Graph application is an important branch in the field of big data. Although graph analysis has more significant performance advantages than traditional relational databases in displaying the relationship between entities, the irregular memory access pattern caused by a large number of random accesses in graph processing destroys the time and space locality of memory access, thus causing great performance pressure on the off-chip memory system. Therefore, how to correctly measure the performance of graph application in memory system is crucial for efficient graph application architecture optimization. As an extension of average memory access time (AMAT), concurrent average memory access time (C-AMAT) takes into account the locality and concurrency of memory access, and can more accurately evaluate and analyze the performance of modern processors in the storage system. However, the C-AMAT model ignores the fact that the lower-level cache layer of the processor accesses serially, which will lead to the inaccuracy of the calculation. At the same time, it is difficult to obtain the parameters required for the calculation due to the “pure miss cycle” and other reasons, which also makes it difficult for C-AMAT to be applied in practice. In order to match the computing model of C-AMAT with the memory access mode in modern computers, we proposed parallel C-AMAT (PC-AMAT) and serial C-AMAT (SC-AMAT) based on C-AMAT. PC-AMAT and SC-AMAT respectively extended and characterized the computing model of C-AMAT from the parallel and serial access modes of cache. On this basis, we designed and implemented a “pure miss cycle” extraction algorithm to avoid the huge hardware overhead caused by direct measurement. The experimental results show that the correlation between PC-AMAT and SC-AMAT and IPC is stronger than that of C-AMAT in single-core and multi-core mode. Finally, PC-AMAT and SC-AMAT are used to measure and analyze the memory performance of graph application, and the optimization strategy of graph application access is proposed based on them.