Applying Idealized Lower-Bound Runtime Models to Understand Inefficiencies in Data-Intensive Computing

摘要

"Data-intensive scalable computing" (DISC) refers to a rapidly growing style of computing characterized by its reliance on large and expanding datasets [3]. Driven by the desire and capability to extract insight from such datasets, DISC is quickly emerging as a major activity of many organizations. Map-reduce style programming frameworks such as MapReduce [4] and Hadoop [1] support DISC activities by providing abstractions and frameworks to more easily scale data-parallel computations over commodity machines.