Intel^(■) Math Kernel Library PARDISO* forIntel^(■) Xeon Phi^(TM) Manycore Coprocessor

The paper describes an efficient direct method to solve an equation Ax = b, where A is a sparse matrix, on the Intel®Xeon PhiTM coprocessor. The main challenge for such a system is how to engage all available threads (about 240) and how to reduce OpenMP* synchronization overhead, which is very expensive for hundreds of threads. The method consists of decomposing A into a product of lower-triangular, diagonal, and upper triangular matrices followed by solves of the resulting three subsystems. The main idea is based on the hybrid parallel algorithm used in the Intel®Math Kernel Library Parallel Direct Sparse Solver for Clusters [1]. Our implementation exploits a static scheduling algorithm during the factorization step to reduce OpenMP synchronization overhead. To effectively engage all available threads, a three-level approach of parallelization is used. Furthermore, we demonstrate that our implementation can perform up to 100 times better on factorization step and up to 65 times better in terms of overall performance on the 240 threads of the Intel®Xeon PhiTM coprocessor.

基于光谱知识库的TM影像冬小麦条锈病监测研究

基于高光谱信息的冬小麦条锈病严重度反演模型通常不能直接应用在宽波段卫星影像上,而拥有高光谱波段信息的航空遥感影像又因数据尺度小、成本高难以应用于大规模监测。文章提出一种通过构建冬小麦条锈病光谱知识库,利用TM影像实现病情识别和监测的方法。该方法以包含各种不同病情严重度的试验田的三幅小麦关键生育期PHI航空遥感影像为媒介,利用病情指数DI的经验反演模型和基于波谱响应函数的TM波段模拟,建立DI和TM波段模拟反射率间的光谱知识库。在此基础上,通过马氏距离法和光谱角度填图(SAM)法将待检象元的光谱信息与光谱知识库进行匹配分析从而实现对病情识别和监测。监测的精度利用模拟TM象元进行评价,识别的效果利用TM影像象元进行检验。结果表明,该方法在一定生育期范围内具有较佳的监测精度和识别效果。其中,使用模拟TM象元在小麦灌浆期精度最佳,评价的R2达到0.93,乳熟期次之,拔节期最差,基本不能用于监测。使用TM影像象元在灌浆期和乳熟期可较好地识别染病象元,在拔节期无法有效识别染病象元。匹配方法马氏距离法略优于光谱角度匹配法。