PageRank is a fundamental link analysis algorithm that also functions as a key representative of the performance of Sparse Matrix-Vector (SpMV) multiplication. The traditional PageRank implementation generates fine granularity random memory accesses resulting in large amount of wasteful DRAM traffic and poor bandwidth utilization. In this paper, we present a novel Partition-Centric Processing Methodology (PCPM) to compute PageRank, that drastically reduces the amount of DRAM communication while achieving high sustained memory bandwidth. PCPM uses a Partition-centric abstraction coupled with the Gather-Apply-Scatter (GAS) programming model. By carefully examining how a PCPM based implementation impacts communication characteristics of the algorithm, we propose several system optimizations that improve the execution time substantially. More specifically, we develop (1) a new data layout that significantly reduces communication and random DRAM accesses, and (2) branch avoidance mechanisms to get rid of unpredictable data-dependent branches. We perform detailed analytical and experimental evaluation of our approach using 6 large graphs and demonstrate an average 2.7x speedup in execution time and 1.7x reduction in communication volume, compared to the state-of-the-art. We also show that unlike other GAS based implementations, PCPM is able to further reduce main memory traffic by taking advantage of intelligent node labeling that enhances locality. Although we use PageRank as the target application in this paper, our approach can be applied to generic SpMV computation.