J. Vachálek, M. Melicher, Pavol Vasek, Juraj Slovak
{"title":"利用MATLAB进行数值加速数据处理,以满足专家系统的需要","authors":"J. Vachálek, M. Melicher, Pavol Vasek, Juraj Slovak","doi":"10.1109/CYBERI.2018.8337543","DOIUrl":null,"url":null,"abstract":"The article deals with alternative techniques of accelerating numerical computations. Working with sets of local models requires fast processing of data in form of matrices. These data objects consist of input matrices with different dimensions and their mutual multiplication, division or other basic mathematical operations. The most suitable accelerating technique for these operations is parallelized computation. The technique can be applied in the form of using a large number of simple mathematical coprocessors which are found in modern graphics cards, so called general-purpose computation on graphics processing units, or connecting multiple computers to a distributed computing network called high performance computing cluster. The techniques are later discussed in the article. The article also contains practical comparison of both techniques and evaluation of the possibilities of their application.","PeriodicalId":6534,"journal":{"name":"2018 Cybernetics & Informatics (K&I)","volume":"41 1","pages":"1-5"},"PeriodicalIF":0.0000,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Numerical acceleration of data processing using MATLAB for the needs of expert systems\",\"authors\":\"J. Vachálek, M. Melicher, Pavol Vasek, Juraj Slovak\",\"doi\":\"10.1109/CYBERI.2018.8337543\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The article deals with alternative techniques of accelerating numerical computations. Working with sets of local models requires fast processing of data in form of matrices. These data objects consist of input matrices with different dimensions and their mutual multiplication, division or other basic mathematical operations. The most suitable accelerating technique for these operations is parallelized computation. The technique can be applied in the form of using a large number of simple mathematical coprocessors which are found in modern graphics cards, so called general-purpose computation on graphics processing units, or connecting multiple computers to a distributed computing network called high performance computing cluster. The techniques are later discussed in the article. The article also contains practical comparison of both techniques and evaluation of the possibilities of their application.\",\"PeriodicalId\":6534,\"journal\":{\"name\":\"2018 Cybernetics & Informatics (K&I)\",\"volume\":\"41 1\",\"pages\":\"1-5\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 Cybernetics & Informatics (K&I)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CYBERI.2018.8337543\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 Cybernetics & Informatics (K&I)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CYBERI.2018.8337543","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Numerical acceleration of data processing using MATLAB for the needs of expert systems
The article deals with alternative techniques of accelerating numerical computations. Working with sets of local models requires fast processing of data in form of matrices. These data objects consist of input matrices with different dimensions and their mutual multiplication, division or other basic mathematical operations. The most suitable accelerating technique for these operations is parallelized computation. The technique can be applied in the form of using a large number of simple mathematical coprocessors which are found in modern graphics cards, so called general-purpose computation on graphics processing units, or connecting multiple computers to a distributed computing network called high performance computing cluster. The techniques are later discussed in the article. The article also contains practical comparison of both techniques and evaluation of the possibilities of their application.