14283892
Analyzing the Energy-Time Trade-Off in High-Performance Computing Applications
Although users of high-performance computing are most interested in raw performance both energy and power consumption has become critical concerns. One approach to lowering energy and power is to use high-performance cluster nodes that have several power-performance states so that the energy-time trade-off can be dynamically adjusted. This paper analyzes the energy-time trade-off of a wide range of applications-serial and parallel-on a power-scalable cluster. We use a cluster of frequency and voltage-scalable AMD-64 nodes, each equipped with a power meter. We study the effects of memory and communication bottlenecks via direct measurement of time and energy. We also investigate metrics that can, at runtime, predict when each type of bottleneck occurs. Our results show that, for programs that have a memory or communication bottleneck, a power-scalable cluster can save significant energy with only a small time penalty. Furthermore, we find that, for some programs, it is possible to both consume less energy and execute in less time by increasing the number of nodes while reducing the frequency-voltage setting of each node
2768791
Finding representative set from massive data
In the information age, data is pervasive. In some applications, data explosion is a significant phenomenon. The massive data volume poses challenges to both human users and computers. In this project, we propose a new model for identifying representative set from a large database. A representative set is a special subset of the original dataset, which has three main characteristics: It is significantly smaller in size compared to the original dataset. It captures the most information from the original dataset compared to other subsets of the same size. It has low redundancy among the representatives it contains. We use information-theoretic measures such as mutual information and relative entropy to measure the representativeness of the representative set. We first design a greedy algorithm and then present a heuristic algorithm that delivers much better performance. We run experiments on two real datasets and evaluate the effectiveness of our representative set in terms of coverage and accuracy. The experiments show that our representative set attains expected characteristics and captures information more efficiently.
11841640
Accelerating Profile Queries in Elevation Maps
Elevation maps are a widely used spatial data representation in geographical information systems (GIS). Paths on elevation maps can be characterized by profiles, which describe relative elevation as a function of distance. In this research, we address the inverse of this mapping - given a profile, how to efficiently find paths that could have generated it. This is called the profile query problem. Profiles have a wide variety of uses that include registering tracking information, or even other maps, to a given map. We describe a probabilistic model to characterize the maximal likelihood that a point lying on a path matches the query profile. Propagation of such probabilities to neighboring points can effectively prune the search space. This model enables us to efficiently answer queries of arbitrary profiles with user-specified error tolerances. When compared to existing spatial index methods, our approach supports more flexible queries with orders of magnitude speedup.
9042204
CRD: fast co-clustering on large datasets utilizing sampling-based matrix decomposition
The problem of simultaneously clustering columns and rows (co-clustering) arises in important applications, such as text data mining, microarray analysis, and recommendation system analysis. Compared with the classical clustering algorithms, co-clustering algorithms have been shown to be more effective in discovering hidden clustering structures in the data matrix. The complexity of previous co-clustering algorithms is usually O(m X n), where m and n are the numbers of rows and columns in the data matrix respectively. This limits their applicability to data matrices involving a large number of columns and rows. Moreover, some huge datasets can not be entirely held in main memory during co-clustering which violates the assumption made by the previous algorithms. In this paper, we propose a general framework for fast co-clustering large datasets, CRD. By utilizing recently developed sampling-based matrix decomposition methods, CRD achieves an execution time linear in m and n. Also, CRD does not require the whole data matrix be in the main memory. We conducted extensive experiments on both real and synthetic data. Compared with previous co-clustering algorithms, CRD achieves competitive accuracy but with much less computational cost.
108674
Sample Selection for Maximal Diversity
The problem of selecting a sample subset sufficient to preserve diversity arises in many applications. One example is in the design of recombinant inbred lines (RIL) for genetic association studies. In this context, genetic diversity is measured by how many alleles are retained in the resulting inbred strains. RIL panels that are derived from more than two parental strains, such as the collaborative cross (Churchill et al., 2004), present a particular challenge with regard to which of the many existing lab mouse strains should be included in the initial breeding funnel in order to maximize allele retention. A similar problem occurs in the study of customer reviews when selecting a subset of products with a maximal diversity in reviews. Diversity in this case implies the presence of a set of products having both positive and negative ranks for each customer. In this paper, we demonstrate that selecting an optimal diversity subset is an NP-complete problem via reduction to set cover. This reduction is sufficiently tight that greedy approximations to the set cover problem directly apply to maximizing diversity. We then suggest a slightly modified subset selection problem in which an initial greedy diversity solution is used to effectively prune an exhaustive search for all diversity subsets bounded from below by a specified coverage threshold. Extensive experiments on real datasets are performed to demonstrate the effectiveness and efficiency of our approach.
