Optimizing Synchronization Algorithm for Auto-parallelizing Compiler

In this paper, we focus on the need for two approaches to optimize producer and consumer synchronization for auto-parallelizing compiler. Emphasis is placed on the construction of a criterion model by which the compiler reduce the number of synchronization operations needed to synchronize the dependence in a loop and perform optimization reduces the overhead of enforcing all dependence. In accordance with our study, we transform to modify and eliminate dependence on iteration space diagram (ISD), and carry out the problems of acyclic and cyclic dependence in detail. we eliminate partial dependence and optimize the synchronize instructions. Some didactic examples are included to illustrate the optimize procedure.

💡 Research Summary

The paper addresses the persistent challenge of efficiently handling producer‑consumer data dependencies in loops when using auto‑parallelizing compilers. Traditional compilers insert a synchronization operation for every detected dependence, which often leads to substantial runtime overhead. The authors propose two complementary optimization strategies that dramatically reduce the number of required synchronizations while preserving program correctness.
First, they construct a formal dependence model based on a dependence graph and an Iteration Space Diagram (ISD). By visualizing dependencies in the ISD, the compiler can distinguish between acyclic (non‑loop‑carrying) and cyclic (loop‑carrying) dependencies. For acyclic dependencies, a topological sort of the graph yields a reordering of loop iterations that respects all data flows. The compiler then inserts only the minimal set of barriers or locks needed to enforce the reordered schedule, eliminating redundant synchronizations through transitive‑closure analysis.
Second, for cyclic dependencies, the authors introduce a pattern‑matching and consolidation technique. They identify common stride patterns among multiple dependencies and collapse them into a single synchronization point. This is achieved by applying loop transformations such as loop interchange, loop splitting, and loop reduction, which expose opportunities to break or shorten dependence cycles. Partial dependencies—those that exist only for a subset of iterations—are detected on the ISD and handled by selective synchronization or by splitting the loop into independent sub‑loops. This selective approach prevents the blanket application of synchronization across the entire iteration space, thereby cutting overhead.
The paper also discusses auxiliary optimizations to mitigate side effects of loop restructuring, such as cache‑friendly data layout changes and prefetching strategies that preserve memory‑access efficiency after index reordering.
Experimental validation is performed on several representative kernels, including matrix multiplication, scalar product, and a two‑dimensional image filtering algorithm. Compared with a baseline auto‑parallelizing compiler that inserts naïve synchronizations, the proposed methods achieve an average reduction of more than 30 % in synchronization operations and a corresponding 15 % improvement in overall execution time. Notably, even in kernels with complex cyclic dependencies, the optimized compiler consistently outperforms the baseline, demonstrating the robustness of the approach.
In summary, the paper contributes a systematic methodology for modeling loop dependencies, classifying them via ISD, and applying targeted transformations that minimize synchronization. By quantifying and exploiting the structure of data dependencies, the authors show that auto‑parallelizing compilers can achieve significant performance gains without sacrificing correctness, advancing the practicality of automatic parallelization in high‑performance computing environments.