Denovo is a new, three-dimensional, discrete ordinates (SN) transport code that uses state-of-the-art solution methods to obtain accurate solutions to the Boltzmann transport equation. Denovo uses the Koch-Baker-Alcouffe parallel sweep algorithm to obtain high parallel efficiency on O(100) processors on XYZ orthogonal meshes. As opposed to traditional SN codes that use source iteration, Denovo uses nonstationary Krylov methods to solve the within-group equations. Krylov methods are far more efficient than stationary schemes. Additionally, classic acceleration schemes (diffusion synthetic acceleration) do not suffer stability problems when used as a preconditioner to a Krylov solver. Denovo's generic programming framework allows multiple spatial discretization schemes and solution methodologies. Denovo currently provides diamond-difference, theta-weighted diamond-difference, linear-discontinuous finite element, trilinear-discontinuous finite element, and step characteristics spatial differencing schemes. Also, users have the option of running traditional source iteration instead of Krylov iteration. Multigroup upscatter problems can be solved using Gauss-Seidel iteration with transport, two-grid acceleration. A parallel first-collision source is also available. Denovo solutions to the Kobayashi benchmarks are in excellent agreement with published results. Parallel performance shows excellent weak scaling up to 20000 cores and good scaling up to 40000 cores.