{"title":"gpu上分子力学的迭代诱导偶极子计算","authors":"F. Pratas, R. Mata, L. Sousa","doi":"10.1145/1735688.1735708","DOIUrl":null,"url":null,"abstract":"In this work, we present a first step towards the efficient implementation of polarizable molecular mechanics force fields with GPU acceleration. The computational bottleneck of such applications is found in the treatment of electrostatics, where higher-order multipoles and a self-consistent treatment of polarization effects are needed. We have coded these sections, for the case of a non-periodic simulation, with the CUDA programming model. Results show a speedup factor of 21 for a single precision GPU implementation, when comparing to the serial CPU version. A discussion of the optimization and parameterization steps is included. Comparison between different graphic cards and a shared memory parallel CPU implementation is also given. The current work demonstrates the potential usefulness of GPU programming in accelerating this field of applications.","PeriodicalId":381071,"journal":{"name":"GPGPU-3","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Iterative induced dipoles computation for molecular mechanics on GPUs\",\"authors\":\"F. Pratas, R. Mata, L. Sousa\",\"doi\":\"10.1145/1735688.1735708\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this work, we present a first step towards the efficient implementation of polarizable molecular mechanics force fields with GPU acceleration. The computational bottleneck of such applications is found in the treatment of electrostatics, where higher-order multipoles and a self-consistent treatment of polarization effects are needed. We have coded these sections, for the case of a non-periodic simulation, with the CUDA programming model. Results show a speedup factor of 21 for a single precision GPU implementation, when comparing to the serial CPU version. A discussion of the optimization and parameterization steps is included. Comparison between different graphic cards and a shared memory parallel CPU implementation is also given. The current work demonstrates the potential usefulness of GPU programming in accelerating this field of applications.\",\"PeriodicalId\":381071,\"journal\":{\"name\":\"GPGPU-3\",\"volume\":\"38 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-03-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"GPGPU-3\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1735688.1735708\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"GPGPU-3","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1735688.1735708","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Iterative induced dipoles computation for molecular mechanics on GPUs
In this work, we present a first step towards the efficient implementation of polarizable molecular mechanics force fields with GPU acceleration. The computational bottleneck of such applications is found in the treatment of electrostatics, where higher-order multipoles and a self-consistent treatment of polarization effects are needed. We have coded these sections, for the case of a non-periodic simulation, with the CUDA programming model. Results show a speedup factor of 21 for a single precision GPU implementation, when comparing to the serial CPU version. A discussion of the optimization and parameterization steps is included. Comparison between different graphic cards and a shared memory parallel CPU implementation is also given. The current work demonstrates the potential usefulness of GPU programming in accelerating this field of applications.