This paper introduces the vector sparse matrix transform (vector SMT), a new decorrelating transform suitable for performing distributed processing of high-dimensional signals in sensor networks. We assume that each sensor in the network encodes its measurements into vector outputs instead of scalar ones. The proposed transform decorrelates a sequence of pairs of vector outputs, until these vectors are decorrelated. In our experiments, we simulate distributed anomaly detection by a network of cameras, monitoring a spatial region. Each camera records an image of the monitored environment from its particular viewpoint and outputs a vector encoding the image. Our results, with both artificial and real data, show that the proposed vector SMT transform effectively decorrelates image measurements from the multiple cameras in the network while maintaining low overall communication energy consumption. Since it enables joint processing of the multiple vector outputs, our method provides significant improvements to anomaly detection accuracy when compared with the baseline case when the images are processed independently.

Here, we propose the vector Sparse Matrix Transform (SMT), a novel decorrelating transform suitable for performing distributed processing of high dimensional signals in sensor networks. We assume that each sensor in the network encodes its measurements into vector outputs instead of scalar ones. The proposed transform decorrelates a sequence of pairs of vector sensor outputs, until these vectors are decorrelated. In our experiments, we simulate distributed anomaly detection by a camera network monitoring a spatial region. Each camera records an image of the monitored environment from its particular viewpoint and outputs a vector encoding the image. Results show that the vector SMT effectively decorrelates images from the multiple cameras in the network and significantly improves anomaly detection accuracy while requiring low overall communication energy.

Covariance estimation for high dimensional signals is a classically difficult problem in statistical signal analysis and machine learning. In this paper, we propose a maximum likelihood (ML) approach to covariance estimation, which employs a novel non-linear sparsity constraint. More specifically, the covariance is constrained to have an eigen decomposition which can be represented as a sparse matrix transform (SMT). The SMT is formed by a product of pairwise coordinate rotations known as Givens rotations. Using this framework, the covariance can be efficiently estimated using greedy optimization of the log-likelihood function, and the number of Givens rotations can be efficiently computed using a cross-validation procedure. The resulting estimator is generally positive definite and well-conditioned, even when the sample size is limited. Experiments on a combination of simulated data, standard hyperspectral data, and face image sets show that the SMT-based covariance estimates are consistently more accurate than both traditional shrinkage estimates and recently proposed graphical lasso estimates for a variety of different classes and sample sizes. An important property of the new covariance estimate is that it naturally yields a fast implementation of the estimated eigen-transformation using the SMT representation. In fact, the SMT can be viewed as a generalization of the classical fast Fourier transform (FFT) in that it uses "butterflies" to represent an orthonormal transform. However, unlike the FFT, the SMT can be used for fast eigen-signal analysis of general non-stationary signals.

Recently, the Sparse Matrix Transform (SMT) has been proposed as a tool for estimating the eigen-decomposition of high dimensional data vectors. The SMT approach has two major advantages: First it can improve the accuracy of the eigendecomposition, particularly when the number of observations, n, is less the the vector dimension, p. Second, the resulting SMT eigen-decomposition is very fast to apply, i.e. O(p). In this paper, we present an SMT eigen-decomposition method suited for application to signals that live on graphs. This new SMT eigen-decomposition method has two major advantages over the more generic method presented in. First, the resulting SMT can be more accurately estimated due to the graphical constraint. Second, the computation required to design the SMT from training data is dramatically reduced from an average observed complexity of p3to p log p.

In this paper, we develop a classification method for high-dimensional data based on the Sparse Matrix Transform (SMT). The recently proposed SMT has been shown to produce more accurate estimates of covariance matrices when the number of training samples n is much less than the number of dimensions p of the data. Here we introduce a classifier that uses the SMT to model the covariance structure of the data. Experiments in face recognition using the FERET face database show that our method is superior to a conceptually very similar and low-dimensional method in at least two key aspects: First, the SMT classifier is more robust to the size of the training set, remaining accurate even when only a few training samples are available; Second, the total computation required to apply the SMT classifier to high-dimensional data is very low, making this method attractive for use in low-power and mobile devices, or in application settings requiring fast computation.

The design and development of a new computer system is a lengthy process, with a considerable amount of time elapsed between the beginning of development and first hardware availability. Hence, fast and reasonably accurate simulation of processor architecture has become critical as an enabling mechanism for software engineers to develop and tune system software and applications. In this paper, we present the time-stamped timing model extensions to the BlueGene/L functional simulator. These extensions were implemented to create a pseudo cycle-accurate simulator capable of providing tracing capabilities for detection of bottlenecks and for performance tuning of applications, before the actual hardware became available. Our validation tests, using the DAXPY kernel and the serial version of the NAS benchmarks, show that our pseudo cycle-accurate simulator provides timing information within 15% of the times measured using the actual BlueGene/L hardware. In addition, we present a couple of case studies, which describes how this simulator can be used for identification of performance bottlenecks and for application tuning.

BlueGene/L is a massively parallel computer system with 65536 dual-processor compute nodes. The peak performance of BlueGene/L is in excess of 360 TFLOP/s if both processor cores in a node are used for computation. The main challenge of deploying this dual-core mode of operation is that the L1 caches in each core are not hardware coherent. This forces a software-based approach to cache coherence and guides our design of a programming model for dual-core mode. We describe the design, implementation, and performance evaluation of system software for enabling the use of dual-core mode on BlueGene/L. Our preliminary performance results show that our approach to dual-core mode is effective for key numerical kernels.