Tensor algebra operations such as contractions 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 from 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., graphics processing units). 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 scalable ground- and excited-state electronic structure methods. We present case studies highlighting the ease of use, including the performance and productivity gains compared to other frameworks.

Advanced Computing Mathematics and Data Division Pacific Northwest National Laboratory Richland Washington 99354 USA

Argonne Leadership Computing Facility Argonne National Laboratory Argonne Illinois 60439 USA

Google Inc Mountain View California 94043 USA

Intel Corporation Richland Washington 99352 USA

J Heyrovský Institute of Physical Chemistry Academy of Sciences of the Czech Republic 182 23 Prague 8 Czech Republic

Physical Sciences Division Pacific Northwest National Laboratory Richland Washington 99354 USA

School of Computer Science and Engineering Chung Ang University Seoul 06974 South Korea

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