標題: Titlebook: Big Data Benchmarks, Performance Optimization, and Emerging Hardware; 6th Workshop, BPOE 2 Jianfeng Zhan,Rui Han,Roberto V. Zicari Conferen [打印本頁] 作者: Abridge 時間: 2025-3-21 19:45
書目名稱Big Data Benchmarks, Performance Optimization, and Emerging Hardware影響因子(影響力)
書目名稱Big Data Benchmarks, Performance Optimization, and Emerging Hardware影響因子(影響力)學科排名
書目名稱Big Data Benchmarks, Performance Optimization, and Emerging Hardware網(wǎng)絡(luò)公開度
書目名稱Big Data Benchmarks, Performance Optimization, and Emerging Hardware網(wǎng)絡(luò)公開度學科排名
書目名稱Big Data Benchmarks, Performance Optimization, and Emerging Hardware被引頻次
書目名稱Big Data Benchmarks, Performance Optimization, and Emerging Hardware被引頻次學科排名
書目名稱Big Data Benchmarks, Performance Optimization, and Emerging Hardware年度引用
書目名稱Big Data Benchmarks, Performance Optimization, and Emerging Hardware年度引用學科排名
書目名稱Big Data Benchmarks, Performance Optimization, and Emerging Hardware讀者反饋
書目名稱Big Data Benchmarks, Performance Optimization, and Emerging Hardware讀者反饋學科排名
作者: 不能平靜 時間: 2025-3-21 21:33 作者: COMA 時間: 2025-3-22 02:24
Arithmetic Flags and Instructions graphs depict similar structures, their tiny differences may result in?totally different storage and indexing strategies, that should not be omitted. Finally, we put forward the requirements to seeding datasets and synthetic data generators for benchmarking knowledge graph management based on the s作者: Orthodontics 時間: 2025-3-22 05:10 作者: NEXUS 時間: 2025-3-22 09:12
Arithmetic Flags and Instructionsop 2.5 and 2.6, Hortonworks HDP, and Cloudera CDH. Performance evaluations show that our plugin ensures the expected performance of up?to 3.7x improvement in TestDFSIO write, associated with the hybrid RDMA-enhanced design, to all these distributions. We also demonstrate that our RDMA-based plugin c作者: 召集 時間: 2025-3-22 13:46 作者: 精確 時間: 2025-3-22 19:20
BigDataBench-MT: A Benchmark Tool for Generating Realistic Mixed Data Center Workloadsoad traces, and a multi-tenant generator that flexibly scales the workloads up and down according to users’ requirements. Based on this, our demo illustrates the workload customization and generation process using a visual interface. The proposed tool, called BigDataBench-MT, is a multi-tenant versi作者: 彩色的蠟筆 時間: 2025-3-23 00:00 作者: 同謀 時間: 2025-3-23 04:51 作者: 動機 時間: 2025-3-23 09:12
A Plugin-Based Approach to Exploit RDMA Benefits for Apache and Enterprise HDFSop 2.5 and 2.6, Hortonworks HDP, and Cloudera CDH. Performance evaluations show that our plugin ensures the expected performance of up?to 3.7x improvement in TestDFSIO write, associated with the hybrid RDMA-enhanced design, to all these distributions. We also demonstrate that our RDMA-based plugin c作者: 植物群 時間: 2025-3-23 12:45 作者: ADORN 時間: 2025-3-23 15:16 作者: Surgeon 時間: 2025-3-23 19:18
0302-9743 erformance Optimization, and Emerging Hardware, BPOE 2015, held in Kohala Coast, HI, USA, in August/September 2015 as satellite event of VLDB 2015, the 41st International Conference on Very Large Data Bases..The 8 papers presented were carefully reviewed and selected from 10 submissions. The worksho作者: 憎惡 時間: 2025-3-23 23:23
Arithmetic Flags and Instructionsa lot comparing with the original version. We analyzed on which aspects Spark have made efforts to support many workloads efficiently and whether the changes make the support for SQL achieve better performance.作者: textile 時間: 2025-3-24 04:12 作者: 愛了嗎 時間: 2025-3-24 10:05
Conference proceedings 2016are, BPOE 2015, held in Kohala Coast, HI, USA, in August/September 2015 as satellite event of VLDB 2015, the 41st International Conference on Very Large Data Bases..The 8 papers presented were carefully reviewed and selected from 10 submissions. The workshop focuses on architecture and system suppor作者: 爭論 時間: 2025-3-24 12:00 作者: SENT 時間: 2025-3-24 18:05
https://doi.org/10.1007/b138691 and analytics-aware. In addition, the benchmark also provides application plug-ins that allow for compelling demonstration of big data solutions. We describe the benchmark design challenges, an early prototype and three use cases.作者: Desert 時間: 2025-3-24 19:00
