A practical streaming approximate matrix multiplication algorithm

Francis, Deena P.; Raimond, Kumudha

doi:10.1016/j.jksuci.2018.09.010

Cited by 1 publication

(1 citation statement)

References 29 publications

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“…A well-known method for accelerating matrix multiplication is to use GPUs. Other research has focused on implementing fast matrix multiplication libraries [ 7 , 8 , 9 ], designing custom hardware to accelerate specific classes of matrix multiplication [ 10 , 11 , 12 ], accelerating distributed matrix multiplication [ 13 , 14 , 15 ], designing exact and low-computation-order algorithms based on the divide-and-conquer method [ 16 , 17 , 18 ], and designing efficient approximate matrix multiplication (AMM) algorithms [ 19 , 20 , 21 ]. These areas are still active research fields.…”

Section: Introductionmentioning

confidence: 99%

Acceleration of Approximate Matrix Multiplications on GPUs

Okuyama

Röhm

Mihana

et al. 2023

Entropy

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Matrix multiplication is important in various information-processing applications, including the computation of eigenvalues and eigenvectors, and in combinatorial optimization algorithms. Therefore, reducing the computation time of matrix products is essential to speed up scientific and practical calculations. Several approaches have been proposed to speed up this process, including GPUs, fast matrix multiplication libraries, custom hardware, and efficient approximate matrix multiplication (AMM) algorithms. However, research to date has yet to focus on accelerating AMMs for general matrices on GPUs, despite the potential of GPUs to perform fast and accurate matrix product calculations. In this paper, we propose a method for improving Monte Carlo AMMs. We also give an analytical solution for the optimal values of the hyperparameters in the proposed method. The proposed method improves the approximation of the matrix product without increasing the computation time compared to the conventional AMMs. It is also designed to work well with parallel operations on GPUs and can be incorporated into various algorithms. Finally, the proposed method is applied to a power method used for eigenvalue computation. We demonstrate that, on an NVIDIA A100 GPU, the computation time can be halved compared to the conventional power method using cuBLAS.

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