32 results about "General matrix" patented technology

A system and method for optimizing the performance of a graphics processing unit (GPU) for processing and execution of general matrix operations such that the operations are accelerated and optimized. The system and method describes the layouts of operands and results in graphics memory, as well as partitioning the processes into a sequence of passes through a macro step. Specifically, operands are placed in memory in a pattern, results are written into memory in a pattern appropriate for use as operands in a later pass, data sets are partitioned to insure that each pass fits into fixed sized memory, and the execution model incorporates generally reusable macro steps for use in multiple passes. These features enable greater efficiency and speed in processing and executing general matrix operations.

The invention discloses an FPGA (Field Programmable Gate Array)-based general matrix fixed-point multiplier. An internal structure of the multiplier consists of a control module, a conversion module, an operation module and a storage module. The control module is used for generating a control signal according to dimension of a to-be-operated matrix. The conversion module is responsible for performing conversion between a fixed-point number and a floating-point number during operation. The operation module is used for reading operation data from the storage module and the conversion module, performing fixed-point multiplication and fixed-point accumulating operation and storing a result in the storage module. The storage module is used for caching to-be-operated matrix data and result matrix data, providing an interface compatible with a bus signal and allowing access of other components on a bus. The characteristic of high fixed-point calculation efficiency in hardware is fully utilized; by using a unique operation structure, simultaneous conversion and operation of the data are realized to improve the overall operation speed, and a plurality of matrix fixed-point multipliers can be simultaneously used to perform parallel calculation; thus the fixed-point multiplication of an arbitrary dimension matrix can be supported, and meanwhile extremely high calculation efficiency is guaranteed. Compared with matrix multiplication performed by using the floating-point number, the multiplier has the advantage that the calculation efficiency is greatly improved.

A method and system for performing general matrix-matrix multiplication (GEMM) operations on a graphics processor unit (GPU) using Smart kernels. During operation, the system may generate a set of kernels that includes at least one of a variable-dimension variable-K GEMM kernel, a variable-dimension constant-K GEMM kernel, or a combination thereof. A constant-K GEMM kernel performs computations for matrices with a specific value of K (e.g., the number of columns in a first matrix and the number of rows in a second matrix). Variable-dimension GEMM kernels allow for flexibility in the number of rows and columns used by a thread block to perform matrix multiplication for a sub-matrix. The system may generate rules to select the best (e.g., fastest) kernel for performing computations according to the particular parameter combination of the matrices being multiplied.

A method for analyzing a torsional vibration inherent characteristic of a planet gear transmission system comprises the following four steps: (1) carrying out mathematical modeling on pure torsional vibration of a single planet row with damping by using an Lagrangian equation; (2) analyzing the inherent characteristic of the transmission system; (3) verifying the influence of the damping on a fixed frequency through multigroup calculation; and (4) establishing a general matrix for the planet gear transmission system and carrying out modeling simulation verification through instance value calculation and simulation X. Through the adoption of the invention, an obtained damped natural vibration differential equation in the form of a parameterized matrix (a rigidity matrix, a damping matrix and a connecting matrix) in the planet transmission system is general; corresponding matrix forms are directly selected for corresponding parts according to different connecting structures and planet gear system parameters to construct an integral system matrix; and after parameters are brought in, the inherent characteristic of the transmission system is rapidly and accurately analyzed.

The invention relates to a method and system for conducting compression and query on a sparse matrix. According to the method, a k2-tree method is improved on the aspects of rank operation change and common matrix and non-zero matrix processing. Firstly, a sparse matrix to be processed is preprocessed to obtain a sparse matrix A of a square matrix with a unit value 0 or1; then, a k2-tree algorithm is adopted to obtain arrays T (tree) and L (leaves), the Rank array interval fixing digits are stored according to information in the T (tree) to obtain Rank (tree), V (leaves) and rank (leaves) values are obtained according to the L (leaves) and an original corresponding sparse matrix, and stored values in the sparse matrix A can be queried after coordinates of query units are input. The sparse matrix can be effectively compressed, query speed is higher, and more storage space is saved.

A method and system for performing general matrix-vector multiplication (GEMV) operations on a graphics processor unit (GPU) using Smart kernels. During operation, the system may generate a set of kernels that includes at least one of a variable-N GEMV kernel and a constant-N GEMV kernel. A constant-N GEMV kernel performs computations for matrix and vector combinations with a specific value of N (e.g., the number of columns in a matrix and the number of rows in a vector). Variable-N GEMV kernels may perform computations for all values of N. The system may also generate 1B1R kernels, constant-N variable-rows GEMV kernels, and variable-N variable-rows GEMV kernels. The system may generate constant-N variable-threads GEMV kernels, and variable-N variable-threads GEMV kernels. The system may also generate variable-threads-rows GEMV kernels for the set. This may include ConstN kernels (e.g., constant-N variable-threads-rows GEMV kernels), and VarN kernels (e.g., variable-N variable-threads-rows GEMV kernels).

