Instruction Dependency Markers for Producer-Consumer Tracking
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Solution Overview
Problem
Existing data processing systems struggle to efficiently identify and manage producer-consumer relationships between instructions, particularly when these relationships are indirect or complex, leading to inefficiencies in data processing operations.
Innovation Solution
A data processing apparatus with training and dependency tracking circuitry to identify and propagate dependency markers for producer-consumer relationships, allowing for the selection of candidate consumer instructions based on set dependency markers, thereby improving data processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If traditional data processing systems are used to identify producer-consumer relationships, then the system structure remains simple, but the ability to identify complex and indirect dependencies is insufficient
Solution Approach 1:
The patent introduces dependency markers as intermediary elements that mediate between producer instructions and consumer instructions. These markers are set by producer instructions and propagated through the data processing apparatus, enabling consumer instructions to be identified without requiring complex direct analysis of all instruction relationships. The training circuitry uses these markers to learn and store producer-consumer relationships in the relationship table.
Solution Approach 2:
The data processing apparatus performs self-learning through the training circuitry, which automatically analyzes observed instructions, evaluates candidate producer-consumer relationships, and populates the relationship table without external intervention. The dependency tracking circuitry then uses this learned knowledge to automatically identify and propagate dependencies during normal operation, making the system self-sufficient in managing complex relationships.
2Productivity
If dependency tracking circuitry is added to propagate dependency markers, then data processing efficiency is improved through earlier prefetch initiation, but the device complexity increases
Solution Approach 1:
The dependency tracking circuitry sets dependency markers at the point of producer instruction execution, performing preliminary marking before consumer instructions are executed. This allows the system to proactively identify and prepare for consumer instructions that depend on producer data, enabling earlier prefetch operations and reducing waiting time for data availability.
Solution Approach 2:
The patent replaces complex mechanical or software-based dependency analysis mechanisms with a streamlined circuit-level implementation. The dependency tracking circuitry uses simple marker setting and propagation logic at the hardware level, which is more efficient than software polling or complex circuit analysis, thereby improving productivity with minimal complexity overhead.
3Measurement precision
If the relationship table stores multiple producer-consumer relationships, then the accuracy of consumer instruction identification is improved, but the memory requirements and table size increase
Solution Approach 1:
The training circuitry extracts only the essential and frequently occurring producer-consumer relationships from the observed instructions and stores them in the relationship table. Rather than storing all possible relationships, the system selectively captures the most relevant patterns, thereby improving identification accuracy for common cases while minimizing the storage requirements of the relationship table.
4Adaptability or versatility
If training circuitry is used to evaluate candidate relationships and populate the relationship table, then the system adapts to complex dependencies, but the initialization time and processing overhead increase
Solution Approach 1:
The training circuitry performs partial training by evaluating a limited set of candidate producer-consumer relationships rather than exhaustively analyzing all possible instruction pairs. The circuitry focuses on identifying the most probable and frequently occurring relationships within a reasonable time frame, accepting that not all relationships will be captured to avoid excessive training time and initialization overhead.
Data Source
AI summary
A relationship table stores a plurality of producer-consumer relationships defining associations between producers and consumers, wherein a source operand of a consumer is generated in dependence on producer data resulting from the producer. A candidate producer is selected and, based on subsequent candidate consumers, a candidate producer-consumer relationship is established and stored in the relationship table. A dependency marker is set in association with the producer data and a set dependency marker is propagated so as to be associated with result data values generated in data processing operations in dependence on the producer data. Candidate consumers are selected when they have at least one source operand that has a set dependency marker.