Most client-side applications running on multicore processors are likely to be irregular programs that deal with complex, pointer-based data structures such as large sparse graphs and trees. However, we understand very little about the nature of parallelism in irregular algorithms, let alone how to exploit it effectively on multicore processors.

In this paper, we show that, although the behavior of irregular algorithms can be very complex, many of them have a generalized data-parallelism that we call amorphous data-parallelism. The algorithms in our study come from a variety of important disciplines such as data-mining, AI, compilers, networks, and scientific computing. We also argue that these algorithms can be divided naturally into a small number of categories, and that this categorization provides a lot of insight into their behavior. Finally, we discuss how these insights should guide programming language support and parallel system implementation for irregular algorithms.