Join is the most important and expensive operation in relational databases. The parallel join operation is very sensitive to the presence of the data skew. In this paper we present two new parallel join algorithms for coarse grained machines which work optimally in presence of arbitrary amount of data skew. The first algorithm is sort-based and the second is hash-based. Both of these algorithms employ a preprocessing phase to equally partition the work among the processors. These algorithms are shown to be theoretically as well as practically scalable.

Integer sorting is a subclass of the sorting problem where the elements have integer values and the largest element is polynomially bounded in the number of elements to be sorted. It is useful for applications in which the size of the maximum value of element to be sorted is bounded. In this paper, we present a new distributed radix-sort algorithm for integer sorting. The structure of our algorithm is similar to radix sort except that it typically requires less number of communication phases. We present experimental results for our algorithm on two distributed memory multiprocessors, the Intel Paragon and the Thinking machine CM-5. These results are compared with two other well known practical parallel sorting algorithms based on radix sort and sample sort. The experimental results show that the distributed radix-sort is competitive with the other two algorithms.