ACCELERATION OF MOLECULAR MODELING APPLICATIONS WITH GRAPHICS PROCESSORS

使用图形处理器加速分子建模应用

• 批准号: 7955608
• 负责人: DAVID J HARDY
• 金额: $ 3.83万
• 依托单位: UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2010-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7955608
• 关键词: Acceleration; Architecture; Arts; Bioinformatics; Computer Retrieval of Information on Scientific Projects Database; Computer software; Funding; Generations; Grant; Individual; Institution; Modeling; Molecular Models; Operative Surgical Procedures; Performance; Process; Research; Research Personnel; Resources; Source; Time; United States National Institutes of Health; molecular modeling

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Over the past several years, the hardware and software architecture of graphics processing units (GPUs) has evolved to the point that they can now be used for general purpose scientific computations. State-of-the-art graphics processors include hundreds of individual arithmetic units and can perform up to 500 billion floating point operations per second, a level of performance far above that available with current generation CPU cores. The Resource has implemented several GPU-accelerated computational kernels for key molecular modeling tasks which achieve performance levels of ten to one hundred times that of traditional CPU implementations (http://www.ks.uiuc.edu/Research/gpu/).

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 在过去的几年里，图形处理单元（GPU）的硬件和软件架构已经发展到现在可以用于通用科学计算的地步。 最先进的图形处理器包括数百个独立的算术单元，每秒可执行高达5000亿次浮点运算，这一性能水平远远高于当前一代CPU内核。 该资源已为关键分子建模任务实现了几个GPU加速计算内核，其性能水平是传统CPU实现的十到一百倍（http：//www.ks.uiuc.edu/Research/gpu/）。