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There has been recent interest in the deployment of ab initio density matrixrenormalization group computations on high performance computing platforms.Here, we introduce a reformulation of the conventional distributed memory abinitio DMRG algorithm that connects it to the conceptually simpler andadvantageous sum of sub-Hamiltonians approach. Starting from this framework, wefurther explore a hierarchy of parallelism strategies, that includes (i)parallelism over the sum of sub-Hamiltonians, (ii) parallelism over sites,(iii) parallelism over normal and complementary operators, (iv) parallelismover symmetry sectors, and (v) parallelism over dense matrix multiplications.We describe how to reduce processor load imbalance and the communication costof the algorithm to achieve higher efficiencies. We illustrate the performanceof our new open-source implementation on a recent benchmark ground-statecalculation of benzene in an orbital space of 108 orbitals and 30 electrons,with a bond dimension of up to 6000, and a model of the FeMo cofactor with 76orbitals and 113 electrons. The observed parallel scaling from 448 to 2800 CPUcores is nearly ideal.

Low communication high performance ab initio density matrix renormalization group algorithms