ExaTN - A Scalable Exascale Math Library for Hierarchical Tensor Network Representations and Simulations

A problem that will benefit from exascale computing is predictive simulation of two- and three-dimensional quantum many-body Hamiltonians. Enabling efficient numerical simulations requires new insights into wavefunction compression that scales on extremely heterogeneous HPC architectures. To address this need, we develop ExaTN: a math library of parallel numerical primitives for processing operations based on hierarchical tensor representations. Our library provides a scalable infrastructure for building a performance portable framework for simulating strongly correlated quantum systems on heterogeneous HPC platforms such as Titan and Summit. Our framework is the first to deliver a massively parallel implementation of the multiscale entanglement renormalization ansatz (MERA), a promising hierarchical tensor decomposition scheme capable of expressing local expectation values in strongly entangled quantum systems efficiently. In this talk, we will present our integrated framework for processing hierarchical tensor representations on exascale HPC systems via a multi-level asynchronous task-based programming model. This provides scientists with a capability for simulating strongly entangled systems in condensed matter physics.