Improving the life-cycle of tensor algorithm development
karl pierce
In this talk we showcase the advancements of the low-level implementation of ITensors. These advancements seek to tighten the gap between rapid algorithmic development and efficient exascale implementations. Advancements were attained through the generic redevelopment of the backend NDTensors module. We showcase our work via acceleration of the DMRG optimization of the one- and two-dimensional model Hamiltonian problems with controllable accuracy using GPU accelerators.
Physics & Quantum Chemistry
Method (1.5)