Herein is described a data-parallel algorithm for topic modeling in which the memory requirements are streamlined for implementation on a highly-parallel architecture, such as a GPU. Specifically, approximate counters are used in a large mixture model or clustering algorithm (e.g., an uncollapsed Gibbs sampler) to decrease memory usage over what is required when conventional counters are used. The decreased memory usage of the approximate counters allows a highly-parallel architecture with limited memory to process more computations for the large mixture model more efficiently. Embodiments describe binary Morris approximate counters, general Morris approximate counters, and Csrös approximate counters in the context of an uncollapsed Gibbs sampler, and, more specifically, for a Greedy Gibbs sampler.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for identifying sets of correlated words comprising: receiving information about a set of documents; wherein the set of documents comprises a plurality of words; running an uncollapsed Gibbs sampler over a Dirichlet distribution of the plurality of words in the set of documents to produce sampler result data, further comprising: representing one or more counts in the uncollapsed Gibbs sampler using one or more approximate counters; determining, from the sampler result data, one or more sets of correlated words; wherein the method is performed by one or more computing devices. 2. The method of claim 1, wherein the one or more approximate counters comprise binary Morris approximate counters. 3. The method of claim 1, wherein the one or more approximate counters comprise general Morris approximate counters. 4. The method of claim 1, wherein the one or more approximate counters comprise Csrös approximate counters. 5. The method of claim 1, wherein running the uncollapsed Gibbs sampler over the Dirichlet distribution of the plurality of words in the set of documents to produce sampler result data, further comprises representing one or more other counts in the Gibbs sampler using one or more conventional counters. 6. The method of claim 1, wherein the one or more approximate counters comprise two or more of: binary Morris approximate counters, general Morris approximate counters, Csrös approximate counters, or conventional counters. 7. The method of claim 1, wherein: running the uncollapsed Gibbs sampler over the Dirichlet distribution comprises computing in parallel a plurality of values, including the one or more counts, for the uncollapsed Gibbs sampler; and the plurality of values are computed in a plurality of parallel Single Program Multiple Data (SPMD) units on a graphics processing unit (GPU). 8. The method of claim 1, further comprising representing each of the one or more approximate counters using eight bits or fewer than eight bits. 9. The method of claim 1, wherein the uncollapsed Gibbs sampler has at least one variable uncollapsed. 10. The method of claim 1, wherein the uncollapsed Gibbs sampler is a Greedy Gibbs sampler. 11. One or more non-transitory computer-readable media storing sequences of instructions for identifying sets of correlated words, wherein said sequences of instructions, when executed by one or more processors, cause: receiving information about a set of documents; wherein the set of documents comprises a plurality of words; running an uncollapsed Gibbs sampler over a Dirichlet distribution of the plurality of words in the set of documents to produce sampler result data, further comprising: representing one or more counts in the uncollapsed Gibbs sampler using one or more approximate counters; determining, from the sampler result data, one or more sets of correlated words. 12. The one or more non-transitory computer-readable media of claim 11, wherein the one or more approximate counters comprise binary Morris approximate counters. 13. The one or more non-transitory computer-readable media of claim 11, wherein the one or more approximate counters comprise general Morris approximate counters. 14. The one or more non-transitory computer-readable media of claim 11, wherein the one or more approximate counters comprise Csrös approximate counters. 15. The one or more non-transitory computer-readable media of claim 11, wherein running the uncollapsed Gibbs sampler over the Dirichlet distribution of the plurality of words in the set of documents to produce sampler result data, further comprises representing one or more other counts in the Gibbs sampler using one or more conventional counters. 16. The one or more non-transitory computer-readable media of claim 11, wherein the one or more approximate counters comprise two or more of: binary Morris approximate counters, general Morris approximate counters, Csrös approximate counters, or conventional counters. 17. The one or more non-transitory computer-readable media of claim 11, wherein: running the uncollapsed Gibbs sampler over the Dirichlet distribution comprises computing in parallel a plurality of values, including the one or more counts, for the uncollapsed Gibbs sampler; and the sequences of instructions include instructions, that when executed by said one or more processors, causes the plurality of values to be computed in a plurality of parallel Single Program Multiple Data (SPMD) units on a graphics processing unit (GPU). 18. The one or more non-transitory computer-readable media of claim 11, wherein the sequences of instructions include instructions, that when executed by said one or more processors, cause representing each of the one or more approximate counters using eight bits or fewer than eight bits. 19. The one or more non-transitory computer-readable media of claim 11, wherein the uncollapsed Gibbs sampler has at least one variable uncollapsed. 20. The one or more non-transitory computer-readable media of claim 11, wherein the uncollapsed Gibbs sampler is a Greedy Gibbs sampler.