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Support vector data description (SVDD) is a popular technique for detecting anomalies. The SVDD classifier partitions the whole space into an inlier region, which consists of the region near the training data, and an outlier region, which consists of points away from the training data. The computation of the SVDD classifier requires a kernel function, and the Gaussian kernel is a common choice for...
Reducing operation and the maintenance costs of wind turbines has become a primary issue of wind farm owners and operators. Since the supervisory control and data acquisition (SCADA) system has been widely used in wind farms, it is costeffective to use SCADA data to realize condition monitoring. To this end, this paper proposes a method to calculate health index of wind turbines based on SCADA data...
Increasing use of online backup services, as well as the popularity of user-generated content, has increased the demand for bandwidth. However, traffic generated by these applications can impact on the responsiveness of delay-sensitive applications if they receive a 'fair-share' of the available bandwidth. Less-than-Best-Effort TCP congestion control mechanisms aim to allow lower-priority applications...
Network performance is one of the most important entities in today’s long-distance networks. TCP congestion control mechanisms play an important role in these networks. Most of the current TCP congestion control mechanisms which are also known as TCP variants, detect congestion and slow down the packets transmission to avoid further congestion in the network. In this paper, three classes...
State-of-the-art storage devices that have parallel capability have significantly reduced the performance gap between processor and storage I/O. However, the internal parallelism makes it difficult to measure utilization that can be used as a basis of load balancing, which is a critical feature of performance improvement of parallel systems. When utilization of storage reaches to one hundred percent,...
Multipath TCP is a recent TCP extension that enables the utilization of different paths for a single connection. This provides various benefits including bandwidth aggregation and fast handovers on mobiles. A Multipath TCP connection starts with a single TCP connection called subflow and other subflows are added later to increase bandwidth or support failover. One drawback of Multipath TCP is that...
With wafer fabs running at near full capacity, it is a constant challenge to maintain high yields. Many different products are fabricated by the same equipment. So the sudden change in product yield, a yield excursion, can have a significant impact to many different products. Therefore, it is critical to detect an excursion as early as possible and fix the cause in order to minimize the impact. This...
Due to energy efficiency, heterogeneous computing is gaining more and more attention. Since FPGA implementations are time consuming, high-level synthesis (HLS) is used to close the productivity gap. OpenCL has become accepted as a good programming model for HLS, due to its portability, good capability of design verification and rich instruction set. This work implements different optimization strategies...
Accelerators are specialized hardware designs that generally guarantee two to three orders of magnitude higher energy efficiency than general-purpose processor cores for their target computational kernels. To cope with the complexity of integrating many accelerators into heterogeneous systems, we have proposed Embedded Scalable Platforms (ESP) that combines a flexible architecture with a companion...
The emergence of power efficiency as a primary constraint in processor and system designs poses new challenges concerning power and energy awareness for numerical libraries and scientific applications. Power consumption also plays a major role in the design of data centers in particular for peta- and exa-scale systems. Understanding and improving the energy efficiency of numerical simulation becomes...
The performance of commodity video-gaming embedded devices (consoles, graphics cards, tablets, etc.) has been advancing at a rapid pace owing to strong consumer demand and stiff market competition. Gaming devices are currently amongst the most powerful and cost-effective computational technologies available in quantity. In this article, we evaluate a sample of current generation video-gaming devices...
Intel®'s Xeon® processor with integrated FPGA is a new research platform that provides all the capabilities of a Broadwell Xeon Processor with the added functionality of an Arria 10 FPGA in the same package. In this paper, we present an implementation on this platform to showcase the abilities and effectiveness of utilizing both hardware architectures to accelerate a convolutional based neural network...
This paper presents the details of a CUDA implementation of the Subgraph Isomorphism Graph Challenge, a new effort aimed at driving progress in the graph analytics field. challenge consists of two graph analytics: triangle counting and k-truss. We present our CUDA implementation of the graph triangle counting operation and of the k-truss subgraph decomposition. Both implementations share the same...
Common spatial patterns (CSP) is a widely used method in the field of electroencephalogram (EEG) signal processing. The goal of CSP is to find spatial filters that maximize the ratio between the variances of two classes. The conventional CSP is however sensitive to outliers because it is based on the L2-norm. Inspired by the correntropy induced metric (CIM), we propose in this work a new algorithm,...
The Gaussian kernel least-mean-square (Gaussian KLMS) algorithm has been studied under different implementation conditions. Though analytical models that predict its behavior are available, methodologies for determining the algorithm parameter values to satisfy given design criteria is still missing from the literature. In this paper we propose, test, and validate a methodology for the design of the...
In this paper we propose a cluster based version of the anomaly detection methodology based on signal reconstruction, using Auto Associative Kernel Regression (AAKR), combined with residuals analysis, using Sequential Probability Ratio Test (SPRT). We demonstrate how the proposed cluster based methodology can be successfully applied for anomaly detection on a marine diesel engine in operation. Furthermore,...
Machine Learning (ML) approaches are widelyused classification/regression methods for data mining applications. However, the time-consuming training process greatly limits the efficiency of ML approaches. We use the example of SVM (traditional ML algorithm) and DNN (state-of-the-art ML algorithm) to illustrate the idea in this paper. For SVM, a major performance bottleneck of current tools is that...
The exponential growth of available data has increased the need for interactive exploratory analysis. Dataset can no longer be understood through manual crawling and simple statistics. In Geographical Information Systems (GIS), the dataset is often composed of events localized in space and time; and visualizing such a dataset involves building a map of where the events occurred.We focus in this paper...
This paper presents a low-overhead optimizer for the ubiquitous sparse matrix-vector multiplication (SpMV) kernel. Architectural diversity among different processors together with structural diversity among different sparse matrices lead to bottleneck diversity. This justifies an SpMV optimizer that is both matrix- and architecture-adaptive through runtime specialization. To this direction, we present...
We address the problem of optimizing global shared memory usage in deeply heterogeneous accelerators in the context of HPC systems running multiple applications with different quality of service levels. We explore predictive memory allocation algorithms, allowing to serve up to 28% more high priority requests when using a moving average based prediction in a low-workload scenario.
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