Mapping Fitness and Free Energy Landscapes of Proteins

抽象， 申请资金购买计算机设备，以支持家长所述的研究项目 GRANT NIH 1R35GM132090《绘制蛋白质的适合度和自由能景观图》(R.M.Levy，PI)。《少年派》 是一位在计算结构生物学和生物物理学方面有40年经验的计算科学家。 在家长资助中描述的研究集中在计算的发展和应用上 研究蛋白质结构、功能和动力学的方法。美国国立卫生研究院资助的所有项目 1R35GM132090依赖于访问最先进的计算资源。自2015年以来，软件的进步 算法使人们更多地关注用于机器学习和 解决计算化学、生物物理学和结构生物学方面的问题。访问最新的GPU 硬件是成功完成家长资助中所述研究的关键。《少年派》 申请补充购买8个NVIDIA A100 40 GB PCIe GPU，GPU核心数量增加一倍 与PI当前可访问的消费级GPU相比，带宽增加了三倍，而且它们 针对高性能计算进行了更好的优化。新的GPU将连接到现有的 共享计算机集群，生物物理和计算生物学研究资源中心(CB2RR) 集群，它支持NIH资助的研究人员。GPU将使用High- 吞吐量、低开销InfiniBand HDR 200 Gbps连接，将与GPU一起购买。在……里面 此外，还需要资金将计算机集群的存储系统升级为500TB的RAID-6，因为 目前的硬盘现在已经过了保修期，并且已经到了生命周期的末尾。该系统将在以下方面表现出色 加速高度并行化的纯GPU和CPU-GPU混合分子动力学模拟和机器 学习应用程序，如NIH 1R35GM132090所述。

Abstract, Funds are requested to purchase computer equipment to support the research projects described in the parent grant NIH 1R35GM132090 “Mapping Fitness and Free Energy Landscapes of Proteins” (R.M. Levy, PI). The PI is a computational scientist with forty years of experience in computational structural biology and biophysics. The research described in the parent grant is focused on the development and application of computational methods for studying the structure, function, and dynamics of proteins. All of the projects supported by NIH 1R35GM132090 rely on access to state-of-the-art computational resources. Since 2015, advances in software and algorithms have led to a significantly greater focus on GPU implementations for machine learning and for solving problems in computational chemistry, biophysics, and structural biology. Access to the latest GPU hardware is essential for the successful completion of the research described in the parent grant. The PI requests a supplement to purchase 8 NVIDIA A100 40GB PCIe GPUs, with double the number of GPU cores and triple the bandwidth relative to the current consumer-level GPUs the PI currently has access to, and they are much better optimized for high-performance computing. The new GPUs will be connected to an existing shared computer cluster, the Center for Biophysics and Computational Biology Research Resource (CB2RR) cluster, which supports NIH funded researchers. The GPUs will be connected to the cluster using high- throughput, low overhead InfiniBand HDR 200 Gbps connections, which will be purchased with the GPUs. In addition, funds are requested to upgrade the storage system of the computer cluster with a 500TB RAID-6, as the current hard disks are now out of warranty and at the end of their life cycle. This system will excel at accelerating highly parallelized GPU-only and CPU-GPU hybrid molecular dynamics simulations and machine learning applications as described in NIH 1R35GM132090.