cufQUMC

CUDA Fortran version of QU-fitting with replica exchange MCMC method (parallel tempering).

Prerequisites

• Python 3
• CUDA Fortran 23.1 or greater.
  • You can also use the NVHPC container: https://catalog.ngc.nvidia.com/orgs/nvidia/containers/nvhpc

Installation

• Compile the Fortran code

  $ nvfortran -cuda -cudalib=curand -gpu=cc80,managed -o libcufqumc libcufqumc.f90

Here, "cc" means Compute Capability and "cc80" is for NVIDIA A100. See this page to check the compute capability of the GPU you use.

Usage

• run Python code to perform QUMC

  $ python cufqumc.py <options>

The format of data files is [frequency (in Hz), Stokes Q, Stokes U, error in Q, error in U] (five columns).

usage: cufqumc.py [-h] [-d DATADIR] [-m FITMODEL] -b NBURNIN -s NSAMPLE [-o OUTPUTDIR] [-n NBETA] [-r] [-t NTHREADS]

CUDA Fortran QUMC.

optional arguments:
  -h, --help    show this help message and exit
  -d DATADIR    Directory containing data files.
  -m FITMODEL   Fitting Model. ["delta", "gauss", "tophat", "osul17"] are available.
                e.g. One "delta" and two "gauss" components: -m="{'delta':1,'gauss':2}".
  -b NBURNIN    Number of chain for Burn-in (step widths adjustment) [Required].
  -s NSAMPLE    Number of chain for sampling [Required].
  -o OUTPUTDIR  Output directory ["./out"].
  -n NBETA      The number of Replica [100]. beta=[1] when Nbeta=1.
  -r            Resume MCMC from the last results in "OutputDir" directory [False]. 
                When activated, only "Nburnin", "Nsample", "OutputDir" and "Nthreads" are needed to specify.
  -t NTHREADS   Number of threads per block [1024].

The maximum number of threads per block of recent GPUs is 1024 as long as their compute capability is 2.x and higher. Otherwise, specify the number of threads per block using "-t" option. See also this page.

Faraday components

• "delta": Dirac delta function / Faraday thin source

  $P(\lambda^2)=f_0 e^{2i(\theta_0+\phi_0\lambda^2)}$

• "gauss": Gaussian function / Faraday component with Burn depolarization

  $P(\lambda^2)=f_0 e^{-2\sigma_0^2\lambda^4} e^{2i(\theta_0+\phi_0\lambda^2)} $

• "tophat": Tophat function

  $P(\lambda^2)=f_0 \cfrac{\sin(2\delta\phi_0\lambda^2)}{4\delta\phi_0\lambda^2} e^{2i(\theta_0+\phi_0\lambda^2)}$

• "osul17": O'Sullivan 2017 model. See Eq.2 in O'Sullivan et al. (2017)

  $P(\lambda^2)=f_0 e^{-2\sigma_0^2\lambda^4} \cfrac{\sin(2\delta\phi_0\lambda^2)}{4\delta\phi_0\lambda^2} e^{2i(\theta_0+\phi_0\lambda^2)}$

Notes

This code runs QUMC and outputs parameter chains at beta=1.0 and chisq chains.