Conditional Loop Restructuring for Lower Control Overhead
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Solution Overview
Problem
Existing loop optimization techniques such as loop splitting and full loop unrolling do not provide optimal performance enhancements in all situations due to increased program size and instruction buffer constraints, leading to inefficiencies in modern processor utilization.
Innovation Solution
A method involving generating a conditional tree, multi-dimensional table, and slicing it to create sequences with fixed strides, determining induction variables, and creating new conditionals to modify loops, reducing code size and control overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If loop splitting is used to optimize loop performance, then parallelism is enhanced and branch mispredictions are reduced, but program size increases and instruction buffer constraints are violated
Solution Approach 1:
The patent applies segmentation by dividing the loop optimization into two stages: generating multiple candidate optimized loops with different numbers of iterations, then selecting the best candidate. This avoids generating all possible segmentations simultaneously, reducing program size while still achieving performance optimization through selective loop splitting.
Solution Approach 2:
The patent uses partial action by generating candidate loops with partial unrolling (different iteration counts) rather than full unrolling. The compiler selects the optimal degree of unrolling based on performance metrics, avoiding the excessive program size growth that would result from complete loop unrolling while still achieving significant performance improvement.
2Productivity
If full loop unrolling is used to eliminate control overhead, then branch instructions are reduced and performance improves, but program size increases significantly
Solution Approach 1:
The patent implements partial loop unrolling by generating candidate loops with different unrolling factors (e.g., unroll by 2, 4, or 8 iterations). Instead of always applying full unrolling, the compiler evaluates multiple partial unrolling options and selects the one that provides the best performance-to-size ratio, thereby reducing control overhead without excessively inflating program size.
Solution Approach 2:
The patent changes the parameter of unrolling factor to generate multiple candidate versions. By varying this parameter and evaluating performance metrics for each candidate, the system finds the optimal unrolling degree that balances performance improvement against program size growth, avoiding both insufficient and excessive unrolling.
3Productivity
If multiple loop optimization candidates are generated, then optimal loop version can be selected, but complexity of loop modification process increases
Solution Approach 1:
The patent performs preliminary analysis of the loop structure and characteristics before generating optimization candidates. By pre-computing relevant metrics and identifying optimization opportunities in advance, the system reduces the complexity of the subsequent candidate generation and selection process, making the overall workflow more manageable and efficient.
Solution Approach 2:
The patent introduces an intermediary selection process that evaluates multiple candidate loops based on performance metrics and selects the optimal version. This intermediary layer simplifies the complexity by providing a systematic criterion for choosing among candidates, avoiding the need for complex manual analysis or trial-and-error optimization.
Data Source
AI summary
A computer implemented method for generating modified loops for loops with a number of conditionals is provided. A processor set generates a conditional tree based on the number of conditionals in the loops. A value is determined by evaluating each conditional in the number of conditionals The processor set generates a multi-dimensional table based on values from the number of conditionals. The processor set determines an induction variable for the modified loops. The processor set slices the multi-dimensional table to generate a number of slices. The processor set generates a number of sequences by splitting values in each slice. The processor set generates a number of new conditionals based on the determined induction variable and at least values and strides in the number of sequences. The processor set generates the modified loops based on the number of new conditionals.