In the past 20 years there has been treftlendous progress in developing and analyzing parallel algorithftls. Researchers have developed efficient parallel algorithms to solve most problems for which efficient sequential solutions are known. Although some ofthese algorithms are efficient only in a theoretical framework, many are quite efficient in practice or have key ideas that have been used in efficient implementations. This research on parallel algorithms has not only improved our general understanding ofparallelism but in several cases has led to improvements in sequential algorithms. Unf:ortunately there has been less success in developing good languages f:or prograftlftling parallel algorithftls, particularly languages that are well suited for teaching and prototyping algorithms. There has been a large gap between languages

We present a collection of new techniques for designing and analyzing efficient external-memory algorithms for graph problems and illustrate how these techniques can be applied to a wide variety of specific problems. Our results include: ffl Proximate-neighboring. We present a simple method for deriving external-memory lower bounds via reductions from a problem we call the "proximate neighbors" problem. We use this technique to derive non-trivial lower bounds for such problems as list ranking, expression tree evaluation, and connected components. ffl PRAM simulation. We give methods for efficiently simulating PRAM computations in external memory, even for some cases in which the PRAM algorithm is not work-optimal. We apply this to derive a number of optimal (and simple) external-memory graph algorithms. ffl Time-forward processing. We present a general technique for evaluating circuits (or "circuit-like" computations) in external memory. We also use this in a deterministic list rank...

In this paper we develop an optimal cache-oblivious priority queue data structure, supporting insertion, deletion, and deletemin operations in O ( 1 B logM/B N) amortized memory B transfers, where M and B are the memory and block transfer sizes of any two consecutive levels of a multilevel memory hierarchy. In a cache-oblivious data structure, M and B are not used in the description of the structure. The bounds match the bounds of several previously developed external-memory (cache-aware) priority queue data structures, which all rely crucially on knowledge about M and B. Priority queues are a critical component in many of the best known external-memory graph algorithms, and using our cache-oblivious priority queue we develop several cacheoblivious graph algorithms.

In this thesis we study the Input/Output (I/O) complexity of large-scale problems arising e.g. in the areas of database systems, geographic information systems, VLSI design systems and computer graphics, and design I/O-efficient algorithms for them. A general theme in our work is to design I/O-efficient algorithms through the design of I/O-efficient data structures. One of our philosophies is to try to isolate all the I/O specific parts of an algorithm in the data structures, that is, to try to design I/O algorithms from internal memory algorithms by exchanging the data structures used in internal memory with their external memory counterparts. The results in the thesis include a technique for transforming an internal memory tree data structure into an external data structure which can be used in a batched dynamic setting, that is, a setting where we for example do not require that the result of a search operation is returned immediately. Using this technique we develop batched dynamic external versions of the (one-dimensional) range-tree and the segment-tree and we develop an external priority queue. Following our general philosophy we show how these structures can be used in standard internal memory sorting algorithms

this paper and supplied many helpful comments. This research was supported in part by NSF grants DCR-85-11713, CCR-86-05353, and CCR-88-14977, and by DARPA contract N00039-84-C-0165.

In the design of algorithms for large-scale applications it is essential to consider the problem of minimizing Input/Output (I/O) communication. Geographical information systems (GIS) are good examples of such large-scale applications as they frequently handle huge amounts of spatial data. In this note we survey the recent developments in external-memory algorithms with applications in GIS. First we discuss the Aggarwal-Vitter I/O-model and illustrate why normal internal-memory algorithms for even very simple problems can perform terribly in an I/O-environment. Then we describe the fundamental paradigms for designing I/O-efficient algorithms by using them to design efficient sorting algorithms. We then go on and survey external-memory algorithms for computational geometry problems -- with special emphasis on problems with applications in GIS -- and techniques for designing such algorithms: Using the orthogonal line segment intersection problem we illustrate the distribution-sweeping and ...

We give the first efficient parallel algorithms for recognizing chordal graphs, finding a maximum clique and a maximum independent set in a chordal graph, finding an optimal coloring of a chordal graph, finding a breadth-first search tree and a depth-first search tree of a chordal graph, recognizing interval graphs, and testing interval graphs for isomorphism. The key to our results is an efficient parallel algorithm for finding a perfect elimination ordering.

As most important applications today are large-scale in nature, high-performance methods are becoming indispensable. Two promising computational paradigms for large-scale applications are dynamic and I/O-efficient computations. We give efficient dynamic data structures for several fundamental problems in computational geometry, including point location, ray shooting, shortest path, and minimum-link path. We also develop a collection of new techniques for designing and analyzing I/O-efficient algorithms for graph problems, and illustrate how these techniques can be applied to a wide variety of specific problems, including list ranking, Euler tour, expression-tree evaluation, least-common ancestors, connected and biconnected components, minimum spanning forest, ear decomposition, topological sorting, reachability, graph drawing, and visibility representation. Finally, we present an extensive experimental study comparing the practical I/O efficiency of four algorithms for the orthogonal s...

Two improved list-ranking algorithms are presented. The "peeling-off" algorithm leads to an optimal PRAM algorithm, but was designed with application on a real parallel machine in mind. It is simpler than earlier algorithms, and in a range of problem sizes, where previously several algorithms where required for the best performance, now this single algorithm suffices. If the problem size is much larger than the number of available processors, then the "sparse-ruling-sets" algorithm is even better. In previous versions this algorithm had very restricted practical application because of the large number of communication rounds it was performing. This main weakness of this algorithm is overcome by adding two new ideas, each of which reduces the number of communication rounds by a factor of two. 1 Introduction A list is a basic data structure: it consists of nodes which are linked together, so that every node has precisely one predecessor and one successor, except for the initial n...

The broad thesis presented in this paper suggests that the serial emulation of a parallel algorithm has the potential advantage of running on a serial machine faster than a standard serial algorithm for the same problem. It is too early to reach definite conclusions regarding the significance of this thesis. However, using some imagination, validity of the thesis and some arguments supporting it may lead to several far-reaching outcomes: (1) Reliance on "predictability of reference" in the design of computer systems will increase. (2) Parallel algorithms will be taught as part of the standard computer science and engineering undergraduate curriculum irrespective of whether (or when) parallel processing will become ubiquitous in the generalpurpose computing world. (3) A strategic agenda for high-performance parallel computing: A multi-stage agenda, which in no stage compromises user-friendliness of the programmer 's...