September 16, 2023
Journal Article

TAMM: Tensor Algebra for Many-body Methods


Tensor contraction operations in computational chemistry consume a significant fraction of the 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. TAMM 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 direct their attention to various optimizations on the underlying constructs, such as efficient data distribution, optimized scheduling algorithms, and efficient use of intra-node resources (e.g., GPUs). The modular structure of TAMM allows it to support different hardware architectures and incorporate new algorithmic advances. We describe the TAMM framework and our approach to the sustainable development of tensor contraction-based methods in computational chemistry applications. We present case studies highlighting the ease of use, including the performance and productivity gains compared to other implementations.

Published: September 16, 2023


Mutlu E., A.R. Panyala, N. Gawande, A. Bagusetty, J.G. Glabe, J. Kim, and K. Kowalski, et al. 2023. TAMM: Tensor Algebra for Many-body Methods. The Journal of Chemical Physics 159, no. 2:Art. No. 02480. PNNL-SA-169718. doi:10.1063/5.0142433