Quantum many-body physics is the body of knowledge which studies systems of many interacting particles and the mathematical framework for calculating properties of these systems. Methods in many-body physics which use a first principles approach to solving the many-body Schrodinger equation are referred to as ab initio methods, and provide approximate solutions which are systematically improvable. Coupled cluster theory is an ab initio quantum many-body method which has been shown to provide accurate calculations of ground state energies for a wide range of systems in quantum chemistry and nuclear physics. Calculations of physical properties using ab initio many-body methods can be computationally expensive, requiring the development of efficient data structures, algorithms and techniques in high-performance computing to achieve numerical accuracy.Many physical systems of interest are difficult or impossible to measure experimentally, and so are reliant on predictive and accurate calculations from many-body theory. Neutron stars in particular are difficult to collect observational data for, but simulations of infinite nuclear matter can provide key insights to the internal structure of these astronomical objects. The main focus of this thesis is the development of a large and versatile coupled cluster program which implements a sparse tensor storage scheme and efficient tensor contraction algorithms. A distributed memory data structure for these large, sparse tensors is used so that the code can run in a high-performance computing setting, and can thus handle the computational challenges of infinite nuclear matter calculations using large basis sets. By validating these data structures and algorithms in the context of coupled cluster theory and infinite nuclear matter, they can be applied to a wide range of many-body methods and physical systems.
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