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Titlebook: Big Data Benchmarks, Performance Optimization, and Emerging Hardware; 6th Workshop, BPOE 2 Jianfeng Zhan,Rui Han,Roberto V. Zicari Conferen

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發(fā)表于 2025-3-28 16:33:40 | 只看該作者
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發(fā)表于 2025-3-28 21:24:34 | 只看該作者
Introduction to Artificial Intelligencestems and high performance computing. Through prolonged and unremitting efforts, benchmarks on these domains have been reaching their maturity gradually. However, in terms of emerging scenarios of big data, its different properties in data volume, data types, data processing requirements and techniq
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發(fā)表于 2025-3-28 23:03:14 | 只看該作者
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發(fā)表于 2025-3-29 04:33:38 | 只看該作者
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發(fā)表于 2025-3-29 14:03:31 | 只看該作者
Protected-Mode Interrupt Processingility evaluation. But in practice a multicore machine usually hosts a mix of concurrent programs for better resource utilization. In this paper, we show that this lack of mixed workloads in evaluation is inadequate at predicting real-world behavior especially in the spectrum of big data and latency-
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發(fā)表于 2025-3-29 17:25:47 | 只看該作者
48#
發(fā)表于 2025-3-29 21:14:43 | 只看該作者
https://doi.org/10.1007/b138691g insights from big data. While Apache Spark is gaining popularity for exhibiting superior scale-out performance on the commodity machines, the impact of data volume on the performance of Spark based data analytics in scale-up configuration is not well understood. We present a deep-dive analysis of
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