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Developing efficient and scalable algorithms for Latent Dirichlet Allocation(LDA) is of wide interest for many applications. Previous work has developed anO(1) Metropolis-Hastings sampling method for each token. However, theperformance is far from being optimal due to random accesses to the parametermatrices and frequent cache misses.  In this paper, we first carefully analyze the memory access efficiency ofexisting algorithms for LDA by the scope of random access, which is the size ofthe memory region in which random accesses fall, within a short period of time.We then develop WarpLDA, an LDA sampler which achieves both the best O(1) timecomplexity per token and the best O(K) scope of random access. Our empiricalresults in a wide range of testing conditions demonstrate that WarpLDA isconsistently 5-15x faster than the state-of-the-art Metropolis-Hastings basedLightLDA, and is comparable or faster than the sparsity aware F+LDA. WithWarpLDA, users can learn up to one million topics from hundreds of millions ofdocuments in a few hours, at an unprecedentedly throughput of 11G tokens persecond.

WarpLDA: a Cache Efficient O(1) Algorithm for Latent Dirichlet Allocation