Scalable soft real-time supervisor for tomographic AO
Scalable soft real-time supervisor for tomographic AO
复制标题
用于断层扫描 AO 的可扩展软实时监控器
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
David E. Keyes
中科院分区：
文献类型：
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作者：
N. Doucet;N. Doucet;Ronald Kriemann;Eric Gendron;D. Gratadour;H. Ltaief;David E. Keyes
下一代超大望远镜（ELT）的AO断层成像的实现具有挑战性，因为这种系统的自由度非常大，特别是当涉及到断层成像重建器计算时，由于其尺寸。通过管理器模块计算该矩阵需要在共享或分布式存储器系统上利用高性能计算技术，以符合断层摄影AO系统的规范，该规范规定了几分钟量级的更新速率。在Green-Flash项目的范围内，我们正在探索几种方法来优化这种软实时监控管道的执行。这包括低秩技术，以减少计算负荷。我们已经测试了几种压缩方案，以优化线性代数涉及的层析重建，以及计算的协方差矩阵涉及这个过程。在本文中，我们提出了可扩展和便携式管道，我们已经开发，以解决这些问题。性能方面的解决方案和可扩展性的报告。此外，低秩算法的情况下强调，试图解决断层重建器的计算挑战的监督模块，并在实时数据管道的水平，以减少计算负载（因此，整体RTC系统延迟）的方式。
Implementations of AO tomography for the next generation of Extremely Large Telescopes (ELTs) is challenging because of the extremely large number of degrees of freedom of such systems, in particular when it comes to the tomographic reconstructor computation, due to its size. The computation of this matrix, via the supervisor module, requires leveraging high performance computing techniques, on shared or distributed memory systems, to comply with the specifications of tomographic AO systems, which prescribe an update rate of the order of few minutes. In the scope of the Green-Flash project, we are exploring several approaches to optimize the execution of this soft real-time supervision pipeline. This includes low-rank techniques to reduce the computational load. We have tested several compression schemes to optimize the linear algebra involved in the tomographic reconstructor as well as the computation of the covariance matrices involved in this process. We present, in this paper, the scalable and portable pipeline we have developed to address these issues. Performance in terms of time to solution and scalability are reported. Additionally, the case of low-rank algorithms is stressed as both an attempt to address the computation challenge of the tomographic reconstructor for the supervisor module, and a way to reduce the computational load (hence the overall RTC system latency) at the level of the real-time data pipeline.