27730185
Parallel All-Row Preconditioned Interval Linear Solver for Nonlinear Equations on Multiprocessors
Interval Newton methods in conjunction with generalized bisection form the basis of algorithms that find all real roots within a specified X ⊂ Rn of a system of nonlinear equations F(X) = 0 with mathematical certainty, even in finite-precision arithmetic. One of the major computational cost in such methods is solving a correspondent linear interval system. This paper proposes parallel implementations of the all-row preconditioned interval linear solver on two multiprocessor architectures: shared bus multiprocessor and hypercube connected multiprocessor. Efficient parallel algorithms that are specifically tailored for these architectures are presented. The algorithms effectively use the hardware features to minimize communication overheads. Performance evaluation is carried out by means of actual measurements of the algorithms running on real parallel computers. Our numerical results show that data partitioning, data allocation and processor scheduling play important roles in the performance of the parallel computation. In both systems, significant speedup can be obtained by properly overlapping computation and communication.
11786279
Effects of Arbitration Protocols on the Performance of Multiple-Bus Multiprocessors
229042
Introducing a New Cache Design into Vector Computers
Introduces an innovative cache design for vector computers, called prime-mapped cache. By utilizing the special properties of a Mersenne prime, the new design does not increase the critical path length of a processor, nor does it increase the cache access time as compared to existing cache organizations. The prime-mapped cache minimizes cache miss ratio caused by line interferences that have been shown to be critical for numerical applications by previous investigators. With negligibly additional hardware cost, significant performance gains are obtained by adding the proposed cache memory to an existing vector computer. The performance of the design is studied analytically, using a generic vector computation model. The analytical model is validated through extensive simulation experiments. A performance analysis for various vector access patterns shows that the prime-mapped cache performs significantly better than conventional cache organizations in the vector processing environment. The performance gain will increase with the increase of the speed gap between processors and memories. >
2566279
PRINS: Optimizing Performance of Reliable Internet Storages
Distributed storage systems employ replicas or erasure code to ensure high reliability and availability of data. Such replicas create great amount of network traffic that negatively impacts storage performance, particularly for distributed storage systems that are geographically dispersed over a wide area network (WAN). This paper presents a performance study of our new data replication methodology that minimizes network traffic for data replications. The idea is to replicate the parity of a data block upon each write operation instead of the data block itself. The data block will be recomputed back at the replica storage site upon receiving the parity. We name the new methodology PRINS (Parity Replication in IP-Network Storages). PRINS trades off highspeed computation for communication that is costly and more likely to be the performance bottleneck for distributed storages. By leveraging the parity computation that exists in common storage systems (RAID), our PRINS does not introduce additional overhead but dramatically reduces network traffic. We have implemented PRINS using iSCSI protocol over a TCP/IP network interconnecting a cluster of PCs as storage nodes. We carried out performance measurements on Oracle database, Postgres database, MySQL database, and Ext2 file system using TPC-C, TPC-W, and Micro benchmarks. Performance measurements show up to 2 orders of magnitudes bandwidth savings of PRINS compared to traditional replicas. A queueing network model is developed to further study network performance for large networks. It is shown that PRINS reduces response time of the distributed storage systems dramatically.
36641724
A new and efficient FFT algorithm for distributed memory systems
This paper presents a new and optimal parallel implementation of multidimensional fast Fourier transform algorithm on distributed memory multiprocessors. Its optimality is obtained by minimizing the number of message passings necessary, at the cost of increase in message length. This distinctive feature of the new algorithm effectively utilizes the important architectural property of most of today's distributed memory multiprocessors-wormhole routing for interprocessor communications. By using the algebra of stride permutations and tenser products as a mathematical tool, we are able to derive and formulate an efficient data partition and communication scheme that reduces communication cost from O(N/sup 2/) required for the best known FFT to O(N) on an N/sup 2/-processor machine. Our data partition scheme is natural and efficient for solving discretized boundary value problems such as partial differential equations and finite element analysis. To evaluate the actual performance of our new algorithm in comparison with other existing parallel FFT algorithms, we have carried out implementation experiments on the Intel's Touchstone Delta machine.
78709
STICS: SCSI-to-IP cache for storage area networks
Abstract Data storage plays an essential role in today's fast-growing data-intensive network services. New standards and products emerge very rapidly for networked data storage. Given the mature Internet infrastructure, the overwhelming preference among the IT community recently is using IP for storage networking because of economy and convenience. iSCSI is one of the most recent standards that allow SCSI protocols to be carried out over IP networks. However, there are many disparities between SCSI and IP in terms of protocols, speeds, bandwidths, data unit sizes, and design considerations that prevent fast and efficient deployment of storage area network (SAN) over IP. This paper introduces SCSI-to-IP cache storage (STICS), a novel storage architecture that couples reliable and high-speed data caching with low-overhead conversion between SCSI and IP protocols. A STICS block consists of one or several storage devices and an intelligent processing unit with CPU and RAM. The storage devices are used to cache and store data while the intelligent processing unit carries out the caching algorithm, protocol conversion, and self-management functions. Through the efficient caching algorithm and localization of certain unnecessary protocol overheads, STICS can significantly improve performance, reliability, and scalability over current iSCSI systems. Furthermore, STICS can be used as a basic plug-and-play building block for data storage over IP. Analogous to “cache memory” invented several decades ago for bridging the speed gap between CPU and memory, STICS is the first-ever “cache storage” for bridging the gap between SCSI and IP making it possible to build an efficient SAN over IP. We have implemented software STICS prototype on Linux operating system. Numerical results using popular benchmarks such as vxbench, IOzone, PostMark, and EMC's trace have shown a dramatic performance gain over the current iSCSI implementation.
