Optimizing Performance of OpenMP Parallel Applications through Variable Classification

Abstract

OpenMP provides a versatile framework for parallel computing, allowing developers to transform sequential programs into parallel applications for shared-memory architectures efficiently. One of the central challenges in this transformation lies in accurately identifying appropriate parallel constructs and clauses, which are critical for maximizing performance and ensuring the correctness of the resulting parallel code. A particularly intricate aspect of this process is the classification of variables according to their data-sharing semantics, including first-private, private, last-private, shared, and reduction clauses. Manual classification is laborintensive and significantly susceptible to errors as the program's scale and complexity grow. Although various tools have been developed to assist with variable classification, they often rely on extensive data-dependence analyses and rigid classification schemes, limiting their effectiveness when applied to large-scale programs with complex scoping requirements. This paper presents a novel, cost-effective approach to automate and enhance the accuracy of variable classification in OpenMP parallelization. By reducing the manual effort required and improving the precision of parallel construct insertion, this approach aims to significantly optimize the performance of parallel applications, thereby advancing the utility and accessibility of OpenMP for a wide range of computational tasks. © 2024 IEEE.