Topology optimization aims to find the most efficient material distribution in a specified domain without violating user-defined design constraints. When applied to continuum structures, topology optimization is usually performed by means of the well-known density methods. In this work we focus on the application of its discrete formulation where a given domain is discretized into a ground structure, i.e., a finite spatial distribution of nodes connected using truss members. The ground structure method provides an approximation to optimal Michell-type structures, composed of an infinite number of members, by using a reduced number of truss members. The optimal least weight truss for a single load case, under linear elastic conditions, subjected to stress constraints can be posed as a linear programming problem. The aim of this work is to provide a scalable implementation for the optimization of least weight trusses embedded in any domain geometry. The method removes unnecessary members from a truss that has a user-defined degree of connectivity while keeping the nodal locations fixed. We discuss in detail the scalable implementation of the ground structure method using an efficient and robust interior point algorithm within a parallel computing environment (involving Graphics Processing Units or GPUs). The capabilities of the proposed implementation is illustrated by means of large scale applications on practical problems with millions of members in both 2D and 3D structures.