Matrix Multiply Instruction Register Utilization
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Solution Overview
Problem
Current data processing apparatuses face inefficiencies in performing matrix multiplication operations, particularly in terms of computation density and register utilization, as they often rely on vector-by-element approaches that require multiple operations for each element of the result matrix, leading to suboptimal performance compared to treating register contents as matrices directly.
Innovation Solution
The implementation of a matrix multiply instruction that extracts matrices from source registers and performs dot product operations to generate a square matrix of result data elements, which are then applied to a destination register, enhancing computation density by initiating more multiply-accumulate operations for the same register accesses, and allowing flexibility in bit lengths and matrix dimensions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If vector-by-element approach is used for matrix multiplication, then register utilization is maintained, but computation density decreases and performance is suboptimal
Solution Approach 1:
The patent transforms the traditional vector-by-element approach into a matrix-by-matrix operation by reinterpreting register contents as 2D matrix structures. This dimensional change allows the execution unit to perform multiple dot product operations simultaneously, extracting matrices from source registers and computing result matrices through parallel dot products, thereby significantly increasing computation density without additional hardware resources.
2Productivity
If multiple dot product operations are performed for each result matrix element, then computation density increases, but the number of required operations increases
Solution Approach 1:
The patent merges multiple dot product operations into a single matrix multiplication instruction. By treating register contents as matrices and performing matrix-by-matrix multiplication, the execution unit combines what would traditionally require multiple separate vector operations into one unified operation, thereby increasing computational throughput without proportionally increasing the number of instruction cycles.
Solution Approach 2:
The patent enables continuous utilization of the execution unit by allowing it to perform multiple dot product operations in sequence without intervening instruction fetch or decode cycles. The matrix multiplication instruction keeps the execution pipeline full by continuously performing useful computational work across all elements of the result matrix, maximizing resource utilization and reducing idle time.
3Productivity
If register contents are treated as matrices directly, then computation density improves, but flexibility in handling different data formats decreases
Solution Approach 1:
The patent employs parameter changes to handle different data formats and matrix dimensions. The execution unit dynamically adjusts the interpretation of register contents based on parameters such as element size (e.g., 32-bit vs. 64-bit), matrix dimensions, and data layout. This allows the same hardware structure to efficiently process various matrix multiplication scenarios by changing operational parameters rather than requiring different hardware configurations.
Data Source
AI summary
Techniques for performing matrix multiplication in a data processing apparatus are disclosed, comprising apparatuses, matrix multiply instructions, methods of operating the apparatuses, and virtual machine implementations. Registers, each register for storing at least four data elements, are referenced by a matrix multiply instruction and in response to the matrix multiply instruction a matrix multiply operation is carried out. First and second matrices of data elements are extracted from first and second source registers, and plural dot product operations, acting on respective rows of the first matrix and respective columns of the second matrix are performed to generate a square matrix of result data elements, which is applied to a destination register. A higher computation density for a given number of register operands is achieved with respect to vector-by-element techniques.