Multiplication of medium-density matrices using TensorFlow on multicore CPUs

Tehnički Glasnik

Siraphob Theeracheep¹  Jaruloj Chongstitvatana¹ 

[1] Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand;

关键词: Sparse matrix;    Matrix multiplication;    TensorFlow;   

DOI  :  

来源: DOAJ

【 摘 要 】

Matrix multiplication is an essential part of many applications, such as linear algebra, image processing and machine learning. One platform used in such applications is TensorFlow, which is a machine learning library whose structure is based on dataflow programming paradigm. In this work, a method for multiplication of medium-density matrices on multicore CPUs using TensorFlow platform is proposed. This method, called tbt_matmul, utilizes TensorFlow built-in methods tf.matmul and tf.sparse_matmul. By partitioning each input matrix into four smaller sub-matrices, called tiles, and applying an appropriate multiplication method to each pair depending on their density, the proposed method outperforms the built-in methods for matrices of medium density and matrices of significantly uneven distribution of non-zeros.

【 授权许可】

Unknown