Abstract: The present disclosure provides a GPU-based third-order low-rank tensor calculation method. Operation steps of the method include: transmitting, by a CPU, third-order real value tensor input data DATA1 to a CPU; performing, by the GPU, Fourier transforms on the DATA1, to obtain third-order complex value tensor data DATA2; performing, by the GPU, matrix operations on the DATA2, to obtain third-order complex value tensor data DATA3; performing, by the GPU, inverse Fourier transforms on the DATA3, to obtain third-order real value tensor output data DATA4; and transmitting, by the GPU, the DATA4 to the CPU. In the present disclosure, in the third-order low-rank tensor calculation, a computational task with high concurrent processes is accelerated by using the CPU to improve computational efficiency. Compared with conventional CPU-based third-order low-rank tensor calculation, computational efficiency is significantly improved, and same calculation can be completed by using less time.

Abstract: The present disclosure provides a GPU-based third-order low-rank tensor completion method. Operation steps of the method includes: (1) transmitting, by a CPU, input data DATA1 to a GPU, and initializing the loop count t=1; (2) obtaining, by the GPU, a third-order tensor Yt of a current loop t based on the least squares method; (3) obtaining, by the GPU, a third-order tensor Xt of the current loop t based on the least squares method; (4) checking, by the CPU, whether an end condition is met; and if the end condition is met, turning to (5); otherwise, increasing the loop count t by 1 and turning to (2) to continue the loop; and (5) outputting, by the GPU, output data DATA2 to the CPU. In the present disclosure, in the third-order low-rank tensor completion, a computational task with high concurrent processes is accelerated by using the GPU to improve computational efficiency.

Abstract: The present disclosure provides a GPU-based third-order low-rank tensor completion method. Operation steps of the method includes: (1) transmitting, by a CPU, input data DATA1 to a GPU, and initializing the loop count t=1; (2) obtaining, by the GPU, a third-order tensor Yt of a current loop t based on the least squares method; (3) obtaining, by the GPU, a third-order tensor Xt of the current loop t based on the least squares method; (4) checking, by the CPU, whether an end condition is met; and if the end condition is met, turning to (5); otherwise, increasing the loop count t by 1 and turning to (2) to continue the loop; and (5) outputting, by the GPU, output data DATA2 to the CPU. In the present disclosure, in the third-order low-rank tensor completion, a computational task with high concurrent processes is accelerated by using the GPU to improve computational efficiency.