Predicate Monitoring Circuitry for Vector Processing Efficiency
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Solution Overview
Problem
Existing data processing technologies face challenges in efficiently controlling and optimizing vector processing circuitry, particularly in distinguishing between contiguous partial and sparse predicates, which affects vector utilization and processing efficiency.
Innovation Solution
The implementation of predicate monitoring circuitry that generates a predicate summary value based on bit values of a vector predicate, indicating whether a sparse condition is true or not, allowing for improved control and statistics gathering on vector processing operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If predicate monitoring circuitry is added to generate predicate summary values, then vector processing efficiency is improved through better resource allocation, but device complexity increases
Solution Approach 1:
The patent extracts only the essential predicate information (summary values indicating sparse/contiguous conditions) from the full vector predicate bit values, rather than monitoring and processing all predicate details. This extraction approach provides sufficient information for resource allocation decisions while avoiding the complexity of complete predicate analysis.
Solution Approach 2:
Instead of having complex software or hardware analyze the entire predicate structure to determine sparsity, the patent inverts the approach by having dedicated circuitry directly generate simplified summary values that immediately indicate the predicate condition type. This inversion transforms a complex analysis problem into a simple generation task.
2Productivity
If predicate summary values are generated to differentiate sparse and contiguous conditions, then vector utilization is optimized, but manufacturing precision requirements increase
Solution Approach 1:
The patent applies different processing approaches based on the local quality of the predicate pattern. By identifying whether a predicate is sparse or contiguous at the local level, the system can apply optimized processing strategies for each pattern type, improving overall vector utilization without requiring high-precision analysis of every individual bit.
Data Source
AI summary
Apparatuses and methods of operating such apparatuses are disclosed. Vector processing circuitry performs data processing in multiple parallel processing lanes, wherein the data processing is performed in a subset of the multiple parallel processing lanes determined by bit values of a vector predicate which are set. Predicate monitoring circuitry is responsive to the vector predicate to generate a predicate summary value in dependence on the bit values of the vector predicate. A first value of the predicate summary value indicates that a sparse condition is true for the vector predicate, the sparse condition being true when the bit values of the vector predicate comprise a set bit corresponding to a vector element at a higher index immediately followed by a non-set bit corresponding to a vector element at a lower index. A second value of the predicate summary value indicates that the sparse condition is not true for the vector predicate. Improved predicate controlled vector processing is thus supported.