Pub Date : 1900-01-01DOI: 10.21655/ijsi.1673-7288.00291
Wei Zhang, Hang Zhou, Jiayin Chen, Xi Chen, Zhiyi Ma
{"title":"Database Translation Mechanism: Generating Data Dictionary for Relational Database","authors":"Wei Zhang, Hang Zhou, Jiayin Chen, Xi Chen, Zhiyi Ma","doi":"10.21655/ijsi.1673-7288.00291","DOIUrl":"https://doi.org/10.21655/ijsi.1673-7288.00291","url":null,"abstract":"","PeriodicalId":218849,"journal":{"name":"Int. J. Softw. Informatics","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132452595","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}
Pub Date : 1900-01-01DOI: 10.21655/ijsi.1673-7288.00249
Hongjun Zhang, Yanjun Wu, Heng Zhang, Libo Zhang
Hash tables, as a type of data indexing structure that provides efficient data access based on key values, are widely used in various computer applications, especially in system software, databases, and high-performance computing field that requires extremely high performance. In network, cloud computing and IoT services, hash tables have become the core system components of cache systems. However, with the large-scale increase in the amount of large-scale data, performance bottlenecks have gradually emerged in systems designed with a multi-core CPU as the core of the hash table structure. There is an urgent need to further improve the high performance and scalability of the hash tables. With the increasing popularity of general-purpose Graphic Processing Units (GPUs) and the substantial improvement of hardware computing capabilities and concurrency performance, various types of system software tasks with parallel computing as the core have been optimized on the GPU and have achieved considerable performance improvements. Due to the sparseness and randomness, using the existing parallel structure of the hash tables directly on the GPUs will inevitably bring high-frequency memory access and frequent bus data transmission, which affects the performance of the hash tables on the GPUs. This study focuses on the analysis of memory access, hit ratio, and index overhead of hash table indexes in the cache system. The hybrid access cache indexing framework CCHT (Cache Cuckoo Hash Table) adapted to GPU is proposed and provided. The cache strategy suitable to different requirements of hit ratios and index overheads allows concurrent execution of write and query operations, maximizing the use of the computing performance and concurrency characteristics of GPU hardware, reducing memory access and bus transferring overhead. Through GPU hardware implementation and experimental verification, CCHT is shown to have better performance than other cache indexing hash tables while ensuring cache hit ratios.
{"title":"Hybrid Access Cache Indexing Framework Adapted to GPU","authors":"Hongjun Zhang, Yanjun Wu, Heng Zhang, Libo Zhang","doi":"10.21655/ijsi.1673-7288.00249","DOIUrl":"https://doi.org/10.21655/ijsi.1673-7288.00249","url":null,"abstract":"Hash tables, as a type of data indexing structure that provides efficient data access based on key values, are widely used in various computer applications, especially in system software, databases, and high-performance computing field that requires extremely high performance. In network, cloud computing and IoT services, hash tables have become the core system components of cache systems. However, with the large-scale increase in the amount of large-scale data, performance bottlenecks have gradually emerged in systems designed with a multi-core CPU as the core of the hash table structure. There is an urgent need to further improve the high performance and scalability of the hash tables. With the increasing popularity of general-purpose Graphic Processing Units (GPUs) and the substantial improvement of hardware computing capabilities and concurrency performance, various types of system software tasks with parallel computing as the core have been optimized on the GPU and have achieved considerable performance improvements. Due to the sparseness and randomness, using the existing parallel structure of the hash tables directly on the GPUs will inevitably bring high-frequency memory access and frequent bus data transmission, which affects the performance of the hash tables on the GPUs. This study focuses on the analysis of memory access, hit ratio, and index overhead of hash table indexes in the cache system. The hybrid access cache indexing framework CCHT (Cache Cuckoo Hash Table) adapted to GPU is proposed and provided. The cache strategy suitable to different requirements of hit ratios and index overheads allows concurrent execution of write and query operations, maximizing the use of the computing performance and concurrency characteristics of GPU hardware, reducing memory access and bus transferring overhead. Through GPU hardware implementation and experimental verification, CCHT is shown to have better performance than other cache indexing hash tables while ensuring cache hit ratios.","PeriodicalId":218849,"journal":{"name":"Int. J. Softw. Informatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124460029","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}
Pub Date : 1900-01-01DOI: 10.21655/ijsi.1673-7288.00271
Yanfang Liu, Wenbin Li, Yang Gao
{"title":"Confidence-weighted Learning for Feature Evolution","authors":"Yanfang Liu, Wenbin Li, Yang Gao","doi":"10.21655/ijsi.1673-7288.00271","DOIUrl":"https://doi.org/10.21655/ijsi.1673-7288.00271","url":null,"abstract":"","PeriodicalId":218849,"journal":{"name":"Int. J. Softw. Informatics","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131661820","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}
Pub Date : 1900-01-01DOI: 10.1007/978-3-642-37395-4_1
H. D. Rombach
{"title":"Empirical Software Engineering Models: Can They Become the Equivalent of Physical Laws in Traditional Engineering?","authors":"H. D. Rombach","doi":"10.1007/978-3-642-37395-4_1","DOIUrl":"https://doi.org/10.1007/978-3-642-37395-4_1","url":null,"abstract":"","PeriodicalId":218849,"journal":{"name":"Int. J. Softw. Informatics","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114334230","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}
Pub Date : 1900-01-01DOI: 10.21655/ijsi.1673-7288.00266
Peng Fei Zhu, Wanying Zhang, Yu Wang, Qinghua Hu
{"title":"Multi-granularity Inter-class Correlation Based Contrastive Learning for Open Set Recognition","authors":"Peng Fei Zhu, Wanying Zhang, Yu Wang, Qinghua Hu","doi":"10.21655/ijsi.1673-7288.00266","DOIUrl":"https://doi.org/10.21655/ijsi.1673-7288.00266","url":null,"abstract":"","PeriodicalId":218849,"journal":{"name":"Int. J. Softw. Informatics","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123125803","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}
Pub Date : 1900-01-01DOI: 10.21655/ijsi.1673-7288.00268
Liujuan Cao, Huafeng Kuang, Hong Liu, Yan Wang, Baochang Zhang, Feiyue Huang, Yongjian Wu, Rongrong Ji
{"title":"Towards Robust Adversarial Training via Dual-label Supervised and Geometry Constraint","authors":"Liujuan Cao, Huafeng Kuang, Hong Liu, Yan Wang, Baochang Zhang, Feiyue Huang, Yongjian Wu, Rongrong Ji","doi":"10.21655/ijsi.1673-7288.00268","DOIUrl":"https://doi.org/10.21655/ijsi.1673-7288.00268","url":null,"abstract":"","PeriodicalId":218849,"journal":{"name":"Int. J. Softw. Informatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129180992","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}
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