481550
Improving Fine-Grained Irregular Shared-Memory Benchmarks by Data Reordering
We demonstrate that data reordering can substantially improve the performance of fine-grained irregular shared-memory benchmarks, on both hardware and software shared-memory systems. In particular, we evaluate two distinct data reordering techniques that seek to co-locate in memory objects that are in close proximity in the physical system modeled by the computation. The effects of these techniques are increased spatial locality and reduced false sharing. We evaluate the effectiveness of the data reordering techniques on a set of five irregular applications from SPLASH-2 and Chaos. We implement both techniques in a small library, allowing us to enable them in an application by adding less than 10 lines of code. Our results on one hardware and two software shared-memory systems show that, with data reordering during initialization, the performance of these applications is improved by 12%-99% on the Origin 2000, 30%-366% on TreadMarks, and 14%-269% on HLRC.
43789567
A Self-Organizing Flock of Condors
14398394
Optimizing routing in structured peer-to-peer overlay networks using routing table redundancy
Structured peer-to-peer (p2p) overlay networks like CAN, Chord, Pastry and Tapestry [3, 6, 5, 9] provide a self-organizing substrate for large-scale peer-to-peer applications. These systems provide efficient, fault-tolerant routing, object location and load balancing within a self-organization overlay network. In this paper, we show how redundant information that is collected as part of the normal overlay maintenance protocol can be exploited to improve the performance of routing, in terms of both the number of routing hops and routing delay penalty. We use Pastry as a concrete example to describe the set of optimizations and to evaluate their improvement in routing performance via a large scale simulation using a realistic network topology model. We then discuss how these optimizations can be applied to other structured p2p overlays.
9572409
High-Throughput Multicast Routing Metrics in Wireless Mesh Networks
The stationary nature of nodes in a mesh network has shifted the main design goal of routing protocols from maintaining connectivity between source and destination nodes to finding high-throughput paths between them. In recent years, numerous link-quality-based routing metrics have been proposed for choosing high-throughput paths for unicast protocols. In this paper we study routing metrics for high-throughput tree or mesh construction in multicast protocols. We show that there is a fundamental difference between unicast and multicast routing in how data packets are transmitted at the link layer, and accordingly there is a difference in how the routing metrics for each of these primitives are designed. We adapt certain routing metrics for unicast for high-throughput multicast routing and propose news ones not previously used for high-throughput. We then study the performance improvement achieved by using different link-quality-based routing metrics via extensive simulation and experiments on a mesh network testbed, using ODMRP as a representative multicast protocol. Our testbed experiment results show that ODMRP enhanced with linkquality routing metrics can achieve up to 17.5% throughput improvement as compared to the original ODMRP.
8865265
A Fair, Secure and Trustworthy Peer-to-Peer Based Cycle-Sharing System
The increased popularity of Grid systems and cycle sharing across organizations requires scalable systems that provide facilities to locate resources, to be fair in the use of those resources, to allow resource providers to host untrusted applications safely, and to allow resource consumers to monitor the progress and correctness of jobs executing on remote machines. This paper presents such a framework that locates computational resources with a peer-to-peer network, assures fair resource usage with a distributed credit accounting system, provides resource contributors a safe environment, for example Java Virtual Machine (JVM), to host untrusted applications, and provides the resource consumers a monitoring system, GridCop, to track the progress and correctness of remotely executing jobs. We present the details of the credit accounting subsystem and the GridCop remote job monitoring subsystem. GridCop and the distributed credit accounting system together enable incremental payments so that the risk for both resource providers and resource consumers is bounded.
1998178
A Hierarchical Approach to Internet Distance Prediction
Internet distance prediction gives pair-wise latency information with limited measurements. Recent studies have revealed that the quality of existing prediction mechanisms from the application perspective is short of satisfactory. In this paper, we explore the root causes and remedies for this problem. Our experience with different landmark selection schemes shows that although selecting nearby landmarks can increase the prediction accuracy for short distances, it can cause the prediction accuracy for longer distances to degrade. Such uneven prediction quality significantly impacts application performance. Instead of trying to select the landmark nodes in some "intelligent" fashion, we propose a hierarchical prediction approach with straightforward landmark selection. Hierarchical prediction utilizes multiple coordinate sets at multiple distance scales, with the "right" scale being chosen for prediction each time. Experiments with Internet measurement datasets show that this hierarchical approach is extremely promising for increasing the accuracy of network distance prediction.
