Architecture independent parallel selection with applications to parallel priority queues

AbstractWe present a randomized selection algorithm whose performance is analyzed in an architecture independent way on the bulk-synchronous parallel (BSP) model of computation along with an application of this algorithm to dynamic data structures, namely parallel priority queues. We show that our algorithms improve previous results upon both the communication requirements and the amount of parallel slack required to achieve optimal performance. We also establish that optimality to within small multiplicative constant factors can be achieved for a wide range of parallel machines. While these algorithms are fairly simple themselves, descriptions of their performance in terms of the BSP parameters is somewhat involved; the main reward of quantifying these complications is that it allows transportable software to be written for parallel machines that fit the model

Randomized priority queues for fast parallel access

Applications like parallel search or discrete event simulation often assign priority or importance to pieces of work. An effective way to exploit this for parallelization is to use a priority queue data structure for scheduling the work; but a bottleneck free implementation of parallel priority queue access by many processors is required to make this approach scalable. We present simple and portable randomized algorithms for parallel priority queues on distributed memory machines with fully distributed storage. Accessing O(n) out of m elements on an n-processor network with diameter d requires amortized time O(d + log m/n) with high probability for many network types. On logarithmic diameter networks, the algorithms are as fast as the best previously known EREW-PRAM methods. Implementations demonstrate that the approach is already useful for medium scale parallelism