1923404
Global communication optimization for tensor contraction expressions under memory constraints
The accurate modeling of the electronic structure of atoms and molecules involves computationally intensive tensor contractions involving large multi-dimensional arrays. The efficient computation of complex tensor contractions usually requires the generation of temporary intermediate arrays. These intermediates could be extremely large, but they can often be generated and used in batches through appropriate loop fusion transformations. To optimize the performance of such computations on parallel computers, the total amount of inter-processor communication must be minimized, subject to the available memory on each processor In this paper we address the memory-constrained communication minimization problem in the context of this class of computations. Based on a framework that models the relationship between loop fusion and memory usage, we develop an approach to identify the best combination of loop fusion and data partitioning that minimizes inter-processor communication cost without exceeding the per-processor memory limit. The effectiveness of the developed optimization approach is demonstrated on a computation representative of a component used in quantum chemistry suites.
9799256
Empirical Performance-Model Driven Data Layout Optimization
Empirical optimizers like ATLAS have been very effective in optimizing computational kernels in libraries. The best choice of parameters such as tile size and degree of loop unrolling is determined by executing different versions of the computation. In contrast, optimizing compilers use a model-driven approach to program transformation. While the model-driven approach of optimizing compilers is generally orders of magnitude faster than ATLAS-like library generators, its effectiveness can be limited by the accuracy of the performance models used. In this paper, we describe an approach where a class of computations is modeled in terms of constituent operations that are empirically measured, thereby allowing modeling of the overall execution time. The performance model with empirically determined cost components is used to perform data layout optimization in the context of the Tensor Contraction Engine, a compiler for a high-level domain-specific language for expressing computational models in quantum chemistry. The effectiveness of the approach is demonstrated through experimental measurements on some representative computations from quantum chemistry.
32198408
Modelling Citation Networks for Improving Scientific Paper Classification Performance
This paper describes an approach to the use of citation links to improve the scientific paper classification performance. In this approach, we develop two refinement functions, a linear label refinement (LLR) and a probabilistic label refinement (PLR), to model the citation link structures of the scientific papers for refining the class labels of the documents obtained by the content-based Naive Bayes classification method. The approach with the two new refinement models is examined and compared with the content-based Naive Bayes method on a standard paper classification data set with increasing training set sizes. The results suggest that both refinement models can significantly improve the system performance over the content-based method for all the training set sizes and that PLR is better than LLR when the training examples are sufficient.
20577453
A Novel Clustering Method Based on SVM
For the problem of cluster analysis, the objective function based algorithms are popular and widely used methods. However, the performance of these algorithms depends upon the priori information about cluster number and cluster prototypes. Moreover, it is only effective for analyzing data set with the same type of cluster prototypes. For this end, this paper presents a novel algorithm based on support vector machine (SVM) for realizing fully unsupervised clustering. The experimental results with various test data sets illustrate the effectiveness of the proposed novel clustering algorithm based on SVM.
43629891
A Content-Based Image Quality Metric
image quality assessment plays an important role in relevant fields of image processing. The traditional image quality metric, such as PSNR, cannot reflect the visual perception to the image effectively. For this purpose, based on the fuzzy Sugeno integral a novel image quality assessment measure, called content-based metric (CBM), is proposed in this paper. It fuses the amount and local information into the similarity of the image structural information and gives a comprehensive evaluation for the quality of the specified image. The experimental results illustrate that the proposed metric has a good correlation with the human subjective perception, and can reflect the image quality effectively.
36004220
A Video Shot Boundary Detection Algorithm Based on Feature Tracking
Partitioning a video sequence into shots is the first and key step toward video-content analysis and content-based video browsing and retrieval. A novel video shot boundary detection algorithm is presented based on the feature tracking. First, the proposed algorithm extracts a set of corner-points as features from the first frame of a shot. Then, based on the Kalman filtering, these features are tracked with windows matching method from the subsequent frames. According to the characteristic pattern of pixels intensity changing between corresponding windows, the measure of shot boundary detection can be obtained to confirm the types of transitions and the time interval of gradual transitions. The experimental results illustrate that the proposed algorithm is effective and robust with low computational complexity
14087664
Maximum entropy fuzzy clustering with application to real-time target tracking
The problem of data association for target tracking in a cluttered environment is discussed. In order to deal with the problem of data association for real time target tracking, a novel data association method based on maximum entropy fuzzy clustering is proposed. Firstly, the candidate measurements of each target are clustered with the aid of the modified maximum entropy fuzzy clustering. Then the joint association probabilities are reconstructed by utilizing the fuzzy membership degree of the measurement belonging to the target. At the same time, in order to deal with the uncertainty of the measurements, a new weight assignment is introduced. Moreover, the characteristic of the discrimination factor is analyzed, and the influence of the clutter density on it is also considered, which enables the algorithm eliminate those invalidate measurements and reduce the computational load. Finally, the simulation results show that the proposed algorithms have advantages over the existing ones in terms of efficiency and low computational load.