标题： 成本优化的从头算张量网络态方法：工业视角 显示英文标题

标题： Cost optimized ab initio tensor network state methods: industrial perspectives

摘要： 我们引入了高效的解决方案，在使用昂贵的GPU加速硬件时优化树状张量网络状态方法计算的成本。 通过支持一个强大的计算节点，并附加更多但便宜的辅助节点来存储中间的、预收缩的张量网络临时数据，IO时间几乎可以完全隐藏在计算之后，而不会增加内存峰值。 我们的解决方案基于不同通信通道的不同带宽，如NVLink、PCIe、InfiniBand和可用的存储介质，这些在算法的不同层中被使用。 这种通过异步IO操作实现的简单异构多节点解决方案有可能最小化IO开销，从而为主要计算单元实现最大性能速率。 此外，我们介绍了一种内部开发的大规模并行协议，用于序列化和反序列化块稀疏矩阵和张量，大大减少了数据通信时间。 对于FeMoco活性化合物，展示了自旋适应的从头算密度矩阵重整化群方法的性能曲线，对应U(1)键维值高达15400，在完整活性空间(CAS)大小为最多113个电子和76个轨道[CAS(113, 76)]的情况下。 显示英文摘要

摘要： We introduce efficient solutions to optimize the cost of tree-like tensor network state method calculations when an expensive GPU-accelerated hardware is utilized. By supporting a main powerful compute node with additional auxiliary, but much cheaper nodes to store intermediate, precontracted tensor network scratch data, the IO time can be hidden behind the computation almost entirely without increasing memory peak. Our solution is based on the different bandwidths of the different communication channels, like NVLink, PCIe, InfiniBand and available storage media, which are utilized on different layers of the algorithm. This simple heterogeneous multiNode solution via asynchronous IO operation has the potential to minimize IO overhead, resulting in maximum performance rate for the main compute unit. In addition, we introduce an in-house developed massively parallel protocol to serialize and deserialize block sparse matrices and tensors, reducing data communication time tremendously. Performance profiles are presented for the spin adapted ab initio density matrix renormalization group method for corresponding U(1) bond dimension values up to 15400 on the active compounds of the FeMoco with complete active space (CAS) sizes of up to 113 electrons in 76 orbitals [CAS(113, 76)].