SIMD Stride Predicate Creation for Low-Overhead Vector Execution
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Solution Overview
Problem
The computational overhead of generating predicates for varying strides in SIMD processors leads to increased processing times and resource consumption, offsetting the performance gains from using SIMD processing capabilities.
Innovation Solution
Pre-calculating predicates of sub-vector width in sequences and applying them to the current vector, leveraging pre-established patterns based on the previously used pattern, rather than recalculating each predicate position for every vector throughout instruction execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If predicates are generated for varying strides in SIMD processors, then selective processing capability is improved, but computational overhead and processing time increase
Solution Approach 1:
The patent pre-calculates predicate patterns for different stride lengths and stores them in a lookup table before actual processing. When processing interleaved data, the system simply retrieves the pre-computed predicate pattern corresponding to the current stride length, avoiding real-time calculation overhead. This preliminary preparation resolves the contradiction by enabling selective processing capability while minimizing processing time through efficient pattern retrieval.
2Adaptability or versatility
If predicates are generated for varying strides in SIMD processors, then selective processing capability is improved, but resource consumption increases
Solution Approach 1:
The patent pre-calculates and stores predicate patterns in a lookup table during system initialization or idle periods. During actual data processing, the system only performs memory retrieval operations to obtain the appropriate predicate pattern, significantly reducing computational resource consumption. This approach maintains full selective processing capability while minimizing energy usage during critical processing operations.
3Manufacturing precision
If predicate calculation is performed for every vector, then processing accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent pre-computes all possible predicate patterns for different stride lengths and stores them in a lookup table. During processing, the system retrieves the appropriate pre-computed pattern based on the current stride length, ensuring processing accuracy is maintained while reducing computational complexity from O(n) to O(1) per vector operation.
Solution Approach 2:
The patent creates a lookup table that contains copies of pre-calculated predicate patterns for various stride lengths. Instead of recalculating predicates for each vector, the system copies the appropriate pre-computed pattern from the lookup table, maintaining accuracy while significantly reducing computational complexity during execution.
Data Source
AI summary
System and techniques for creating a single-instruction multiple-data (SIMD) processor predicate based on stride length are described herein. When an instruction for a SIMD processor is received, and the instruction has a specified stride length, a predicate memory can be read to obtain a current predicate. A new predicate can be determined based on the stride length and the current predicate. The new predicate is written to the predicate memory. When an instance of the instruction is executed by the SIMD processor, the execution is performed on a subset of data loaded into the SIMD processor based on the new predicate read from the predicate memory.