Evaluating MapReduce for Multi-core and Multiprocessor Systems

By Colby Ranger, Ramanan Raghuraman, Arun Penmetsa, Gary R. Bradski, Christos Kozyrakis. 2007 IEEE 13th International Symposium on High Performance Computer Architecture, 2007

1,112 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Hardware and Architecture, Information Systems

View Paper: https://www.semanticscholar.org/paper/5738b257b7bdd8e07de817f06ccb5415f3b3f8f0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Robert Morris
2. M. Frans Kaashoek
3. Bingsheng He
4. Qiong Luo
5. Tuyong Wang
6. Keqiu Li
7. R. Ranjan
8. Jie Tao

In Hardware and Architecture

1. Tuyong Wang
2. Keqiu Li
3. Bingsheng He
4. R. Ranjan
5. Jie Tao
6. Ming Wu
7. Jimmy J. Lin
8. Geoffrey C. Fox

In Information Systems

1. Michael Schwarz
2. Moritz Lipp
3. Tuyong Wang
4. Keqiu Li
5. Bingsheng He
6. Qing He
7. R. Ranjan
8. Jie Tao

Citations in the corpus

• Corey: An Operating System for Many Cores (2008, positive): “Phoenix MapReduce [16] implementation has its own dynamically load-balancing scheduler) and, if necessary, use pcore set interval to implement preemptive scheduling policies.”
• X-Stream: edge-centric graph processing using streaming partitions (2013, methodology): “Inspired by solutions to similar problems in cacheconscious sorting [51] and in systems such as Phoenix [45] or tiled map-reduce [24], we implemented a multi-stage shuffler for the in-memory engine in XStream.”
• Review of Bridge Structural Health Monitoring Aided by Big Data and Artificial Intelligence: From Condition Assessment to Damage Detection (2020, neutral): “Most of them have been used in small dataset for a long time, their performance in big dataset became a research topic after BD computing techniques emerged (Chu et al. 2017; Ranger et al. 2007).”
• Big Data Processing in Cloud Computing Environments (2012, positive): “In addition, C. Ranger et al.[27] implement the MapReduce framework on multiple processors in a single machine, which has gained good performance.”
• Book Reviews: Data-Intensive Text Processing with MapReduce by Jimmy Lin and Chris Dyer (2010, background): “sors [90], for GPGPUs [49], for the CELL architecture [89], etc.”
• Mars: A MapReduce Framework on graphics processors (2008, background): “First, the synchronization overhead in the runtime system of the framework must be low so that the system can scale to hun­dreds of processors.”
• SystemML: Declarative machine learning on MapReduce (2011, methodology): “These algorithms have been implemented over multiple MapReduce architectures [7], [8], [9] ranging from multicores [2] to proprietary [10], [11], [12] and open source implementations [13].”
• Trends in big data analytics (2014, neutral): “performance in different environments [47,76].”
• G-Hadoop: MapReduce across distributed data centers for data-intensive computing (2013, methodology): “Other MapReduce implementations are available for various architectures, such as for CUDA [26], in a multicore architecture [27], in FPGA platforms [28], for amultiprocessor architecture [29], in a large-scale shared-memory system [30], in a large-scale cluster [31], in multiple virtual machines [32”
• Data mining with big data (2016, methodology): “[39] proposed a MapReduce-based application programming interface Phoenix, which supports parallel programming in the environment of multicore and multiprocessor systems, and realized three data mining algorithms including k-Means, principal component analysis, and linear regression.”