Vector Sort Indexing With Tile Operations for Large Matrix Throughput
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Solution Overview
Problem
Existing processors face challenges in efficiently handling large matrices due to instruction intensity and difficulty in managing rows of matrices within packed data registers, which is not feasible with larger matrices.
Innovation Solution
Implementing mechanisms to support matrix operations in computer hardware using 2-dimensional data structures called tiles, which can be configured for various dimensions and datatypes, and utilizing instructions for performing ternary tile operations (TILETERNOP) to facilitate efficient matrix manipulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional packed data registers are used to store matrix rows, then the processor can handle smaller matrices efficiently, but it becomes infeasible to manage larger matrices
Solution Approach 1:
The patent divides the matrix into smaller sub-matrices called tiles that can be independently processed. Each tile fits within standard packed data registers, allowing the system to handle large matrices by segmenting them into manageable units. This segmentation enables efficient processing of large matrices without overwhelming the processor's register architecture.
Solution Approach 2:
The patent introduces a tile-based dimension to matrix storage and processing, transitioning from traditional row-by-row processing to a two-dimensional tile grid. This allows the processor to manage large matrices by organizing them into a grid of smaller tiles, each可 be processed independently using standard register operations.
2Productivity
If traditional row-by-row processing is used, then the instruction set remains simple, but the processing throughput for large matrices is limited
Solution Approach 1:
The patent combines multiple operations into single tile-based instructions, allowing the processor to perform multiple operations on entire tiles simultaneously. This merging of operations at the tile level significantly increases throughput for large matrices while keeping individual instructions relatively simple.
Solution Approach 2:
The patent creates a universal tile processing model that can handle various matrix operations (addition, multiplication, transposition, etc.) using the same basic tile architecture. This multi-functionality allows the same tile-based approach to process different types of operations without requiring separate complex instruction sets for each operation type.
3Productivity
If the processor handles all operations in software, then hardware complexity is reduced, but performance is insufficient for modern machine learning workloads
Solution Approach 1:
The patent introduces tile registers as an intermediary between memory and the execution units. These specialized registers are optimized for holding tile data and facilitate efficient data transfer and processing. The tile register architecture serves as a mediator that bridges the gap between software programming models and hardware execution, enabling high performance without requiring complete hardware redesign.
Data Source
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Figure 2(A)~2(C)
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AI summary
Disclosed embodiments relate to apparatuses, systems, and methods for performing sort indexing and/or permutation using an index. An exemplary apparatus or processor comprises: decode circuitry to decode an instruction, the instruction to include a first field to identify a location of a source vector, a second field to identify a location of a destination vector, and an opcode to indicate to execution circuitry to execute the decoded instruction to index values of the source vector and store a result of the indexing in the destination vector by generating, per each element of the source vector, an index value using one or more comparisons; and execution circuitry to execute the decoded instruction as indicated by the opcode.