Pip 安装 pytorch gpu 版本

We show that numerical computations based on tensor renormalization group (TRG) methods can be significantly accelerated with PyTorch on graphics processing units (GPUs) by leveraging NVIDIA's Compute Unified Device Architecture (CUDA). We find improvement in the runtime and its scaling with bond dimension for two-dimensional systems. Our results establish that the utilization of GPU resources is essential for future precision computations with TRG.

随着我国城镇化建设的不断发展,建筑机电安装施工工程规模越来越大,成本管理工程的难度也越来越高。因此,如何做好机电安装工程的造价管理工作已经成为施工单位十分重要的研究课题之一。因此,本文详细分析了建筑工程施工中造价管理普遍存在的各种问题,并在此基础上详细论述了机电安装工程造价管理的实施策略。