https://doi.org/10.1007/b138691ition interval to achieve better performance. The algorithm has been applied to a large management system project. Experimental results show that with the help of dynamic adjusting mechanism, the proposed approach can provide reliable collection service for common data acquisition systems.作者: Condense 時間: 2025-3-24 23:48
Revisiting Benchmarking Principles and Methodologies for Big Data Benchmarkingthis paper, we revisit successful benchmarks in other domains from two perspectives: benchmarking principles which define fundamental rules, and methodologies which guide the benchmark constructions. Further, we conclude the benchmarking principle and methodology on big data benchmarking from a recent open-source effort – BigDataBench.作者: 輕快走過 時間: 2025-3-25 05:54 作者: HEED 時間: 2025-3-25 08:14 作者: 靈敏 時間: 2025-3-25 12:43
Conference proceedings 2016 to discuss the research issues at the intersection of these areas. This book also invites three papers from several industrial partners, including two papers describing tools used in system benchmarking and monitoring and one paper discussing principles and methodologies in existing big data benchmarks.作者: Mobile 時間: 2025-3-25 16:25 作者: 直覺好 時間: 2025-3-25 21:03 作者: 感情 時間: 2025-3-26 00:31
How Data Volume Affects Spark Based Data Analytics on a Scale-up Server wait time during I/O operations and garbage collection, despite 10?% better instruction retirement rate (due to lower L1 cache misses and higher core utilization). We match memory behaviour with the garbage collector to improve performance of applications between 1.6x to 3x.作者: 工作 時間: 2025-3-26 05:45 作者: Hippocampus 時間: 2025-3-26 10:43 作者: Control-Group 時間: 2025-3-26 16:34 作者: elucidate 時間: 2025-3-26 18:22 作者: 戲服 時間: 2025-3-26 22:16
On Statistical Characteristics of Real-Life Knowledge Graphsboth academic and industrial communities in building common-sense and domain-specific knowledge graphs. A natural question arises that how to effectively and efficiently manage a large-scale knowledge graph. Though systems and technologies that use relational storage engines or native graph database作者: 內(nèi)部 時間: 2025-3-27 03:01 作者: Infuriate 時間: 2025-3-27 06:47 作者: Corporeal 時間: 2025-3-27 13:00 作者: Abrupt 時間: 2025-3-27 15:11
An Optimal Reduce Placement Algorithm for Data Skew Based on Samplingperformance bottle-neck in most running Hadoop systems. This paper proposes a reduce placement algorithm called CORP to schedule related map and reduce tasks on the near nodes or clusters or racks for the data locality. Since the number of keys cannot be counted until the input data are processed by作者: 吞沒 時間: 2025-3-27 19:46
AAA: A Massive Data Acquisition Approach in Large-Scale System Monitoringf metrics from each device for real-time anomaly detection, alerting and analysis. It is a great challenge to realize real-time and reliable data collection and gathering in a data acquisition system for large-scale system. In this paper, we propose an Adaptive window Acquisition Algorithm (AAA) to 作者: hangdog 時間: 2025-3-28 00:49
A Plugin-Based Approach to Exploit RDMA Benefits for Apache and Enterprise HDFSadoop MapReduce, HBase, Hive, and Spark. This makes the performance of HDFS a primary concern in the Big Data community. Recent studies have shown that HDFS cannot completely exploit the performance benefits of RDMA-enabled high performance interconnects like InfiniBand. To solve these performance i作者: 過于平凡 時間: 2025-3-28 02:17
Stream-Based Lossless Data Compression Hardware Using Adaptive Frequency Table Management needs to treat BigData management has been reaching to very high speed. In spite of fast increasing of the BigData, the implementation of the data communication path has become complex due to the electrical difficulties such as noises, crosstalks and reflections of the high speed data connection vi作者: Exaggerate 時間: 2025-3-28 09:52 作者: 進取心 時間: 2025-3-28 12:06 作者: Incise 時間: 2025-3-28 16:33 作者: 路標 時間: 2025-3-28 21:24
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作者: 膽汁 時間: 2025-3-28 23:03 作者: 命令變成大炮 時間: 2025-3-29 04:33 作者: flimsy 時間: 2025-3-29 08:28 作者: CLAP 時間: 2025-3-29 14:03
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-作者: 大門在匯總 時間: 2025-3-29 17:25 作者: Mettle 時間: 2025-3-29 21:14
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
