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Titlebook: Advanced Parallel Processing Technologies; 10th International S Chenggang Wu,Albert Cohen Conference proceedings 2013 Springer-Verlag Berli

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楼主: antithetic
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H. Tristram Engelhardt Jr.,Lisa M. Rasmussenent advances of Task-Parallel models add implicit synchronization, where the system automatically detects and satisfies data dependencies among spawned tasks. However, dynamic dependence analysis incurs significant runtime overheads, because the runtime must track task resources and use this informa
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Michael D. Fetters,Marion Danisrovide scalable system performance and cost-effective property. However, virtual machines (VMs) running on NUMA systems will access remote memory and contend for shared on-chip resources, which will decrease the overall performance of VMs and reduce the efficiency, fairness, and QoS that a virtualiz
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The Physician: Professional or Entrepreneurements of multi-/many-core based embedded systems. Barrelfish is a multi-/many-core oriented open-source operating system built by ETH Zurich and Microsoft Research in a multi-kernel style. Every kernel runs on a dedicated core with local interrupts disabled, which may result in a failure in real-ti
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,Creating Children to Save Siblings’ Lives,upt can be quite difficult to fully understand, and many catastrophic system failures are caused by unexpected behaviors. Therefore, interrupt-driven systems need high quality tests, but there is lack of effective interrupt system detection method at present. In this paper, a modeling method of inte
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https://doi.org/10.1007/978-1-59259-445-0ta-race analysis suffers from high false positive rate. A key reason is that static analysis overestimates the set of shared objects a thread can access. We propose thread specialization to distinguish threads statically. By fixing the number of threads as well as the ID assigned to each thread, a p
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https://doi.org/10.1007/1-4020-4241-8with high detection accuracy is implemented on a low-cost field-programmable gate array (FPGA) platform. For the histogram of oriented gradients feature and linear support vector machine classifier, the binarization process is employed instead of normalization, as the original algorithm is unsuitabl
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