Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: CPU- and GPU-based Distributed Sampling in Dirichlet Process Mixtures
  for Large-scale Analysis   In the realm of unsupervised learning, Bayesian nonparametric mixture models,
exemplified by the Dirichlet Process Mixture Model (DPMM), provide a principled
approach for adapting the complexity of the model to the data. Such models are
particularly useful in clustering tasks where the number of clusters is
unknown. Despite their potential and mathematical elegance, however, DPMMs have
yet to become a mainstream tool widely adopted by practitioners. This is
arguably due to a misconception that these models scale poorly as well as the
lack of high-performance (and user-friendly) software tools that can handle
large datasets efficiently. In this paper we bridge this practical gap by
proposing a new, easy-to-use, statistical software package for scalable DPMM
inference. More concretely, we provide efficient and easily-modifiable
implementations for high-performance distributed sampling-based inference in
DPMMs where the user is free to choose between either a multiple-machine,
multiple-core, CPU implementation (written in Julia) and a multiple-stream GPU
implementation (written in CUDA/C++). Both the CPU and GPU implementations come
with a common (and optional) python wrapper, providing the user with a single
point of entry with the same interface. On the algorithmic side, our
implementations leverage a leading DPMM sampler from (Chang and Fisher III,
2013). While Chang and Fisher III's implementation (written in MATLAB/C++) used
only CPU and was designed for a single multi-core machine, the packages we
proposed here distribute the computations efficiently across either multiple
multi-core machines or across mutiple GPU streams. This leads to speedups,
alleviates memory and storage limitations, and lets us fit DPMMs to
significantly larger datasets and of higher dimensionality than was possible
previously by either (Chang and Fisher III, 2013) or other DPMM methods.

17
The categories of this paper are: q-fin
7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
3
The categories of this paper are: physics
10
The categories of this paper are: q-bio
19
The categories of this paper are: eess
9
The categories of this paper are: nlin
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
28
The categories of this paper are: alg-geom