Performance models provide insightful perspectives to predict performance and to propose opti-mization guidance.Although there has been much researches, pinpointing bottlenecks of various memory access patts and reaching high accurate prediction of both regular and irregular programs on various hardware configurations are still not trivial.This work proposes a novel model called process-RAM-feedback ( PRF) to quantify the overhead of computation and data transmission time on general-purpose multi-core processors.The PRF model predicts the cost of instruction for single-core by a directed acyclic graph ( DAG) and the transmission time of memory access between each memory hierarchy through a newly designed cache simulator.By using performance modeling and feedback optimization method, this paper uses PRF model to analyze and optimize convolution, sparse matrix-vector multiplication and sn-sweep as case study for covering with typical regular ker-nel to irregular and data dependence.Through the PRF model, it obtains optimization guidance with various sparsity structures, algorithm designs, and instruction sets support on different data sizes.