Vector Conflict Detection for Scalar-to-SIMD Conversion
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Solution Overview
Problem
Existing processor architectures face challenges in efficiently converting scalar processes to SIMD processes while preserving scalar program order due to data dependencies and memory conflicts, which hinders operational improvements and resource utilization.
Innovation Solution
The implementation of a processor core architecture that dynamically detects memory conflicts using vector-processing operations, allowing for the partitioning of loop iterations into ranges with no conflicts, enabling vectorization while maintaining proper memory ordering through the use of conflict detection logic and instruction formats that support bit-masking and vector comparison operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If scalar processes are converted to SIMD processes to improve processing speed and resource utilization, then productivity is improved, but reliability deteriorates due to potential violations of scalar program order from data dependencies and memory conflicts
Solution Approach 1:
The system performs preliminary conflict detection by comparing memory addresses in the first vector against memory addresses in the second vector before executing the vectorized loop. This preliminary action identifies potential conflicts that would violate scalar program order, allowing the system to mask or serialize conflicting iterations while maintaining parallel execution of non-conflicting iterations, thus resolving the contradiction between improved productivity and preserved reliability
2Loss of time
If vectorization is applied to process multiple data elements simultaneously to improve productivity, then processing time is reduced, but device complexity increases due to the need for conflict detection logic and instruction formats
Solution Approach 1:
The system segments the vectorization process into distinct phases: conflict detection phase (comparing memory addresses using vector comparison instructions), conflict masking phase (generating mask vectors to identify conflicting iterations), and execution phase (executing vectorized operations with masking applied). This segmentation allows the use of existing vector instruction capabilities while adding conflict detection functionality, reducing the increase in device complexity compared to a fully integrated approach
3Reliability
If memory conflicts are detected and managed to preserve scalar program order, then reliability is improved, but productivity decreases due to reduced parallelism
Solution Approach 1:
The system applies local quality by generating a mask vector where each element independently indicates whether the corresponding iteration is conflicting or non-conflicting. This allows different quality treatments for different iterations: conflicting iterations are serialized or masked to preserve program order, while non-conflicting iterations execute in parallel with full vectorization. This local differentiation maintains reliability for critical operations while preserving productivity through parallel execution of safe operations
Data Source
Figure 1A~1B
Figure 2A~2B
Figure 3
AI summary
In one embodiment vector conflict detection instructions are disclosed to perform dynamic memory conflict detection within a vectorized iterative scalar operation. The instructions may be performed by a vector processor to generate a partition vector identifying groups of conflict free iterations. The partition vector may be used to generate a write mask for subsequent vector operations.