@inproceedings {deleonSPP2019,
	title = {Parallel acceleration of density matrix renormalization group calculations with TensorFlow},
	booktitle = {Proceedings of the 37th Samahang Pisika ng Pilipinas Physics Conference},
	year = {2019},
	month = {29 May 2019},
	pages = {SPP-2019-PB-14},
	address = {Tagbilaran City, Philippines},
	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.},
	url = {https://paperview.spp-online.org/proceedings/article/view/SPP-2019-PB-14},
	author = {Kryzz Joshua G. de Leon and Francis N. C. Paraan}
}