Remote sensing analysis is a dominant yet time-consuming part of geospatial applications. The performance can be optimized based on distributed computing, but current systems still face significant challenges. Firstly, the spatial characteristics of remote sensing data lead to an uneven distribution of computational intensity (CIT), which characterizes computing loads, including computation and IO, in different spatial domains. Secondly, it is hard to achieve load-balancing without introducing new computational costs, thus increasing the CIT and reducing the overall performance. Therefore, resolving CIT by decreasing and balancing it is an important research issue for distributed remote sensing computing. This paper proposes LBM-RS, an efficient distributed framework based on load-balancing matrix computing for remote sensing. It implements remote sensing applications based on the distributed matrix, representing the algorithms with a matrix computation and constructing multi-dimensional spatial domains to model computational costs for matrix operation tasks. It resolves the CIT with the minimum computation load and dynamic spatial domain decomposition strategy to support global load balancing. We also exploit the IO efficiency from the task staging strategy and the cache-aware memory structure for remote sensing data. In this way, it can reduce the bandwidth burden and memory access frequency, thus decreasing the overall CIT. Finally, we evaluate the proposed approach on both real and synthetic datasets, and the results demonstrate significant advantages in computation and communication efficiency compared to the benchmarks.

中文翻译：

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用于解决遥感计算强度的高效分布式矩阵


遥感分析是地理空间应用中占主导地位但耗时的部分。可以基于分布式计算优化性能，但当前系统仍面临重大挑战。首先，遥感数据的空间特征导致计算强度 （CIT） 分布不均匀，CIT 表征了不同空间域的计算负载，包括计算和 IO;其次，如果不引入新的计算成本，就很难实现负载均衡，从而增加了 CIT 并降低了整体性能。因此，通过降简和平衡来解决 CIT 是分布式遥感计算的重要研究问题。本文提出了 LBM-RS，一种基于负载均衡矩阵计算的高效分布式框架，用于遥感。它基于分布式矩阵实现遥感应用，用矩阵计算表示算法，并构建多维空间域来模拟矩阵运算任务的计算成本。它以最小的计算负载和动态空间域分解策略解析 CIT，以支持全局负载均衡。我们还利用了任务暂存策略的 IO 效率和遥感数据的缓存感知内存结构。这样可以减少带宽负担和内存访问频率，从而降低整体 CIT。最后，我们在真实和合成数据集上评估了所提出的方法，结果表明，与基准相比，该方法在计算和通信效率方面具有显著优势。