765245
CARE: Finding Local Linear Correlations in High Dimensional Data
Finding latent patterns in high dimensional data is an important research problem with numerous applications. Existing approaches can be summarized into 3 categories: feature selection, feature transformation (or feature projection) and projected clustering. Being widely used in many applications, these methods aim to capture global patterns and are typically performed in the full feature space. In many emerging biomedical applications, however, scientists are interested in the local latent patterns held by feature subsets, which may be invisible via any global transformation. In this paper, we investigate the problem of finding local linear correlations in high dimensional data. Our goal is to find the latent pattern structures that may exist only in some subspaces. We formalize this problem as finding strongly correlated feature subsets which are supported by a large portion of the data points. Due to the combinatorial nature of the problem and lack of monotonicity of the correlation measurement, it is prohibitively expensive to exhaustively explore the whole search space. In our algorithm, CARE, we utilize spectrum properties and effective heuristic to prune the search space. Extensive experimental results show that our approach is effective in finding local linear correlations that may not be identified by existing methods.
20260133
Temporal aggregates for Web services on the semantic Web
In this paper we describe how we encode our temporal aggregates ontology in OWL for Web services on the semantic Web. We also present one example to show how to use the ontology to represent temporal aggregates information.
9886444
Computing Semantic Similarity between Skill Statements for Approximate Matching
This paper explores the problem of computing text similarity between verb phrases describing skilled human behavior for the purpose of finding approximate matches. Four parsers are evaluated on a large corpus of skill statements extracted from an enterprise-wide expertise taxonomy. A similarity measure utilizing common semantic role features extracted from parse trees was found superior to an information-theoretic measure of similarity and comparable to the level of human agreement.
30558034
A scalable fast mode decision algorithm for H.264
This paper presents a scalable fast mode decision algorithm to effectively choose the best coding mode among the 7 macroblock (MB) coding modes that could be used in H.264 video coding. The spatial and temporal complexity of the MB is analyzed to determine the order of searching in the mode decision priority queue such that the most probable mode is checked first, followed by the second most probable mode, and so on. This process is terminated as soon as the computed rate-distortion (RD) cost is below a threshold which is dependent on the complexity ratio of the current MB. By adjusting the threshold we can choose a preferred tradeoff between timesaving and quality compromise. Experimental results show that the proposed fast mode decision algorithm can drastically reduce the encoding time up to 50% with negligible loss of coding efficiency
44456878
A 2-Stage Partial Distortion Search Algorithm for Block Motion Estimation
In this paper, we propose a novel 2-Stage Partial Distortion Search (2S-PDS) algorithm to reduce the computational complexity in block motion estimation algorithms. In this algorithm, an early-rejection stage is introduced where the partial distortion of a decimated pixel block is calculated and compared with its local minimum. A block is rejected without calculating the full distortion of the entire block if the partial distortion is larger. In order to reduce the probability of false rejection, the local minimum is amplified by a pre-defined threshold before the comparison. Experimental results show that the proposed algorithm can reduce the complexity of block motion estimation algorithm significantly with only marginal performance penalty. The proposed algorithm can be used in combination with full-search or other fast search algorithms.
15720571
An adaptive rate control algorithm for video coding over personal digital assistants (PDA)
With the recent development of third-generation communication technologies, encoding live video using a PDA and sharing it among friends has become a reality. However, the embedded processor inside a PDA is still not powerful enough and there are two major hurdles to overcome: (1) video coding needs to meet the rigorous constraint of the available computation capacity of a PDA; (2) In a PDA the computing power allocated to video coding may vary drastically (in bursts). In this paper, a new adaptive rate control algorithm is proposed for video coding over a PDA. This adaptive rate control scheme takes into account the time constraint of a PDA, and its bit allocation depends not only on the available data bits, but more importantly, on the available coding time. Experimental results show that, compared to the existing rate control scheme, the new algorithm can always achieve the maximum frame rate, maximize the utilization of the available bandwidth and computing power, increase the average PSNR, and improve the subjective perceptual quality of the reconstructed video.