Skip to content

Latest commit

 

History

History
145 lines (126 loc) · 5.12 KB

README.md

File metadata and controls

145 lines (126 loc) · 5.12 KB
Raw

rcgpar - Fit mixture models in HPC environments

rcgpar provides CPU and GPU implementations of a variational inference algorithm for estimating mixture model components from a likelihood matrix in parallel.

Installation

rcgpar is currently only available compiled from source.

Compiling from source

Requirements

  • C++17 compliant compiler.
  • cmake

Optional

  • Compiler with OpenMP support.
  • LibTorch
  • CUDA Toolkit or ROCm

Compiling

Clone the rcgpar repository

git clone https://github.com/tmaklin/rcgpar

enter the directory and run

cd rcgpar
mkdir build
cd build

... and follow the instructions below.

CPU estimation only

in the build/ directory, run

cmake ..
make

This creates the librcgomp library in build/lib/.

GPU acceleration

instead of the above, run

cmake -DCMAKE_LIBTORCH_PATH=/absolute/path/to/libtorch ..
make

where /absolute/path/to/libtorch should be the absolute (!) path to the LibTorch distribution.

This creates the librcgomp and librcggpu libraries in build/lib/.

Usage

Link against librcgomp and/or librcggpu and include the rcgpar.hpp header in your project. This header provides four functions:

  • 'rcgpar::rcg_optl_omp' using OpenMP
  • 'rcgpar::rcg_optl_torch' using LibTorch
  • 'rcgpar::em_torch' a different algorithm using LibTorch.

The LibTorch algorithms will run on the GPU if one is present. Otherwise, they will run on the CPU. These algorithms are faster even when ran on the CPU but rcg_optl_torch consumes more memory than rcg_optl_omp.

rcg_optl_omp, rcg_optl_mpi, rcg_optl_torch, and em_torch

These four functions perform the actual model fitting. All have to be called with the following arguments:

const rcgpar::Matrix<double> &logl:
    KxN row-major order matrix containing the log-likelihoods for theobservations,
    where K is the number of components and N is the number of observations.
const std::vector<double> &log_times_observed:
    N-dimensional vector which contains the natural logarithm of the number
	of times that the N:th row in `logl` should be counted. Useful if many
	rows in the log-likelihood matrix are identical - they can be compressed
	by counting them several times via this argument.
const std::vector<double> &alpha0:
    N-dimensional vector containing the prior parameters of the Dirichlet
	distribution that is used as a conjugate prior in the model. Good
	default choice is to set all entries to 1.
const double &tol:
    The estimation process will terminate once the evidence lower bound
	ELBO changes by less than this value from one iteration to the next.
	Good choices are around 1e-6 and 1e-8, adjust according to your needs.
const uint16_t maxiters:
    Maximum number of iterations to run the optimizer for if the tolerance
	criterion is not fulfilled.
std::ostream &log:
    Print status messages here. Silence the messages by supplying a
	std::ofstream that has not been assigned to any file.

'em_torch' requires the extra argument:

std::string precision:
    Either "float" or "double", which determines the precision of the algorithm.

The optimizers return a KxN rcgpar::Matrix<double> type row-major order matrix, where each row is a probability vector assigning the row to the mixture components.

Note: rcg_optl_mpi assumes that the root process holds the full 'logl' and 'log_times_observed values', which are then distributed from the root process to other processes. Contrary to this, 'alpha0', 'tol', and 'maxiters' are assumed to be present on all processes when calling rcg_optl_mpi.

mixture_components and mixture_components_torch

Use 'rcgpar::mixture_components(_torch)' to transform the matrix from rcg_optl_omp/mpi/torch into a probability vector containing the relative contributions of each mixture component. 'mixture_components(_torch)' takes the following input arguments:

const rcgpar::Matrix<double> &probs:
    The matrix returned from rcg_optl_omp/torch, em_torch, or rcg_optl_mpi.
const std::vector<double> &log_times_observed:
    The N-dimensional vector of log times observed that was used
	as input to the call to rcg_optl_omp/torch, em_torch, or rcg_optl_mpi.

'mixture_components(_torch)' will return a N-dimensional probability vector containing the mixture component proportions.

Creating the input matrix

rcgpar requires the input log-likelihood matrix formatted with the internal rcgpar::Matrix class. If your input log-likelihoods are stored in a flattened vector, you can construct the input object to rcg_optl_omp/mpi with the constructor:

Matrix<double>(std::vector<double> &flattened_logl,
               uint16_t n_mixture_components, uint32_t n_observations)

If your data is stored in a 2D vector, use the following constructor:

Matrix<double>(std::vector<std::vector<double>> &logl_2D)

Note that both constructors assume the data is stored in row-major order.

License

The source code from this project is subject to the terms of the LGPL-2.1 license. A copy of the LGPL-2.1 license is supplied with the project, or can be obtained at https://opensource.org/licenses/LGPL-2.1.