1,728 research outputs found

    Garbage collection auto-tuning for Java MapReduce on Multi-Cores

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    MapReduce has been widely accepted as a simple programming pattern that can form the basis for efficient, large-scale, distributed data processing. The success of the MapReduce pattern has led to a variety of implementations for different computational scenarios. In this paper we present MRJ, a MapReduce Java framework for multi-core architectures. We evaluate its scalability on a four-core, hyperthreaded Intel Core i7 processor, using a set of standard MapReduce benchmarks. We investigate the significant impact that Java runtime garbage collection has on the performance and scalability of MRJ. We propose the use of memory management auto-tuning techniques based on machine learning. With our auto-tuning approach, we are able to achieve MRJ performance within 10% of optimal on 75% of our benchmark tests

    Smartphone-based Video of Demodex folliculorum In Biopsied Human Eyelash Follicles

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    The ability of smartphone technology to document static microscopy images has been well documented and is gaining widespread use in ophthalmology, where slit-lamp biomicroscopy is frequently utilized. However, little has been described regarding the use of smartphone technology to relay video of tissue microscopy results to patients, particularly when a tissue sample integrates motility of organisms as a characteristic feature of the disease. Here, we describe the method to use smartphone video to document motility of Demodex folliculorum in human eyelashes, individual results of which can be shown to patients for education and counseling purposes. The use of smartphone video in documenting the motility of organisms may prove to be beneficial in a variety of medical fields; producers of electronic medical records, therefore, may find it helpful to integrate video drop box tools

    Smartphone-based Video of Demodex folliculorum In Biopsied Human Eyelash Follicles

    Get PDF
    The ability of smartphone technology to document static microscopy images has been well documented and is gaining widespread use in ophthalmology, where slit-lamp biomicroscopy is frequently utilized. However, little has been described regarding the use of smartphone technology to relay video of tissue microscopy results to patients, particularly when a tissue sample integrates motility of organisms as a characteristic feature of the disease. Here, we describe the method to use smartphone video to document motility of Demodex folliculorum in human eyelashes, individual results of which can be shown to patients for education and counseling purposes. The use of smartphone video in documenting the motility of organisms may prove to be beneficial in a variety of medical fields; producers of electronic medical records, therefore, may find it helpful to integrate video drop box tools
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