Conference Paper

PEGASUS: A peta-scale graph mining system - Implementation and observations

SCS, Carnegie Mellon Univ., Pittsburgh, PA, USA
DOI: 10.1109/ICDM.2009.14 Conference: Data Mining, 2009. ICDM '09. Ninth IEEE International Conference on
Source: DBLP

ABSTRACT

In this paper, we describe PEGASUS, an open source peta graph mining library which performs typical graph mining tasks such as computing the diameter of the graph, computing the radius of each node and finding the connected components. as the size of graphs reaches several giga-, tera- or peta-bytes, the necessity for such a library grows too. To the best of our knowledge, PEGASUS is the first such library, implemented on the top of the HADOOP platform, the open source version of MAPREDUCE. Many graph mining operations (PageRank, spectral clustering, diameter estimation, connected components etc.) are essentially a repeated matrix-vector multiplication. In this paper we describe a very important primitive for PEGASUS, called GIM-V (generalized iterated matrix-vector multiplication). GIM-V is highly optimized, achieving (a) good scale-up on the number of available machines (b) linear running time on the number of edges, and (c) more than 5 times faster performance over the non-optimized version of GIM-V. Our experiments ran on M45, one of the top 50 supercomputers in the world. We report our findings on several real graphs, including one of the largest publicly available Web graphs, thanks to Yahoo!, with ¿ 6,7 billion edges.

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    • "Similar with irregular reductions, graph algorithms can also be summarized as a computation around a sparse matrix (the graph), and a vertex array. The work of project PEGASUS[13]has already shown how a number of graph mining applications can be viewed as a generalization of sparse matrix vector multiplication (SpMV). Specifically, for each non-zero at the position (i, j) in the sparse matrix, three major steps are followed: 1) reading the value v i of Vertex i, 2) conducting computation using v i and the value of the edge e i,j from the sparse matrix, and 3) updating the value v j of Vertex j using the computation result from the Step 2. "
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    • "In order to achieve scalable graph computing, researchers have proposed many distributed or single machine solutions [3]–[23]. Representative distributed systems include Power- Graph [18], Giraph [15], Pregel [16], GraphLab [17],GraphX [19], PEGASUS [20], and etc. Some of these systems are developed based on popular distributed computing frameworks , such as MapReduce [24] and Spark [25]. "
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