A Parallel Computing Method for the Higher Order Tensor Renormalization Group

TL;DR

This paper introduces a parallel computing approach for the Higher Order Tensor Renormalization Group that reduces computational cost and memory requirements by distributing tensor elements across processes, avoiding communication bottlenecks.

Contribution

A novel parallelization method for HOTRG that distributes tensor elements to minimize communication and optimize computational efficiency in high-dimensional lattice models.

Findings

Reduces computational cost to O(χ^{4d-3}) per process for d ≥ 3.

Maintains memory requirement at O(χ^{2d-1}) per process.

Effectively avoids inter-process communication during tensor contraction.

Abstract

In this paper, we propose a parallel computing method for the Higher Order Tensor Renormalization Group (HOTRG) applied to a $d$-dimensional $( d \geq 2 )$ simple lattice model. Sequential computation of the HOTRG requires $O ( \chi^{4 d - 1} )$ computational cost, where $\chi$ is bond dimension, in a step to contract indices of tensors. When we simply distribute elements of a local tensor to each process in parallel computing of the HOTRG, frequent communication between processes occurs. The simplest way to avoid such communication is to hold all the tensor elements in each process, however, it requires $O ( \chi^{2d} )$ memory space. In the presented method, placement of a local tensor element to more than one process is accepted and sufficient local tensor elements are distributed to each process to avoid communication between processes during considering computation step. For the bottleneck part of computational cost, such distribution is achieved by distributing elements of two local tensors to $\chi^2$ processes according to one of the indices of each local tensor which are not contracted during considering computation. In the case of $d \geq 3$, computational cost in each process is reduced to $O ( \chi^{4 d - 3} )$ and memory space requirement in each process is kept to be $O ( \chi^{2d - 1} )$.