, H. Yviquel, G. Araújo
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引用次数: 1

摘要

从3D场景中渲染图像需要大量的计算,随着场景的复杂性(例如物体和光源的数量)呈指数增长。随着对高清晰度内容的需求不断增加,3D设计师需要使用高性能的计算机系统来保持可接受的渲染时间。由于拥有计算机集群是昂贵的,设计人员通常直接从云服务提供商(例如AWS和Azure)租用计算能力。然而,尽管许多云提供商已经提出了专门的渲染服务,但将它们集成到建模软件的标准工作流中可能会成为一项复杂而繁琐的任务。它通常需要从设计软件导出项目,处理来自不同云的各种访问控制机制以上传项目,并通过命令行远程执行呈现。将计算卸载到云端是一种可以大大简化此类任务的技术。为了实现这一目标,本文使用了openmp4的扩展。X以消除与最终用户的任何主要交互,同时最大限度地降低云集成的复杂性并优化设计工作流。它将这种方法应用于光线追踪应用程序,这是专业3D建模软件(例如Blender)使用的引擎的简化版本。它自动将渲染过程从用户计算机卸载到Microsoft Azure云中的计算机集群,在计算结束后将生成的图像带回来并直接显示在用户计算机的屏幕上,从而提供透明的编程模型和优于本地执行的良好加速。
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Automatic Ray-Tracer Cloud Offloading in OPENMP
Rendering an image from a 3D scene requires a large amount of computation which grows exponentially with the complexity of the scene (e.g. number of objects and light sources). With the increasing demand of high definition content, 3D designers need to use high-performance computer systems to keep the rendering time acceptable. Since owning computer clusters is expensive, designers usually rent computing power directly from cloud service providers (e.g, AWS and Azure). However, even though many cloud providers already propose dedicated rendering services, integrating them within the standard workflow of modeling softwares can become a complex and cumbersome task. It typically requires exporting the project from the design software, dealing with various access control mechanisms from different clouds to upload the project, and executing the rendering remotely through command-line. Offloading computation to the cloud is a technique which can considerably simplify such tasks. To achieve that, this paper uses an extension of openMP 4.X to eliminate any major interactions with the end-user, while minimizing the complexity of cloud integration and optimizing the design workflow. It applies such approach to a ray-tracing application, a simplified version of the engines used by professional 3D modeling software (e.g. Blender). It automatically offloads the rendering process from the user computer to computer cluster within the Microsoft Azure cloud, brings the resulting images back after the computation ends and displays them directly on the screen of the user computer, thus providing a transparent programming model and good speed-ups over local execution.
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