A NOVEL AUTOMATIC C TO NVIDIACUDA CODE OPTIMIZATION FRAMEWORK

Abstract

With the continuous demand for high performance computing, the need for reducing time for executing the application is the current challenge of research. Nevertheless, the execution time for the application not only depends on the hardware or architecture, rather also depends on the algorithm design. Improvement of the hardware may lead to higher investments and the optimization of cost is also to be taken care. Henceforth, the major optimization task is to focus on the algorithm design. A number of algorithm design techniques are available and techniques have reached the maximum of optimization levels. Thus, not limiting to the improvement in the algorithm design, the use of parallel execution of the programs is also to be considered. GPUs are commonly used processing units to speed up the application execution in the domain of game development. The GPUs can be utilized to parallelize the application execution to reach the clock usage to the maximum. The major challenge is to design or re-design the application code from traditional serial programming languages to the parallel codes, which can take the advantages of GPU cores. Nonetheless, the code conversion is not easy and demands a higher understanding of parallel programming and the GPUs are transparent to understand for a beginner. Thus the final demand for the application development industry is to build a code conversion framework to automatically convert the source code into parallel programs. This work presents a novel C to NVIDIA Cuda code converter and gives the legacy programs a chance to run on parallel architecture. This work, to be presented, can be considered as a base line for further reach and be used for bench marking the applications. The results demonstrate a high reduction in execution time.