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Maximum principle for the weak solutions of the Cauchy problem for the fourth‐order hyperbolic equations 四阶双曲方程考奇问题弱解的最大原则
PAMM Pub Date : 2023-08-07 DOI: 10.1002/pamm.202300226
K. Buryachenko
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引用次数: 0
Diagonally‐Addressed Matrix Nicknack: How to improve SpMV performance 对角寻址矩阵昵称:如何提高 SpMV 性能
PAMM Pub Date : 2023-07-12 DOI: 10.1002/pamm.202300228
J. Saak, J. Schulze
{"title":"Diagonally‐Addressed Matrix Nicknack: How to improve SpMV performance","authors":"J. Saak, J. Schulze","doi":"10.1002/pamm.202300228","DOIUrl":"https://doi.org/10.1002/pamm.202300228","url":null,"abstract":"We suggest a technique to reduce the storage size of sparse matrices at no loss of information. We call this technique Diagonally‐Addressed (DA) storage. It exploits the typically low matrix bandwidth of matrices arising in applications. For memory‐bound algorithms, this traffic reduction has direct benefits for both uni‐precision and multi‐precision algorithms. In particular, we demonstrate how to apply DA storage to the Compressed Sparse Rows (CSR) format and compare the performance in computing the Sparse Matrix Vector (SpMV) product, which is a basic building block of many iterative algorithms. We investigate 1367 matrices from the SuiteSparse Matrix Collection fitting into the CSR format using signed 32 bit indices. More than 95% of these matrices fit into the DA‐CSR format using 16 bit column indices, potentially after Reverse Cuthill‐McKee (RCM) reordering. Using IEEE 754 double$mathtt {double}$ precision scalars, we observe a performance uplift of 11% (single‐threaded) or 17.5% (multithreaded) on average when the traffic exceeds the size of the last‐level CPU cache. The predicted uplift in this scenario is 20%. For traffic within the CPU's combined level 2 and level 3 caches, the multithreaded performance uplift is over 40% for a few test matrices.","PeriodicalId":510616,"journal":{"name":"PAMM","volume":"192 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139360257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Applied Bayesian structural health monitoring: Inclinometer data anomaly detection and forecasting 应用贝叶斯结构健康监测:倾斜仪数据异常检测和预测
PAMM Pub Date : 2023-07-01 DOI: 10.1002/pamm.202300132
David K. E. Green, A. Jaspan
{"title":"Applied Bayesian structural health monitoring: Inclinometer data anomaly detection and forecasting","authors":"David K. E. Green, A. Jaspan","doi":"10.1002/pamm.202300132","DOIUrl":"https://doi.org/10.1002/pamm.202300132","url":null,"abstract":"Inclinometer probes are devices that can be used to measure deformations within earthwork slopes. This paper demonstrates a novel application of Bayesian techniques to real‐world inclinometer data, providing both anomaly detection and forecasting. Specifically, this paper details an analysis of data collected from across the entire UK rail network.","PeriodicalId":510616,"journal":{"name":"PAMM","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139364771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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