A Framework for Data Partitioning for C++ Data-Intensive Applications

Abstract

We present an automated framework that partitions the code and data types for the needs of data management in an object-oriented source code. The goal is to identify the crucial data types from data management perspective and separate these from the rest of the code. In this way, the design complexity is reduced allowing the designer to easily focus on the important parts of the code to perform further refinements and optimizations. To achieve this, static and dynamic analysis is performed on the initial C++ specification code. Based on the analysis results, the data types of the application are characterized as crucial or non-crucial. Continuing, the initial code is rewritten automatically in such a way that the crucial data types and the code portions that manipulate them are separated from the rest of the code. Experiments on well-known multimedia and telecom applications demonstrate the correctness of the performed automated analysis and code rewriting as well as the applicability of the introduced framework in terms of execution time and memory requirements. Comparisons with Rational's QuantifyTM suite show the failure of QuantifyTM to analyze correctly the initial code for the needs of data management.