兽皮
发表于 2025-3-25 04:51:53
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FOIL
发表于 2025-3-25 07:45:19
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Folklore
发表于 2025-3-25 15:14:40
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curettage
发表于 2025-3-25 19:30:43
Performance Benefits of DataMPI: A Case Study with BigDataBench7 % and 50 % speedups compared with those of Hadoop and Spark, respectively. Most of the benefits come from the high-efficiency communication mechanisms in DataMPI. We also notice that the resource (CPU, memory, disk and network I/O) utilizations of DataMPI are also more efficient than those of the other two frameworks.
Carcinoma
发表于 2025-3-25 23:40:33
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发酵
发表于 2025-3-26 00:36:45
Efficient HTTP Based I/O on Very Large Datasets for High Performance Computing with the Libdavix Libes of HTTP. Then, we describe in detail how we solved these issues. Our solutions have been implemented in a toolkit called davix, available through several recent Linux distributions..Finally, we describe the results of our benchmarks where we compare the performance of davix against a HPC specific protocol for a data analysis use case.
acrimony
发表于 2025-3-26 06:01:45
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粗鄙的人
发表于 2025-3-26 11:04:02
https://doi.org/10.1007/978-3-031-41985-0the tool kits are suited for a specific environment. In this paper we propose DSIMBench, a benchmark containing two classic microarray analysis functions with eight different parallel R workflows, and evaluate the benchmark in the IC Cloud testbed platform.
LOPE
发表于 2025-3-26 13:12:27
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颠簸下上
发表于 2025-3-26 19:06:52
Computational Morphology Tasks,ks to evaluate and compare big data systems has become an active topic for both research and industry communities. To date, most of the state-of-the-art big data benchmarks are designed for specific types of systems. Based on our experience, however, we argue that considering the complexity, diversi