Tensor contraction operations in computational chemistry consume significant fractions of computing time on large-scale computing platforms. The widespread use of tensor contractions between large multi-dimensional tensors in describing electronic structure theory has motivated the development of multiple tensor algebra frameworks targeting heterogeneous computing platforms. In this paper, we present Tensor Algebra for Many-body Methods (TAMM), a framework for productive and performance-portable development of scalable computational chemistry methods. The TAMM framework decouples the specification of the computation and the execution of these operations on available high-performance computing systems. With this design choice, the scientific application developers (domain scientists) can focus on the algorithmic requirements using the tensor algebra interface provided by TAMM whereas high-performance computing developers can focus on various optimizations on the underlying constructs such as efficient data distribution, optimized scheduling algorithms, efficient use of intra-node resources (e.g., GPUs). The modular structure of TAMM allows it to be extended to support different hardware architectures and incorporate new algorithmic advances. We describe the TAMM framework and our approach to sustainable development of tensor contraction-based methods in computational chemistry applications. We present case studies that highlight the ease of use as well as the performance and productivity gains compared to other implementations.

翻译：摘要：在计算化学中，张量缩并操作在大型计算平台上占据了显著的计算时间。描述电子结构理论时，大型多维张量之间的张量缩并被广泛应用，这促使了面向异构计算平台的多种张量代数框架的开发。本文提出了面向多体方法的张量代数框架（TAMM），这是一个用于可扩展计算化学方法的高效且性能可移植的开发框架。TAMM框架将计算规约与在现有高性能计算系统上的执行过程解耦。通过这一设计选择，科学应用开发者（领域科学家）可以利用TAMM提供的张量代数接口专注于算法需求，而高性能计算开发者则可以专注于底层构件的各种优化，例如高效的数据分布、优化的调度算法以及节点内资源（如GPU）的有效利用。TAMM的模块化结构使其能够扩展以支持不同的硬件架构并融入新的算法进展。本文描述了TAMM框架及其在计算化学应用中基于张量缩并方法的可持续开发策略。我们通过案例研究展示了该框架的易用性，以及与其他实现相比在性能和生产力方面的提升。