Method for small matrix inversion by using GPU (graphic processing unit)

Abstract

The invention discloses a method for small matrix inversion by using a GPU (graphic processing unit), which relates to the field of wireless communication. The method includes the steps: setting up a two-dimensional array sm_a with KX(NXN) serving as dimensionality on a shared memory of the GPU, and setting up a two-dimensional array sm_is and a two-dimensional array sm_js, wherein the dimensionality of each of the two-dimensional array sm_is and the two-dimensional array sm_js is KXN, and each of K and N is a natural number larger than 0; parallelly storing K N-order square matrixes in a global memory of the GPU into the two-dimensional array sm_a of the shared memory; and completing inversion processing for the K N-order square matrixes in the shared memory by the aid of the two-dimensional array sm_is and the two-dimensional array sm_js. By the aid of the method, parallel threads are increased, the shared memory is not excessively occupied, good expandability is achieved, and calculating speed for small matrix inversion is remarkably increased.

Description

technical field [0001] The invention relates to the technical field of wireless communication, in particular to a method for inverting a small matrix by using a GPU. Background technique [0002] Matrix inversion is an important matrix operation that is often encountered, and it is widely used in signal processing, neural network, automatic control and other fields. Especially in 4G wireless communication standards, multiple key functional modules, such as OFDM (Orthogonal Frequency Division Multiplexing, Orthogonal Frequency Division Multiplexing) system channel estimation, MIMO (Multiple-Input Multiple-Out-put, multiple input and output antenna system ) signal detection, etc., when using the zero-forcing algorithm or the minimum mean square error algorithm, it can be attributed to the inverse operation of a certain transformation of the channel matrix. In addition, for the LDPC (Low Density Parity Check Code, low Density parity check code) code encoding also requires larg...

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Problems solved by technology

However, if one thread is used to calculate the inverse of a matrix, then each thread consumes more shared memory, thereby limiting the number of concurrent threads on the SM, thereby reducing its parallel efficiency

On the other hand, if one thread block is used to calculate the inverse of a matrix, that is, each thread in the thread block processes one element of the matrix, since the matrix size we have to process is often small (for example, 2×2, 4×4, 8×8), so the parallel threads on a thread block are too small, which will also affect its efficiency

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