  In the research area of parallel computation, the communication cost has been
extensively studied, while the IO cost has been neglected. For big data
computation, the assumption that the data fits in main memory no longer holds,
and external memory must be used. Therefore, it is necessary to bring the IO
cost into the parallel computation model. In this paper, we propose the first
parallel computation model which takes IO cost as well as non-uniform
communication cost into consideration. Based on the new model, we raise several
new problems which aim to minimize the IO and communication cost on the new
model. We prove the hardness of these new problems, then design and analyze the
approximate algorithms for solving them.
