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- S. D. Bruda, S. G. Akl
- 2006

We show that all the problems solvable by a nondeterministic machine with logarithmic work space (NL) can be solved in real time by a parallel machine, no matter how tight the real-time constraints are. We also show that several other real-time problems are in effect solvable in nondeterministic logarithmic space once their real-time constraints are dropped… (More)

We developed a model to investigate the phenomenon of whitewashing in peer-to-peer (P2P) systems. A white washer is a node that leaves the network and re-joins using a different identity. Whitewashing is a common problem in P2P systems and is a difficult to manage properly for reputable systems. The paper discusses the definition and properties of trust,… (More)

We investigate the relative computational power of parallel models with shared memory. Based on feasibility considerations present in the literature, we split these models into " lightweight " and " heavyweight, " and then find that the heavyweight class is strictly more powerful than the lightweight class, as expected. On the other hand, we contradict the… (More)

Traditionally, interest in parallel computation centered around the speedup provided by parallel algorithms over their sequential counterparts. In this paper, we ask a diierent type of question: Can parallel computers, due to their speed, do more than simply speed up the solution to a problem? We show that for real-time optimization problems, a parallel… (More)

A data-accumulating algorithm (d-algorithm for short) works on an input considered as a virtually endless stream. The computation terminates when all the currently arrived data have been processed before another datum arrives. In this paper, the class of d-algorithms is characterized. It is shown that this class is identical to the class of on-line… (More)

A correcting algorithm is one that receives an endless stream of corrections to its initial input data and terminates when all the corrections received have been taken into account. We give a characterization of correcting algorithms based on the theory of data{accumulating algorithms. In particular, it is shown that any correcting algorithm exhibits… (More)

Parallel computers can do more than simply speed up sequential computations. They are capable of nding solutions that are far better in quality than those obtained by sequential computers. This fact is demonstrated by analyzing sequential and parallel solutions to numerical problems in a real-time paradigm. In this setting, numerical data required to solve… (More)

In the data{accumulating paradigm, the input is an endless stream. A computation is considered to be nished when all the already received data are processed before another datum arrives. We study sorting algorithms in this paradigm. First, we consider the data arrival law as being polynomial in time. We prove the existence of an upper bound on the running… (More)

The primary purpose of parallel computation is the fast execution of computational tasks that are too slow to perform sequentially. H o w ever, it was shown recently that a second equally important motivation for using parallel computers exists: Within the paradigm of real-time computation, some classes of problems have the property that a solution to a… (More)