{"title":"计算非点阵肿瘤生长模型的性能分析","authors":"D. Kiss, A. Lovrics","doi":"10.1109/NC.2017.8263270","DOIUrl":null,"url":null,"abstract":"This paper presents a performance analysis of a three dimensional off-lattice agent based model of tumor growth. The model uses short range potential functions to handle mechanical interactions. This computationally intensive process can be accelerated by various reduction techniques. We focus on two different methods, a spatial decomposition which is extremely useful for simulating low density suspended cell cultures, and a heuristic to reduce computations for simulating solid tumors. We show that these techniques can significantly improve simulation performance in appropriate situations.","PeriodicalId":140536,"journal":{"name":"2017 IEEE 30th Neumann Colloquium (NC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Performance analysis of a computational off-lattice tumor growth model\",\"authors\":\"D. Kiss, A. Lovrics\",\"doi\":\"10.1109/NC.2017.8263270\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a performance analysis of a three dimensional off-lattice agent based model of tumor growth. The model uses short range potential functions to handle mechanical interactions. This computationally intensive process can be accelerated by various reduction techniques. We focus on two different methods, a spatial decomposition which is extremely useful for simulating low density suspended cell cultures, and a heuristic to reduce computations for simulating solid tumors. We show that these techniques can significantly improve simulation performance in appropriate situations.\",\"PeriodicalId\":140536,\"journal\":{\"name\":\"2017 IEEE 30th Neumann Colloquium (NC)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE 30th Neumann Colloquium (NC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NC.2017.8263270\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 30th Neumann Colloquium (NC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NC.2017.8263270","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Performance analysis of a computational off-lattice tumor growth model
This paper presents a performance analysis of a three dimensional off-lattice agent based model of tumor growth. The model uses short range potential functions to handle mechanical interactions. This computationally intensive process can be accelerated by various reduction techniques. We focus on two different methods, a spatial decomposition which is extremely useful for simulating low density suspended cell cultures, and a heuristic to reduce computations for simulating solid tumors. We show that these techniques can significantly improve simulation performance in appropriate situations.