Issue No. 11 - Nov. (2012 vol. 23)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPDS.2012.43
Zheng Wei , University of Maryland, College Park
Joseph JaJa , University of Maryland, College Park
Current high-throughput algorithms for constructing inverted files all follow the MapReduce framework, which presents a high-level programming model that hides the complexities of parallel programming. In this paper, we take an alternative approach and develop a novel strategy that exploits the current and emerging architectures of multicore processors. Our algorithm is based on a high-throughput pipelined strategy that produces parallel parsed streams, which are immediately consumed at the same rate by parallel indexers. We have performed extensive tests of our algorithm on a cluster of 32 nodes, and were able to achieve a throughput close to the peak throughput of the I/O system: a throughput of 280 MB/s on a single node and a throughput that ranges between 5.15 GB/s (1 Gb/s Ethernet interconnect) and 6.12 GB/s (10 Gb/s InfiniBand interconnect) on a cluster with 32 nodes for processing the ClueWeb09 data set. Such a performance represents a substantial gain over the best known MapReduce algorithms even when comparing the single node performance of our algorithm to MapReduce algorithms running on large clusters. Our results shed a light on the extent of the performance cost that may be incurred by using the simpler, higher level MapReduce programming model for large scale applications.
Indexing, Clustering algorithms, Program processors, Multicore processing, Dictionaries, Throughput, pipeline, Inverted files, MapReduce, multicore processors, cluster, I/O throughput, parallel algorithms, parallel parsing and indexing
J. JaJa and Z. Wei, "An Optimized High-Throughput Strategy for Constructing Inverted Files," in IEEE Transactions on Parallel & Distributed Systems, vol. 23, no. , pp. 2033-2044, 2012.