Fast recursive matrix multiplication for multi-core architectures

AbstractIn this article, we present a fast algorithm for matrix multiplication optimized for recent multicore architectures. The implementation exploits different methodologies from parallel programming, like recursive decomposition, efficient low-level implementations of basic blocks, software prefetching, and task scheduling resulting in a multilevel algorithm with adaptive features. Measurements on different systems and comparisons with GotoBLAS, Intel Math Kernel Library (IMKL), and AMD Core Math Library (AMCL) show that the matrix implementation presented has a very high efficiency

Multiplication of medium-density matrices using TensorFlow on multicore CPUs

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