Processor Tile Permutation for Matrix Efficiency
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Solution Overview
Problem
Mainstream processors face difficulties in efficiently handling large matrices due to the complexity and instruction intensity required for matrix operations, particularly in loading, processing, and storing data across packed registers and memory.
Innovation Solution
The implementation of matrix operations using 2-dimensional data structures referred to as 'tiles' that can be configured for different dimensions and data types, allowing for optimized processing through specialized instructions and hardware support, such as the matrix operations accelerator, which enables efficient matrix multiplication, addition, and other operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional processor architectures are used to handle large matrices, then general-purpose computing is maintained, but processing efficiency and performance deteriorate due to instruction intensity and complexity
Solution Approach 1:
The patent divides large matrices into smaller tile blocks that can be processed independently and in parallel. This segmentation allows the processor to handle manageable portions of data simultaneously, improving overall processing efficiency while reducing the complexity of individual operations.
Solution Approach 2:
The patent introduces a tile-based two-dimensional processing model that adds spatial organization to matrix operations. By organizing data into tiles with specific dimensions and using permute operations to rearrange tile positions, the system optimizes data access patterns and enables more efficient parallel processing.
2Speed
If data is loaded and stored across packed registers and memory using traditional methods, then data accessibility is maintained, but processing speed deteriorates due to computational overhead
Solution Approach 1:
The patent performs preliminary organization of matrix data into tile blocks before processing. By pre-configuring the tile structure and arranging data in optimized formats, the system reduces the need for complex data manipulation during processing, thereby decreasing computational overhead and improving processing speed.
Solution Approach 2:
The patent introduces tile blocks as intermediary structures between raw matrix data and processed results. These tiles serve as intermediate containers that facilitate efficient data movement between memory and registers, reducing the computational overhead associated with direct data access and manipulation.
3Productivity
If specialized matrix operations hardware is implemented, then processing performance is improved, but device complexity and configuration requirements increase
Solution Approach 1:
The patent designs a tile-based processing framework that can handle multiple matrix operations (multiplication, addition, transposition, permutation) using a unified approach. This universal tile structure can be configured for different operations without requiring separate specialized hardware for each function, thereby improving performance while managing complexity.
Solution Approach 2:
The patent employs dynamic tile configuration where tile dimensions, data types, and arrangement patterns can be adjusted based on the specific operation requirements. This dynamic adaptability allows the hardware to optimize for different matrix operations without being locked into a fixed configuration, balancing performance improvement with configuration flexibility.
Data Source
Figure 1A~1B
Figure 2(A)~2(C)
Figure 3
AI summary
Techniques for permuting two source two dimensional (2D) arrays are described. A processor of an aspect includes a decoder circuitry to decode an instruction having an opcode. The instruction may indicate a first source 2D array, a second source 2D array, and permute control elements. Execution circuitry is coupled with the decoder circuitry. The execution circuitry is to execute the decoded instruction to select data elements, from among any data elements of the first source 2D array, and any data elements of the second source 2D array, that are each indicated by a different corresponding one of the permute control elements. The execution circuitry is also to store the selected data elements in data element positions of a result 2D array that each correspond to a different one of the permute control elements. Other processors, methods, systems, and instructions are disclosed.