698071
Towards Urban 3D Reconstruction from Video
The paper introduces a data collection system and a processing pipeline for automatic geo-registered 3D reconstruction of urban scenes from video. The system collects multiple video streams, as well as GPS and INS measurements in order to place the reconstructed models in geo- registered coordinates. Besides high quality in terms of both geometry and appearance, we aim at real-time performance. Even though our processing pipeline is currently far from being real-time, we select techniques and we design processing modules that can achieve fast performance on multiple CPUs and GPUs aiming at real-time performance in the near future. We present the main considerations in designing the system and the steps of the processing pipeline. We show results on real video sequences captured by our system.
1859019
A parallel algorithm for clustering protein-protein interaction networks
The increasing availability of interaction graphs requires new resource-efficient tools capable of extracting valuable biological knowledge from these networks. In this paper we report on a novel parallel implementation of Girvan and Newman's clustering algorithm that is capable of running on clusters of computers. Our parallel implementation achieves almost linear speed-up up to 32 processors and allows us to run this computationally intensive algorithm on large protein-protein interaction networks. Preliminary experiments show that the algorithm has very high accuracy in identifying functional related protein modules.
6653256
Domain-constrained semi-supervised mining of tracking models in sensor networks
Accurate localization of mobile objects is a major research problem in sensor networks and an important data mining application. Specifically, the localization problem is to determine the location of a client device accurately given the radio signal strength values received at the client device from multiple beacon sensors or access points. Conventional data mining and machine learning methods can be applied to solve this problem. However, all of them require large amounts of labeled training data, which can be quite expensive. In this paper, we propose a probabilistic semi supervised learning approach to reduce the calibration effort and increase the tracking accuracy. Our method is based on semi-supervised conditional random fields which can enhance the learned model from a small set of training data with abundant unlabeled data effectively. To make our method more efficient, we exploit a Generalized EM algorithm coupled with domain constraints. We validate our method through extensive experiments in a real sensor network using Crossbow MICA2 sensors. The results demonstrate the advantages of methods compared to other state-of-the-art object-tracking algorithms.
13301215
Formalizing planning knowledge for hierarchical planning
A hierarchical planning system achieves efficiency by planning with the most important conditions first, and considering details later in the planning process. Few attempts have been made to formalize the structure of the planning knowledge for hierarchical planning. For a given domain, there is usually more than one way to define its planning knowledge. Some of the definitions can lead to efficient planning, while others may not. In this paper, we provide a set of restrictions which defines the relationships between a non-primitive action and its set of subactions. When satisfied, these restrictions guarantee improved efficiency for hierarchical planning. One important feature of these restrictions is that they are syntactic and therefore do not depend on the particular structure of any plan. Along with these restrictions, we also provide algorithms for preprocessing the planning knowledge of a hierarchical planner. When used during planning, the preprocessed operator hierarchies can enable a planner to significantly reduce its search space. 
 
 
 
Un systeme de planification hierarchique permet d'obtenir un rendement efficace dans la mesure ou il planifie d'abord les conditions importantes avant de s'attarder aux details secondares dans le processus de planification. Quelques ten-tatives ont ete realisees en vue de formaliser la structure des connaissances de planification dans la planification hierarchique. Dans un domaine donne, il existe normalement plus d'une facon de definir les connaissances de planification. Certaines definitions peuvent permettre une planification efficace et d'autres non. Dans cet article, l'auteur propose une serie de restrictions qui definissent les rapports entre une action non primitive et son ensemble de sous-actions. Lorsqu'elles sont respectees, ces restrictions permettent d'ameliorer l'efficacite dans la planification hierarchique. l'une des caracteristiques importantes de ces restrictions est qu'elles sont syntaxiques et ne dependent done pas de la structure particuliere d'un plan. En plus de ces restrictions, l'auteur propose egalement des algorithmes pour le pretraitement des connaissances de planification d'un planificateur hierarchique. Quand elles sont utilisees durant la planification, les hierarchies d'operateur peuvent permettre au planificateur de reduire considerablement son espace de recherche.
17711119
IRC: an iterative reinforcement categorization algorithm for interrelated Web objects
Most existing categorization algorithms deal with homogeneous Web data objects, and consider interrelated objects as additional features when taking the interrelationships with other types of objects into account. However, focusing on any single aspects of these interrelationships and objects does not fully reveal their true categories. In this paper, we propose a categorization algorithm, the iterative reinforcement categorization algorithm (IRC), to exploit the full interrelationships between the heterogeneous objects on the Web. IRC attempts to classify the interrelated Web objects by iterative reinforcement between individual classification results of different types via the interrelationships. Experiments on a clickthrough log dataset from MSN search engine show that, with the Fl measures, IRC achieves a 26.4% improvement over a pure content-based classification method, a 21% improvement over a query metadata-based method, and a 16.4% improvement over a virtual document-based method. Furthermore, our experiments show that IRC converges rapidly.