Abstract: To satisfy the requirements for exploiting various types of available parallelism in destination programs on multicore platforms fully and efficiently, an extended TStreams model was developed by introducing the hierarchical relationship, equivalent relationship and characteristic weights as the support, so as to implement a declarative parallelism based feature aware parallel optimization framework (FAPOF) which is on the basis of the model. FAPOF supports multi-aspects and multi-grains description for the latent parallelism and appropriate optimization rules available in destination algorithms. With users' description, FAPOF can evaluate the different combinations of the optimization decisions in a compiler-driven method, thus the destination programs can be optimized in parallel semi-automatically. In this way, the programmers' complex and difficult ″decision-making″ jobs in parallel optimization will be transformed into an easier ″description″ job for available parallelism. Test results showed that the method mentioned above can reduce the difficulty in parallel optimization significantly with the optimization efficiency improved.