  We present the Tucker tensor DFT (TTDFT) code which uses a tensor-structured
algorithm with graphic processing unit (GPU) acceleration for conducting
ground-state DFT calculations on large-scale systems. The Tucker tensor DFT
algorithm uses a localized Tucker tensor basis computed from an additive
separable approximation to the Kohn-Sham Hamiltonian. The discrete Kohn-Sham
problem is solved using Chebyshev filtering subspace iteration method that
relies on matrix-matrix multiplications of a sparse symmetric Hamiltonian
matrix and a dense wavefunction matrix, expressed in the localized Tucker
tensor basis. These matrix-matrix multiplication operations, which constitute
the most computationally intensive step of the solution procedure, are GPU
accelerated providing ~8-fold GPU-CPU speedup for these operations on the
largest systems studied. The computational performance of the TTDFT code is
presented using benchmark studies on aluminum nano-particles and silicon
quantum dots with system sizes ranging up to ~7,000 atoms.
