1. GPU accelerated tensor contractions in the plaquette renormalization scheme
- Author
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Yu, J.F., Hsiao, H.-C., and Kao, Ying-Jer
- Subjects
- *
GRAPHICS processing units , *COMPUTER architecture , *PARALLEL computers , *TENSOR algebra , *COMPUTER algorithms , *MOTHERBOARDS - Abstract
Abstract: We use the graphical processing unit (GPU) to accelerate the tensor contractions, which is the most time consuming operations in the variational method based on the plaquette renormalized states. Using a frustrated Heisenberg J 1–J 2 model on a square lattice as an example, we implement the algorithm based on the compute unified device architecture (CUDA). For a single plaquette contraction with the bond dimensions C =3 of each rank of the tensor, results are obtained 25 times faster on GPU than on a current CPU core. This makes it possible to simulate systems with the size 8×8 and larger, which are extremely time consuming on a single CPU. This technology successfully relieves the computing time dependence with C, while in the CPU serial computation, the total required time scales both with C and the system size. [Copyright &y& Elsevier]
- Published
- 2011
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