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首页> 外文期刊>The journal of physical chemistry, A. Molecules, spectroscopy, kinetics, environment, & general theory >Performance Optimization of Tensor Contraction Expressions for Many-Body Methods in Quantum Chemistry
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Performance Optimization of Tensor Contraction Expressions for Many-Body Methods in Quantum Chemistry

机译:量子化学中多体方法的张量压缩表达式的性能优化

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Complex tensor contraction expressions arise in accurate electronic structure models in quantum chemistry, such as the coupled cluster method. This paper addresses two complementary aspects of performance optimization of such tensor contraction expressions. Transformations using algebraic properties of commutativity and associativity can be used to significantly decrease the number of arithmetic operations required for evaluation of these expressions. The identification of common subexpressions among a set of tensor contraction expressions can result in a reduction of the total number of operations required to evaluate the tensor contractions. The first part of the paper describes an effective algorithm for operation minimization with common subexpression identification and demonstrates its effectiveness on tensor contraction expressions for coupled cluster equations. The second part of the paper highlights the importance of data layout transformation in the optimization of tensor contraction computations on modern processors. A number of considerations, such as minimization of cache misses and utilization of multimedia vector instructions, are discussed. A library for efficient index permutation of multidimensional tensors is described, and experimental performance data is provided that demonstrates its effectiveness.
机译:复杂的张量收缩表达式出现在量子化学的精确电子结构模型中,例如耦合簇方法。本文讨论了这种张量收缩表达式的性能优化的两个互补方面。使用可交换性和可结合性的代数性质进行的变换可用于显着减少评估这些表达式所需的算术运算次数。在一组张量收缩表达式中识别公共子表达式可导致减少评估张量收缩所需的操作总数。本文的第一部分描述了一种有效的用于最小化操作的算法,该算法具有常见的子表达式识别,并证明了其对耦合聚类方程的张量收缩表达式的有效性。本文的第二部分突出了数据布局转换在优化现代处理器上的张量收缩计算中的重要性。讨论了许多考虑因素,例如最小化高速缓存未中和对多媒体矢量指令的利用。描述了一个用于多维张量的有效索引置换的库,并提供了证明其有效性的实验性能数据。

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