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• W2783448383 abstract "Latent Dirichlet Allocation (LDA) is a widely used machine learning technique in topic modeling and data analysis. Training large LDA models on big datasets involves dynamic and irregular computation patterns and is a major challenge to both algorithm optimization and system design. In this paper, we present a comprehensive benchmarking of our novel synchronized LDA training system HarpLDA+ based on Hadoop and Java. It demonstrates impressive performance when compared to three other MPI/C++ based state-of-the-art systems, which are LightLDA, F+NomadLDA, and WarpLDA. HarpLDA+ uses optimized collective communication with a timer control for load balance, leading to stable scalability in both shared-memory and distributed systems. We demonstrate in the experiments that HarpLDA+ is effective in reducing synchronization and communication overhead and outperforms the other three LDA training systems." @default.
• W2783448383 created "2018-01-26" @default.
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• W2783448383 date "2017-12-01" @default.
• W2783448383 modified "2023-09-27" @default.
• W2783448383 title "HarpLDA+: Optimizing latent dirichlet allocation for parallel efficiency" @default.
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• W2783448383 doi "https://doi.org/10.1109/bigdata.2017.8257932" @default.
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