A Technique for Automated Parallel Optimization of Function Calls in C++ Code

In modern software development, achieving high performance increasingly relies on effective parallelization. While much of the existing research has focused on loop-level parallelism, function-level parallelization remains relatively underutilized. Yet, in many real-world applications, function calls serve as natural units of computation that could greatly benefit from concurrent execution. To address this gap, we present an automated tool that analyzes sequential C++ code, identifies independent function calls, and evaluates their suitability for parallel execution. The tool performs three key analyses: dependency analysis to detect function calls, context analysis to understand execution conditions, and workload assessment to determine whether parallelization would result in significant performance benefits. Based on the analysis results, the tool transforms eligible function calls into parallel equivalents without altering the original program logic. Additionally, the tool generates detailed Control Flow Graphs (CFG) for each function in three formats, facilitating further structural analysis. Three benchmark programs were used in experimental testing. The evaluation measured both sequential and parallel execution times, along with the computed performance gain expressed as a percentage reduction in runtime. Results demonstrated the tool’s ability to improve execution efficiency and reduce processing time. These outcomes emphasize the tool’s role in advancing function-level automatic parallelization. The tool showed notable performance improvements across the three benchmark applications, with the Employee Performance System achieving the highest improvement of 54.6%, followed by the Genomic Sequence System at 48.3%, and the Book Reviews System achieving an improvement of 36.1%. Demonstrating the tool’s ability to improve efficiency via automated function-level parallelization.