Image- and data-parallel rendering across multiple nodes on high-performance computing systems is widely used in visualization to provide higher frame rates, support large data sets, and render data in situ. Specifically for in situ visualization, reducing bottlenecks incurred by the visualization and compositing is of key concern to reduce the overall simulation runtime. Moreover, prior algorithms have been designed to support either image- or data-parallel rendering and impose restrictions on the data distribution, requiring different implementations for each configuration. In this paper, we introduce the Distributed FrameBuffer, an asynchronous image-processing framework for multi-node rendering. We demonstrate that our approach achieves performance superior to the state of the art for common use cases, while providing the flexibility to support a wide range of parallel rendering algorithms and data distributions. By building on this framework, we extend the open-source ray tracing library OSPRay with a data-distributed API, enabling its use in data-distributed and in situ visualization applications.

翻译：在高性能计算系统中，跨多个节点进行图像与数据并行渲染广泛应用于可视化领域，以提供更高帧率、支持大数据集并实现原位渲染。特别是在原位可视化中，减少可视化与合成环节的瓶颈对于降低整体模拟运行时间至关重要。此外，现有算法被设计为支持图像并行或数据并行渲染，并对数据分布施加限制，导致不同配置需要采用不同的实现方式。本文提出分布式帧缓冲器——一种用于多节点渲染的异步图像处理框架。我们证明，该方法在常见用例中实现了优于现有技术的性能，同时具备灵活性，能支持广泛的并行渲染算法与数据分布。基于此框架，我们扩展了开源光线追踪库OSPRay的数据分布API，使其能够应用于数据分布式与原位可视化场景。