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Driven by the rapid hardware development of parallel CPU/GPU architectures, we have witnessed emerging relational query processing techniques and implementations on those parallel architectures. However, most of those implementations are not portable across different architectures, because they are usually developed from scratch and target at a specific(More)
Recently, there have been some emerging processor designs that the CPU and the GPU (Graphics Processing Unit) are integrated in a single chip and share Last Level Cache (LLC). However, the main memory bandwidth of such coupled CPU-GPU architectures can be much lower than that of a discrete GPU. As a result, current GPU query co-processing paradigms can(More)
MapReduce, originally developed by Google for search applications, has recently become a popular programming framework for parallel and distributed environments. This paper presents an energy-efficient architecture design for MapReduce on Field Programmable Gate Arrays (FPGAs). The major goal is to enable users to program FPGAs with simple MapReduce(More)
Architecture designers tend to integrate both CPU and GPU on the same chip to deliver energy-efficient designs. To effectively leverage the power of both CPUs and GPUs on integrated architectures, researchers have recently put substantial efforts into co-running a single application on both the CPU and the GPU of such architectures. However, few studies(More)
Architecture designers tend to integrate both CPUs and GPUs on the same chip to deliver energy-efficient designs. It is still an open problem to effectively leverage the advantages of both CPUs and GPUs on integrated architectures. In this work, we port 42 programs in Rodinia, Parboil, and Polybench benchmark suites and analyze the co-running behaviors of(More)
Environmental problems have attracted increasing attention, yet individuals' connectedness to nature remains a significant concern for potential solutions to these problems. In this article, we propose a novel method to promote connectedness to nature: mindful learning. One hundred and thirty-four students participated in the experiment. First, baseline(More)
This paper describes our efforts to apply various advanced supervised machine learning and natural language processing techniques, including Binomial Logistic Regression, Support Vector Machines, Neural Networks, Ensemble Techniques, and Latent Dirichlet Allocation (LDA), to the problem of detecting fraud in financial reporting documents available from the(More)
In the big data era, in-memory data analytics is an effective means of achieving high performance data processing and realizing the value of data in a timely manner. Efforts in this direction have been spent on various aspects, including in-memory algorithmic designs and system optimizations. In this paper, we propose to develop the next-generation(More)
Avoiding threatened ventriculostomy shunt exposure in the pediatric population remains a difficult problem for the neurosurgeon and reconstructive surgeon. In this case series, the authors present a novel method of augmenting scalp soft tissue with acellular dermal matrix (ADM) in patients with a history of ventricular shunt revisions. Soft tissue(More)
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