27295065
Automatic extraction of titles from general documents using machine learning
In this paper, we propose a machine learning approach to title extraction from general documents. By general documents, we mean documents that can belong to any one of a number of specific genres, including presentations, book chapters, technical papers, brochures, reports, and letters. Previously, methods have been proposed mainly for title extraction from research papers. It has not been clear whether it could be possible to conduct automatic title extraction from general documents. As a case study, we consider extraction from Office including Word and PowerPoint. In our approach, we annotate titles in sample documents (for Word and PowerPoint, respectively) and take them as training data, train machine learning models, and perform title extraction using the trained models. Our method is unique in that we mainly utilize formatting information such as font size as features in the models. It turns out that the use of formatting information can lead to quite accurate extraction from general documents. Precision and recall for title extraction from Word are 0.810 and 0.837, respectively, and precision and recall for title extraction from PowerPoint are 0.875 and 0.895, respectively in an experiment on intranet data. Other important new findings in this work include that we can train models in one domain and apply them to other domains, and more surprisingly we can even train models in one language and apply them to other languages. Moreover, we can significantly improve search ranking results in document retrieval by using the extracted titles.
9078923
Robust subspace analysis for detecting visual attention regions in images
Detecting visually attentive regions of an image is a challenging but useful issue in many multimedia applications. In this paper, we describe a method to extract visual attentive regions in images using subspace estimation and analysis techniques. The image is represented in a 2D space using polar transformation of its features so that each region in the image lies in a 1D linear subspace. A new subspace estimation algorithm based on Generalized Principal Component Analysis (GPCA) is proposed. The robustness of subspace estimation is improved by using weighted least square approximation where weights are calculated from the distribution of K nearest neighbors to reduce the sensitivity of outliers. Then a new region attention measure is defined to calculate the visual attention of each region by considering both feature contrast and geometric properties of the regions. The method has been shown to be effective through experiments to be able to overcome the scale dependency of other methods. Compared with existing visual attention detection methods, it directly measures the global visual contrast at the region level as opposed to pixel level contrast and can correctly extract the attentive region.
13514576
Program-Counter-Based Pattern Classification in Buffer Caching
Program-counter-based (PC-based) prediction techniques have been shown to be highly effective and are widely used in computer architecture design. In this paper, we explore the opportunity and viability of applying PC-based prediction to operating systems design, in particular, to optimize buffer caching. We propose a Program-Counterbased Classification (PCC) technique for use in pattern-based buffer caching that allows the operating system to correlate the I/O operations with the program context in which they are issued via the program counters of the call instructions that trigger the I/O requests. This correlation allows the operating system to classify I/O access pattern on a per-PC basis which achieves significantly better accuracy than previous per-file or per-application classification techniques. PCC also performs classification more quickly as per-PC pattern just needs to be learned once. We evaluate PCC via trace-driven simulations and an implementation in Linux, and compare it to UBM, a state-of-the-art pattern-based buffer replacement scheme. The performance improvements are substantial: the hit ratio improves by as much as 29.3% (with an average of 13.8%), and the execution time is reduced by as much as 29.0% (with an average of 13.7%).
30629384
Ordering Unsymmetric Matrices into Bordered Block Diagonal Form for Parallel Processing
Large, sparse, unsymmetric systems of linear equations appear frequently in areas such as chemical engineering. One way of speeding up the solution of these linear systems is to solve them in parallel by reordering the unsymmetric matrix into a bordered block-diagonal (BBD) form. A multilevel ordering algorithm is presented in this paper. Numerical results of the algorithm are given to demonstrate that this algorithm gives better orderings than existing algorithms.
12662534
A Framework for Managing Emergent Transmissions in IP Networks
Theories and experiments have shown that average end-to-end delay of flows increases and TCP becomes inefficient and instable during network congestion period, regardless of which queue management and packet scheduling mechanism are being applied. Despite the concerns of network congestion problem have raised by the implementations of various per-flow based transmission protocols and queue policies on routers, there are still very few efforts to assess the feasibility of modifying network infrastructure to accommodate these new algorithms. It is practically impossible to reach the performance that claimed by the per-flow based algorithms, while updating routers is very costly and beyond the control of every individual network domain. In this paper, we present an application layer reliable multi-connection best effort framework which provides quality of service (QoS) for emergent communications with no or very few changes of the existing network infrastructure. Performance of our approach and other several existing algorithms is studied by using the network simulator NS2.