In this paper we examine the problem of extending modern operating systems to run efficiently on large-scale shared memory multiprocessors without a large implementation effort. Our approach brings back an idea popular in the 1970s, virtual machine monitors. We use virtual machines to run multiple commodity operating systems on a scalable multiprocessor. This solution addresses many of the challenges facing the system software for these machines. We demonstrate our approach with a prototype called Disco that can run multiple copies of Silicon Graphics ’ IRIX operating system on a multiprocessor. Our experience shows that the overheads of the monitor are small and that the approach provides scalability as well as the ability to deal with the non-uniform memory access time of these systems. To reduce the memory overheads associated with running multiple operating systems, we have developed techniques where the virtual machines transparently share major data structures such as the program code and the file system buffer cache. We use the distributed system support of modern operating systems to export a partial single system image to the users. The overall solution achieves most of the benefits of operating systems customized for scalable multiprocessors yet it can be achieved with a significantly smaller implementation effort. 1

We survey the use of weighted finite-state transducers (WFSTs) in speech recognition. We show that WFSTs provide a common and natural representation for HMM models, context-dependency, pronunciation dictionaries, grammars, and alternative recognition outputs. Furthermore, general transducer operations combine these representations flexibly and efficiently. Weighted

A syntax-directed translator first parses the source-language input into a parsetree, and then recursively converts the tree into a string in the target-language. We model this conversion by an extended treeto-string transducer that have multi-level trees on the source-side, which gives our system more expressive power and flexibility. We also define a direct probability model and use a linear-time dynamic programming algorithm to search for the best derivation. The model is then extended to the general log-linear framework in order to rescore with other features like n-gram language models. We devise a simple-yet-effective algorithm to generate non-duplicate k-best translations for n-gram rescoring. Initial experimental results on English-to-Chinese translation are presented. 1

We present an approximately-efficient and approximately-strategyproof auction mechanism for a single-good multi-unit allocation problem. The bidding language allows marginaldecreasing piecewise constant curves and quantity-based side constraints. We develop a fully polynomial-time approximation scheme for the multi-unit allocation problem, which computes a -approximation in worst-case time , given bids each with a constant number of pieces. We integrate this approximation scheme within a VickreyClarke -Groves mechanism and compute payments for an asymptotic cost of ! . The maximal possible gain from manipulation to a bidder in the combined scheme is bounded by 4294-16716 " is the total surplus in the efficient outcome.

We have designed a radix sort algorithm for vector multiprocessors and have implemented the algorithm on the CRAY Y-MP. On one processor of the Y-MP, our sort is over 5 times faster on large sorting problems than the optimized library sort provided by CRAY Research. On eight processors we achieve an additional speedup of almost 5, yielding a routine over 25 times faster than the library sort. Using this multiprocessor version, we can sort at a rate of 15 million 64-bit keys per second. Our sorting algorithm is adapted from a data-parallel algorithm previously designed for a highly parallel Single Instruction Multiple Data (SIMD) computer, the Connection Machine CM-2. To develop our version we introduce three general techniques for mapping data-parallel algorithms ontovector multiprocessors. These techniques allow us to fully vectorize and parallelize the algorithm. The paper also derives equations that model the performance of our algorithm on the Y-MP. These equations are then used t...

In this paper we present the RWTH FSA toolkit -- an efficient implementation of algorithms for creating and manipulating weighted finite-state automata. The toolkit has been designed using the principle of on-demand computation and offers a large range of widely used algorithms. To prove the superior efficiency of the toolkit, we compare the implementation to that of other publically available toolkits. We also show that on-demand computations help to reduce memory requirements significantly without any loss in speed. To increase its flexibility, the RWTH FSA toolkit supports high-level interfaces to the programming language Python as well as a command-line tool for interactive manipulation of FSAs. Furthermore, we show how to utilize the toolkit to rapidly build a fast and accurate statistical machine translation system. Future extensibility of the toolkit is ensured as it will be publically available as open source software.

. This paper presents the mask method (MASK), a new population-based, evolutionary search procedure for finding models for satisfiability problems (SAT). In this method, a partial truth assignment for a given boolean expression containing N variables is represented by a partially instantiated 0/1 string with N positions (called mask), with each position coding one variable. The mask method begins with a population of masks. An iteration process follows to evaluate each mask in the population using an evaluation mechanism, then to discard half of them, and finally to divide each remaining mask into two new ones by fixing a free position in the mask to 0 and 1 respectively. This process continues until all the positions in the mask have a fixed value. The method is compared with a class of genetic algorithms (GAs) on a set of SAT instances and proves to be much more efficient. 1 INTRODUCTION The satisfiability problem or SAT [5] is of great importance in Artificial Intelligence both in...

Sensor networks is among the fastest growing technologies that have the potential of changing our lives drastically. These collaborative, dynamic and distributed computing and communicating systems will be self organizing. They will have capabilities of distributing a task among themselves for efficient computation. There are many challenges in implementation of such systems: energy dissipation and clustering being one of them. In order to maintain a certain degree of service quality and a reasonable system lifetime, energy needs to be optimized at every stage of system operation. Sensor node clustering is another very important optimization problem. Nodes that are clustered together will easily be able to communicate with each other. Considering energy as an optimization parameter while clustering is imperative. In this paper we study the theoretical aspects of the clustering problem in sensor networks with application to energy optimization. We illustrate an optimal algorithm for clustering the sensor nodes such that each cluster (which has a master) is balanced and the total distance between sensor nodes and master nodes is minimized. Balancing the clusters is needed for evenly distributing the load on all master nodes. Minimizing the total distance helps in reducing the communication overhead and hence the energy dissipation. This problem (which we call balanced k-clustering) is modeled as a mincost flow problem which can be solved optimally using existing techniques.

A multimedia presentation refers to the presentation of multimedia data using output devices such as monitors for text and video, and speakers for audio. Each presentation consists of multimedia segments which are obtained from a multimedia data model. In this paper, we propose to express semantic coherency of a multimedia presentation in terms of presentation inclusion and exclusion constraints that are incorporated into the multimedia data model. Thus, when a user specifies a set of segments for a presentation, the DBMS adds segments into and/or deletes segments from the set in order to satisfy the inclusion and exclusion constraints. To automate the assembly of a presentation with concurrent presentation streams, we also propose presentation organization constraints that are incorporated into the multimedia data model, independent of any presentation. We give two algorithms for automated presentation assembly and discuss their complexity. We discuss the satisfiability of inclusion a...

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