为现代分布式内存张量软件生成耦合簇代码
Generating coupled cluster code for modern distributed memory tensor software
AI总结：
本文介绍通用阶耦合簇代码生成器及开源张量框架tenpi，以系统化高性能GPU代码开发，并在ExaTENSOR上于1200个GPU验证优异弱扩展。
摘要：
由于异构硬件结构，高效利用基于GPU的HPC平台进行耦合簇计算是一项挑战。软件需要不断适应这些结构，并耗费大量人力工时，因此高性能代码开发的系统化十分必要，对于高阶耦合簇而言更是如此。这通常通过引入问题的高层表示来实现，随后利用编译器/翻译器组件将其转换为面向硬件的低层指令。设计此类软件还面临另一项挑战：既要通过捕获张量的关键对称性来实现高效实现，又要保留相对于硬件的抽象。我们回顾了解决这两项挑战的方法，同时介绍了促使我们开发通用阶耦合簇代码生成器的设计决策。使用分布式内存张量库ExaTENSOR，系统生成的代码在多达1200个GPU上运行时表现出优异的弱可扩展性。我们提出了一个用于耦合簇代码开发的开源模块化张量框架“tenpi”，具备图解推导、可视化模块、符号代数、中间量优化以及对多张量后端的支持。Tenpi为用于相对论分子计算的DIRAC代码的大规模并行ExaCorr模块带来了高阶CC功能。
英文摘要：
Using GPU-based HPC platforms efficiently for coupled cluster computations is a challenge due to heterogeneous hardware structures. The constant need to adapt software to these structures and the required man-hours makes a systematization of high-performance code development desirable, even more so for higher-order coupled cluster. This is generally achieved by introducing a high-level representation of the problem, which is then translated to low-level instructions for the hardware using a compiler/translator component. Designing such software comes with another challenge: Allowing efficient implementation by capturing key symmetries of tensors, while retaining the abstraction from the hardware. We review ways to address these two challenges while presenting design decisions which led us to the development of a general-order coupled cluster code generator. The systematically produced code shows excellent weak scaling behavior running on up to 1200 GPUs using the distributed memory tensor library ExaTENSOR. We present an open-source modular tensor framework "tenpi" for coupled cluster code development with diagrammatic derivation, visualization module, symbolic algebra, intermediate optimization and support for multiple tensor backends. Tenpi brings higher-order CC functionality to the massively parallel ExaCorr module of the DIRAC code for relativistic molecular calculations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
