Matrix Operand Memory Handles for Linear Algebra Efficiency
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Solution Overview
Problem
Current systems are inefficient in executing linear algebra operations due to high processing overhead and complexity, especially when dealing with large, multi-dimensional matrix operands, as they are designed for low arithmetic intensity operations and utilize complex memory management routines.
Innovation Solution
The solution involves partitioning matrix operands into sub-matrices and distributing them across multiple processing units, using a controller circuitry to manage memory handles and execute matrix operations efficiently, thereby reducing memory allocation and subroutine complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If systems are designed for low arithmetic intensity operations, then general-purpose computing is achieved, but linear algebra operations execute inefficiently
Solution Approach 1:
The patent segments large matrix operands into smaller sub-matrices that can be processed in parallel by multiple processing units. This segmentation allows the system to maintain general-purpose computing capability while achieving high efficiency for linear algebra operations by dividing the computation into manageable chunks that exploit parallel processing.
Solution Approach 2:
The patent introduces a new dimension of parallel processing by distributing sub-matrix operations across multiple processing units simultaneously. This dimensional expansion from sequential to parallel execution enables the system to achieve high arithmetic intensity for linear algebra operations without sacrificing general-purpose computing versatility.
2Ease of operation
If complex local memory management routines are used for large matrix operands, then memory access is managed, but processing overhead and execution complexity increase
Solution Approach 1:
By segmenting large matrix operands into smaller sub-matrices, the patent reduces the memory management burden on each processing unit. Each unit handles only its assigned sub-matrix, eliminating the need for complex global memory management routines and reducing processing overhead significantly.
Solution Approach 2:
Each processing unit independently manages its own sub-matrix operations without requiring complex coordination with other units. This self-service approach simplifies memory management by localizing access patterns and eliminating the need for sophisticated cache management routines.
3Device complexity
If matrix operands are processed as whole units, then operation simplicity is maintained, but parallel processing capability is underutilized
Solution Approach 1:
The patent segments matrix operands into sub-matrices that can be independently processed in parallel. This segmentation maintains operational simplicity at the sub-matrix level while enabling efficient parallel processing across multiple units, resolving the contradiction between simplicity and parallel efficiency.
Solution Approach 2:
The patent merges the results of multiple parallel sub-matrix operations to produce the final result. This combining approach allows the system to maintain simple individual operations while achieving high parallel processing efficiency through the coordinated execution of multiple processing units.
Data Source
AI summary
Described herein are methods, systems, and apparatuses to utilize a matrix operation by accessing each of the operation's matrix operands via a respective single memory handle. This use of a single memory handle for each matrix operand eliminates significant overhead in memory allocation, data tracking, and subroutine complexity present in prior art solutions. The result of the matrix operation can also be accessible via a single memory handle identifying the matrix elements of the result.