This paper presents a parallelization framework for emerging applications on the future chip multiprocessors (CMPs). With the continuing prevalence of CMP and the number of on-die cores increasing steadily for the foreseeable future, one key issue in harnessing the computation power of such a CMP is how to effectively manage and execute many threads at the same time. Hence, we study a parallelization framework, which includes (1) coarse-grain and fine-grain multi-threading, (2) performance analysis, and (3) algorithms changes. In particular, this paper shows how the Hough Transform can be parallelized, as an example. Starting with a sports soccer analysis workload that heavily uses Hough Transform to detect lines in sports soccer field, we extract the coarse-grain data-level parallelism and examine its scaling performance on an 8-core symmetric multiprocessor machine. After realizing the parallel performance limiting factors, we target to exploit the fine-grain data-level parallelism ...