Abstract An in situ visualization system based on the particle-based volume rendering offers a highly scalable and flexible visual analytics environment based on multivariate volume rendering. Although it showed excellent computational performance on the conventional CPU platforms, accelerated computation on the latest many core platforms revealed performance bottlenecks related to a function parser and particles I/O. The function parsers handle multidimensional transfer functions, but conventional implementation was not optimized for wide SIMD widths. The I/O bottleneck comes from the latency of output of particle data files. In this paper, we develop a new SIMD-aware function parser and an asynchronous data I/O method based on task-based thread parallelization. The particle generation process is optimized by loop blocking to take advantage of the new function parser. Numerical experiments on the Oakforest-PACS, which consists of 8208 Intel Xeon Phi7250 (Knights Landing) processors, demonstrate an order of magnitude speedup with keeping improved strong scaling up to $$\sim 100\,\hbox {k}$$ ∼ 100 k cores. Graphic abstract

中文翻译：

---

使用 SIMD 感知函数解析器和异步数据 I/O 改进交互式远程原位可视化

摘要 基于粒子体绘制的原位可视化系统提供了一个基于多元体绘制的高度可扩展和灵活的可视化分析环境。尽管它在传统 CPU 平台上表现出出色的计算性能，但在最新的多核平台上的加速计算揭示了与函数解析器和粒子 I/O 相关的性能瓶颈。函数解析器处理多维传递函数，但传统实现并未针对宽 SIMD 宽度进行优化。I/O 瓶颈来自粒子数据文件的输出延迟。在本文中，我们开发了一种新的 SIMD 感知函数解析器和一种基于基于任务的线程并行化的异步数据 I/O 方法。粒子生成过程通过循环阻塞进行了优化，以利用新的函数解析器。Oakforest-PACS 上的数值实验由 8208 个英特尔至强 Phi7250（Knights Landing）处理器组成，证明了一个数量级的加速，同时保持改进的强大扩展到 $$\sim 100\,\hbox {k}$$ ∼ 100 k核。图形摘要