The availability of electronic data is revolutionizing how governments, businesses, and organizations make decisions. But the idea of collecting demographic data is not new. For example, the United States Constitution has required since 1789 that a census be performed every 10 years. In this project, you will process some data from the 2010 census in order to answer efficiently certain queries about population density. These queries will ask for the population in some rectangular area of the country. The input consists of "only" around 220,000 data points, so any desktop computer has plenty of memory. On the other hand, this size makes using parallelism less compelling (but nonetheless required and educational).
You will implement the desired functionality in several ways that vary in their simplicity and efficiency. Some of the ways will require fork-join parallelism and, in particular, Java's ForkJoin Framework. Others are entirely sequential. The last (not necessarily best) approach uses explicit threads, a shared data structure, and lock-based synchronization.
A final portion of this project involves comparing execution times for different approaches and parameter settings. You will want to write scripts to collect timing data for you, and you will want to use a machine that has at least 4 processors.
This project is an experiment where much of the coding details and experimentation are left to you, though we will describe the algorithms you must use. Will parallelism help or hurt? Does it matter given that most of your code runs only in a pre-processing step? The answers may or may not surprise you, but you should learn about parallelism along the way.
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