The embodiment of the invention provides an upper triangular part storage device of a symmetric matrix and a parallel reading method, and the device comprises a storage module selection circuit whichis used for selecting a storage module corresponding to each element of the upper triangular part of the symmetric matrix to be accessed; the address generation circuit is used for calculating a logicaddress of each element of the triangular part on the symmetric matrix to be accessed in the corresponding storage module; the m parallel storage modules are used for storing data corresponding to elements of the triangular part on the symmetric matrix to be accessed; and the data shuffling module is used for performing shuffling operation on the data read from the storage module. According to the embodiment of the invention, only the upper triangular part of the symmetric matrix needs to be stored, any row vector and any column vector of the symmetric matrix are read and recovered in parallel, and a parallel computing unit of hardware can be fully utilized, so that the algorithm efficiency of symmetric matrix operation can be improved to an algorithm efficiency level of general matrix operation.

The invention relates to a multi-energy system general matrix modeling method based on a graph theory and network flow, which is characterized by comprising the following steps of: firstly, providing a group of new definitions to construct a unified energy flow network of the multi-energy system including energy storage; secondly, giving a matrix for describing a topological structure and energy conversion characteristics of the multi-energy system; on the basis, elaborating an energy flow equation matrix modeling process; and then, applying matrix analysis to obtain a coupling matrix. The method is suitable for an MES with a relatively simple structure and is also suitable for processing a system with a plurality of energy distribution devices or conversion devices in a topological structure; a sparse matrix is adopted for both a simple system and a complex system, so that the calculation amount is small. The method has the advantages of scientificity, reasonability, high practicability and good effect.

The invention discloses an error-controllable hybrid precision operator automatic optimization method, which comprises the following steps of: first optimization: under given input, optimizing a general matrix multiplication function through an ID (Identity) conversion method, and recording an operation sequence and a corresponding dimension of the general matrix multiplication function; second optimization: according to an optimized general matrix multiplication GEMM function ID list and a general matrix multiplication function sequence list, transmitting the input of each non-optimized general matrix multiplication calling function to a preset fast matrix multiplication function of different parameter combinations, and carrying out performance timing and error calculation; and third optimization: analyzing the obtained data, converting a single-precision matrix multiplication algorithm into a mixed-precision matrix multiplication operator, and outputting an optimized code. By using the method, the hybrid precision GEMM operator can help to improve the performance in a more complex and wider high-performance computing program. The method can be widely applied to the field of compilers.

The invention discloses a convolutional neural network weight gradient optimization method based on a data stream, and provides a configurable data stream architecture design for convolutional neural network weight gradient optimization, so that convolution operations of different sizes in weight gradient calculation can be supported, and the degree of parallelism is K * K (convolution kernel size) times that of serial input. The training performance of the whole convolutional neural network is improved, and the problem that convolution operations of different sizes are difficult to realize in weight gradient calculation is solved. Compared with the prior art, the method has the advantages that (1) the acceleration effect is obvious: for weight gradient calculation, the degree of parallelism is improved by K * K compared with that of an original serial scheme, and the transmission time of data input is remarkably reduced, so that the purpose of accelerating the whole network training is achieved, and 1-1/(K * K)% of input storage can be reduced compared with a general matrix multiplication scheme; and 2) the applicability and the universality are simultaneously met.

The invention discloses a method for rapid implementation of a matrix switch graphical control system. The method adopts a switch control middleware, a graphical matrix control and a switch driving program, and comprises the following steps: performing operation environment configuration, and using the graphical matrix control and the switch control middleware to build a general system frame; defining a general matrix switch functional interface, wherein switch driving programs of matrix switches realize the function of definition of the general matrix switch functional interface, and using the general system frame to load the switch driving program to generate the matrix switch graphical control system; utilizing the switch control middleware to realize the processes of obtaining switch topology information from the switch driving program and building a two-dimensional graphical matrix; and utilizing the switch control middleware to realize the process of performing switch module operation via mouse operation on the two-dimensional graphical matrix. According to the invention, the development and maintenance difficulties of the matrix switch graphical control system are reduced, and the development cycle of a new product is shortened.