Distributed graph processing is mainstream but suffers from a few unavoidable issues, such as workload imbalancing and the debugging/optimizing difficulties in distributed programs. On the other hand, recent research results show that with a reasonable design of data structure and processing model, graph processing on a single PC can achieve comparable performance as the systems using large number of machines. For example, GraphChi on a single PC can achieve almost the same performance with Spark with 50 nodes. In this paper, a streamlined asynchronous graph processing model, ASP is proposed based on accumulated iterative model and external storage based parallel computing techniques. ASP relies on sequential disk access and allows asynchronous computations on the graph structure data. Based on ASP, a streamlined graph processing framework, S-Maiter is designed and implemented to provide high performance graph processing ability on a single PC. By optimizing I/O threading, memory monitoring, and shard-level priority scheduling, the performance of S-Maiter is greatly improved. Experimental results on a big graph dataset (13 million nodes and 500 million edges) show that, 1-node S-Maiter can achieve comparable performance with distributed Maiter with 16 nodes. Furthermore, S-Maiter is 1.5 times faster than the popular single-PC graph processing system GraphChi.