Parallel acceleration of density matrix renormalization group calculations with TensorFlow

Authors

Kryzz Joshua G. de Leon; Francis N. C. Paraan

Source

Proceedings of the 37th Samahang Pisika ng Pilipinas Physics Conference, Tagbilaran City, Philippines, SPP-2019-PB-14 (2019).

Abstract

We parallelize singular value decomposition in a matrix product state formulation of the density matrix renormalization group using the TensorFlow library to find use cases in which consumer-grade GPU hardware can reduce run times. Specifically, we tested the performance of the implementation on a 20-site spin chain for a variable number of kept states. We were able to acquire a speedup of up to 6.4% when using TensorFlow GPU libraries and a speedup of up to 5.4% with TensorFlow multicore CPU libraries. This speedup is observed when the number of kept states exceeds a threshold value so that the dimensions of the matrices in the calculation are large enough that the gains in parallelization exceed computational overhead costs.