32825768
Compilation and Runtime-Optimizations for Software Distributed Shared Memory
We present two novel optimizations for compiling High Performance Fortran (HPF) to page-based software distributed shared memory systems (SDSM). One technique, compiler-managed restricted consistency, uses compiler-derived knowledge to delay the application of memory consistency operations to data that is provably not shared in the current synchronization interval, thus reducing false sharing. The other technique, compiler-managed shared buffers, when combined with the previous optimization, eliminates fragmentation. Together, the two techniques permit compiler-generated code to efficiently apply multi-dimensional computation partitioning and wavefront parallelism to execute efficiently on SDSM systems.
11313782
Efficient Heuristics for Solving Probabilistic Interval Algebra Networks
A probabilistic interval algebra (PIA) network is an interval algebra network with probabilities associated with the labels on an edge. The probabilities on each edge sum to 1. A solution is a consistent scenario where the product of the probabilities associated with each unique edge label is maximized. In this paper we investigate previous PIA network solution algorithms, and propose new ones. Our first algorithm is based on best first search and guarantees to output the optimal solution. However, this algorithm is only feasible for toy problems. We augment the algorithm with three heuristics. Although our proposed algorithm does not guarantee an optimal solution, it is very useful in practice. Good solutions can be generated quickly
18767470
New event detection based on indexing-tree and named entity
New Event Detection (NED) aims at detecting from one or multiple streams of news stories that which one is reported on a new event (i.e. not reported previously). With the overwhelming volume of news available today, there is an increasing need for a NED system which is able to detect new events more efficiently and accurately. In this paper we propose a new NED model to speed up the NED task by using news indexing-tree dynamically. Moreover, based on the observation that terms of different types have different effects for NED task, two term reweighting approaches are proposed to improve NED accuracy. In the first approach, we propose to adjust term weights dynamically based on previous story clusters and in the second approach, we propose to employ statistics on training data to learn the named entity reweighting model for each class of stories. Experimental results on two Linguistic Data Consortium (LDC) datasets TDT2 and TDT3 show that the proposed model can improve both efficiency and accuracy of NED task significantly, compared to the baseline system and other existing systems.
10651609
Improved Nyström low-rank approximation and error analysis
Low-rank matrix approximation is an effective tool in alleviating the memory and computational burdens of kernel methods and sampling, as the mainstream of such algorithms, has drawn considerable attention in both theory and practice. This paper presents detailed studies on the Nyström sampling scheme and in particular, an error analysis that directly relates the Nyström approximation quality with the encoding powers of the landmark points in summarizing the data. The resultant error bound suggests a simple and efficient sampling scheme, the k-means clustering algorithm, for Nyström low-rank approximation. We compare it with state-of-the-art approaches that range from greedy schemes to probabilistic sampling. Our algorithm achieves significant performance gains in a number of supervised/unsupervised learning tasks including kernel PCA and least squares SVM.
8216383
Locating object contours in complex background using improved snakes
An active contour model, called snake, can adapt to object boundary in an image. A snake is defined as an energy minimizing spline guided by external constraint forces and influenced by image forces that pull it toward features such as lines or edges. The traditional snake model fails to locate object contours that appear in complex background. In this paper, we present an improved snake model associated with new regional similarity energy and a gravitation force field to attract the snake approaching the object contours efficiently. Experiment results show that our snake model works successfully for convex and concave objects in a variety of complex backgrounds.
9437535
Practical Application of FDC in Software Service Pricing
This paper proposes a modified fully distributed cost (FDC) pricing method with third-degree price discrimination for software service. The method is a feasible and simple method to be used in practice. The pricing process is generalized as involving data collection, strategy analysis and strategy establishment. Then a practical SCM software service pricing in retail industry is described
5848822
Soft decoder architecture of LT codes
Luby transform (LT) codes, as the first class of efficient rateless codes, attract a lot of attention in the coding theory field. However, the VLSI implementation of LT codes is challenging due to its random code construction characteristic as well as the flexible output length. In this paper, we present an applicable architecture of a soft-decision LT decoder with a block length of 1024 bits and 100 iterations. Partly parallel input node processing and output node processing techniques are both adopted to accelerate decoding speed. An efficient router and reverse router are designed to indicate the graphic connectivity between input nodes and output nodes. The parallel architecture is prototyped on the target FPGA device.