. We consider three paradigms of computation where the benefits of a parallel solution are greater than usual. Paradigm 1 works on a time-varying input data set, whose size increases with time. In paradigm 2 the data set is fixed, but the processors may fail at any time with a given constant probability. In paradigm 3, the execution of a single operation may require more than one processor, for security or reliability reasons. We discuss the organization of PRAM algorithms for these paradigms, and prove new bounds on parallel speed-up. 1 Introduction The theory of parallel algorithms has a well known body, developed on the PRAM model [5]. Some folklore principles are at the base of this theory, in particular the ones that express upper and lower bounds on the processing time. Let \Pi be a problem of size N , and let T s (N) be the time required by the best known sequential algorithm A s to solve \Pi . Any parallel algorithm A p that solves \Pi with a number P of PRAM processors...

Abstract. This work presents new algorithms for the "Do-All " problem that consists of performing t tasks reliably in a message-passing synchronous system of p fault-prone processors. The algorithms are based on an aggressive coordination paradigm in which multiple coordinators may be active as the result of failures. The first algorithm is tolerant of f < p stop-failures and it does not allow restarts. It has the available processor steps complexity S = O((t + plogp/loglogp), log f) and the message complexity M = O(t + plogp/loglogp + f • p). Unlike prior solutions, our algorithm uses redundant broadcasts when encountering failures and, for large f, it has better S complexity. This algorithm is used as the basis for another algorithm which tolerates any pattern of stop-failures and restarts. This new algorithm is the first solution for the Do-All problem that efficiently deals with processor restarts. Its available processor steps complexity is S = O((t + p log p + f). rain{log p, log f}), and its message complexity is M = O(t +p. logp + f.p), where f is the number of failures. 1

The MILAN project, a joint effort involving Arizona State University and NewYork University, has produced and validated fundamental techniques for the realization of efficient, reliable, predictable virtual machines on top of metacomputing environments that consist of an unreliable and dynamically changing set of machines. In addition to the techniques, the principal outcomes of the project include three parallel programming systems---Calypso, Chime, and Charlotte--- which enable applications be developed for ideal, shared memory, parallel machines to execute on distributed platforms that are subject to failures, slowdowns, and changing resource availability. The lessons learnt from the MILAN project are being used to design Computing Communities, a metacomputing framework for general computations. 1. Motivation MILAN (Metacomputing In Large Asynchronous Networks) is a joint project of Arizona State University and NewYork University. The primary objective of the MILAN project is to p...

We consider the problem of performing t tasks in a distributed system of p faultprone processors. This problem, called do-all herein, was introduced by Dwork, Halpern and Waarts. Our work deals with a synchronous message-passing distributed system with processor stop-failures and restarts. We present two new algorithms based on a new aggressive coordination paradigm by which multiple coordinators may be active as the result of failures. The first algorithm is tolerant of f < p stop-failures and it does not allow restarts. It has available processor steps (work) complexity S = O((t + p log p= log log p) log f) and message complexity M = O(t + p log p= log log p + fp). Unlike prior solutions, our algorithm uses redundant broadcasts when encountering failures and, for p = t and large f , it achieves better work complexity. This algorithm is used as the basis for another algorithm that tolerates stop-failures and restarts. This new algorithm is the first solution for the do-all problem that efficiently deals with processor restarts. Its available processor steps complexity is S = O((t + p log p + f) minflog p; log fg), and its message complexity is M = O(t+p log p+fp), where f is the total number of failures.

We consider the problem of asynchronous execution of parallel programs. We assume that the original program is designed for a synchronous system, whereas the actual system may be asynchronous. We seek an automatic execution scheme, which allows the asynchronous system to execute the synchronous program. Previous execution schemes provide solutions only for the case where the original program is deterministic. Here, we provide the first solution for the more general case where the original program can be nondeterministic (e.g. randomized). Our scheme is based on a novel agreement protocol for the asynchronous parallel setting. Our protocol allows n asynchronous processors to agree on n word-sized values in O(n log n log log n) total work. Total work is defined to be the summation of the number of steps performed by all processors (including steps from busy waiting). 1 Introduction Motivation. Parallel programs are frequently designed assuming tightly-coupled processors, operating in ...

The first half of the paper is a general introduction which emphasizes the central role that the PRAM model of parallel computation plays in algorithmic studies for parallel computers. Some of the collective knowledge-base on non-numerical parallel algorithms can be characterized in a structural way. Each structure relates a few problems and technique to one another from the basic to the more involved. The second half of the paper provides a bird's-eye view of such structures for: (1) list, tree and graph parallel algorithms; (2) very fast deterministic parallel algorithms; and (3) very fast randomized parallel algorithms. 1 Introduction Parallelism is a concern that is missing from "traditional" algorithmic design. Unfortunately, it turns out that most efficient serial algorithms become rather inefficient parallel algorithms. The experience is that the design of parallel algorithms requires new paradigms and techniques, offering an exciting intellectual challenge. We note that it had...

Java offers the basic infrastructure needed to integrate computers connected to the Internet into a seamless distributed computational resource: an infrastructure for running coarse-grained parallel applications on numerous, anonymous machines. First, we sketch such a resource's essential technical properties. Then, we present a prototype of Javelin, an infrastructure for global computing. The system is based on Internet software that is interoperable, increasingly secure, and ubiquitous: Java-enabled Web technology. Ease of participation is seen as a key property for such a resource to realize the vision of a multiprocessing environment comprising thousands of computers. Javelin's architecture and implementation require participants to have access to only a Java-enabled Web browser. Experimental results are given in the form of a Mersenne Prime application and a ray-tracing application that run on a heterogeneous network of several parallel machines, workstations, and PCs. Tw...

this paper, we propose a novel approach

The problem of performing t tasks on n asynchronous or undependable processors is a basic problem in parallel and distributed computing. We consider an abstraction of this problem called the WriteAl l problem---using n processors write 1's into all locations of an array of size t. The most e#cient known deterministic asynchronous algorithms for this problem are due to Anderson and Woll. The first class of algorithms has work complexity of O(t ), for n t and any #>0, and they are the best known for the full range of processors (n = t). To schedule the work of the processors, the algorithms use lists of q permutations on [q](q n) that have certain combinatorial properties. Instantiating such an algorithm for a specific # either requires substantial pre-processing (exponential in 1/# )to find the requisite permutations, or imposes a prohibitive constant (exponential in 1/# ) hidden by the asymptotic analysis. The second class deals with the specific case of t = n 2, and these algorithms have work complexity of O(t log t). They also use lists of permutations with the same combinatorial properties. However instantiating these algorithms requires exponential in n preprocessing to find the permutations. To alleviate this costly instantiation Kanellakis and Shvartsman proposed a simple way of computing the permutations. They conjectured that their construction has the desired properties but they provided no analysis. In this paper

To my family for their support in all these years iii Acknowledgment I am indebted to my family, which have given me strong support in all these years to pursue my own ambition. Special thanks to my advisors: Zvi M. Kedem and Vijay Karamcheti. It is from Zvi that I learned to appreciate simplicity and to look for inter-esting problems from ordinary tasks in the computer world. It is Vijay who showed me how to partition problems, write papers, give presentations, and keep extending current work for further goals and development. I have been working in a very good team. I had enormous help from Ayal Itzkovitz,