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10.1109/BigData.2014.7004226 http://scihub22266oqcxt.onion/10.1109/BigData.2014.7004226 C4389765!4389765!25866846     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Proc+IEEE+Int+Conf+Big+Data 2014 ; 2014 (ä): 159-64 Nephropedia Template TP {{tp|p=25866846|t=2014. MMap: Fast Billion-Scale Graph Computation on a PC via Memory Mapping |pdf=|usr=}}{{25866846}} gab.com Text MMap: Fast Billion-Scale Graph Computation on a PC via Memory Mapping JO: Proc IEEE Int Conf Big Data http://www.kidney.de/pget.php?&tw=1&pmid=25866846 #FOAvip Graph computation approaches such as GraphChi and TurboGraph recently demonstrated that a single PC can perform efficient computation on billion-node graphs. To achieve high speed and scalability, they often need sophisticated data structures and memory management strategies. We propose a minimalist approach that forgoes such requirements, by leveraging the fundamental memory mapping (MMap) capability found on operating systems. We contribute: (1) a new insight that MMap is a viable technique for creating fast and scalable graph algorithms that surpasses some of the best techniques; (2) the design and implementation of popular graph algorithms for billion-scale graphs with little code, thanks to memory mapping; (3) extensive experiments on real graphs, including the 6.6 billion edge YahooWeb graph, and show that this new approach is significantly faster or comparable to the highly-optimized methods (e.g., 9.5× faster than GraphChi for computing PageRank on 1.47B edge Twitter graph). We believe our work provides a new direction in the design and development of scalable algorithms. Our packaged code is available at http://poloclub.gatech.edu/mmap/. Twit Text FOAVip MMap: Fast Billion-Scale Graph Computation on a PC via Memory Mapping ????Proc IEEE Int Conf Big Data http://www.kidney.de/pget.php?&tw=1&pmid=25866846 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25866846 #FOAvip English Wikipedia <ref>{{Cite pmid|25866846}}</ref> MMap: Fast Billion-Scale Graph Computation on a PC via Memory Mapping #MMPMID25866846 Lin Z; Kahng M; Sabrin KM; Chau DH(; Lee H; Kang U Proc IEEE Int Conf Big Data 2014[Oct]; 2014 (ä): 159-64 PMID25866846show ga Graph computation approaches such as GraphChi and TurboGraph recently demonstrated that a single PC can perform efficient computation on billion-node graphs. To achieve high speed and scalability, they often need sophisticated data structures and memory management strategies. We propose a minimalist approach that forgoes such requirements, by leveraging the fundamental memory mapping (MMap) capability found on operating systems. We contribute: (1) a new insight that MMap is a viable technique for creating fast and scalable graph algorithms that surpasses some of the best techniques; (2) the design and implementation of popular graph algorithms for billion-scale graphs with little code, thanks to memory mapping; (3) extensive experiments on real graphs, including the 6.6 billion edge YahooWeb graph, and show that this new approach is significantly faster or comparable to the highly-optimized methods (e.g., 9.5× faster than GraphChi for computing PageRank on 1.47B edge Twitter graph). We believe our work provides a new direction in the design and development of scalable algorithms. Our packaged code is available at http://poloclub.gatech.edu/mmap/. äMMap: Fast Billion-Scale Graph Computation on a PC via Memory Mapping (epmc).pdf DeepDyve Pubget Overpricing