ProFit: OpenCL and OpenMP Support

Get the latest version of ProFit:

library(devtools)
install_github('ICRAR/ProFit')

First load the libraries we need:

library(ProFit)

OpenCL support

The support for OpenCL compatible graphics cards is described in a bit of detail in the help for profitOpenCLEnv profitOpenCLEnvInfo. This should generally work more-or-less out the box if you have a compatible card on your local machine (mileage may vary though). An example using the defaults (that uses the first vaialbel card on your machine if possible):

modellist=list(
  sersic=list(
    xcen=c(180, 60),
    ycen=c(90, 10),
    mag=c(15, 13),
    re=c(14, 5),
    nser=c(3, 10),
    ang=c(46, 80),
    axrat=c(0.4, 0.6),
    box=c(0.5,-0.5)
  ),
  pointsource=list(
    xcen=c(34,10,150),
    ycen=c(74,120,130),
    mag=c(10,13,16)
  ),
  sky=list(
    bg=3e-12
  )
)

magimage(profitMakeModel(modellist=modellist, dim=c(200,200)))

tempCL=profitOpenCLEnv()
magimage(profitMakeModel(modellist=modellist, dim=c(200,200), openclenv=tempCL))

Speed comparisons

You can see the kind of speed up this offers us by comparing these timings.

First a single big image:

system.time(profitMakeModel(modellist=modellist, dim=c(2000,2000), openclenv=tempCL))
system.time(profitMakeModel(modellist=modellist, dim=c(2000,2000), openclenv={}))

Next 100 smaller images:

system.time(for(i in 1:100){profitMakeModel(modellist=modellist, dim=c(200,200), openclenv=tempCL)})
system.time(for(i in 1:100){profitMakeModel(modellist=modellist, dim=c(200,200), openclenv={})})

On my (ASGR's) MacBook Pro circa 2012 with a quad 2.6 GHz Intel Core i7 CPU and a NVIDIA GeForce GT 650M 1024 MB GPU I see a speed up of a factor ~3.5 for the first example (a single big image) and ~4 for the second example (looped smaller images).

OpenMP support

ProFit also supports OpenMP threading at the pixel level. To get this working is fairly straight-forward ona Linux install, a bit harder on a Mac, and probably impossible on a Windows machine (good luck there...). In all cases you will need to re-build ProFit from source rather than rely on the CRAN binary.

When you build ProFit it will try to detect whether your default compiler supports OpenMP. This is stored in ~/.R/Makevars and is a text file used by your local R build to select compilers to build and link against.

Linux

For Linux using a fairly new (2016+) version of GCC or Clang this should work quite easily. If you want to use, e.g., GCC then you should make (or edit) your R Makevars file. For a modern Linux install it should probably look like this:

CC = gcc
CXX = g++
CXX1X = g++
CXX11 = g++

CFLAGS = -O3 -Wall -mtune=native -march=native -Ofast -std=gnu99
CXXFLAGS = -O3 -Wall -mtune=native -march=native -Ofast -std=c++0x
CXX1XFLAGS = -O3 -Wall -mtune=native -march=native  -Ofast -std=c++0x
CXX11FLAGS = -O3 -Wall -mtune=native -march=native  -Ofast -std=c++0x

For Clang it will probably look like this:

CC = clang
CXX = clang++
CXX1X = clang++
CXX11 = clang++

CFLAGS = -O3 -Wall -mtune=native -march=native -Ofast
CXXFLAGS = -O3 -Wall -mtune=native -march=native -Ofast
CXX1XFLAGS = -O3 -Wall -mtune=native -march=native  -Ofast
CXX11FLAGS = -O3 -Wall -mtune=native -march=native  -Ofast

Mac OS X

For Macs (El Capitan or older certainly) the version of Clang incuded is quite old and does not support OpenMP. To get round this you have to install a newer version of LLVM from e.g. homebrew (using something like `brew install llvm'). On my machine homebrew installs to /usr/local/Cellar/llvm/4.0.0/, but this will vary by setup (newer versions than 4.0.0 should work fine).

With this installed you might need to also get Xcode (from the App store) and then explicitly install command line tools for Xcode, which you can do by running this in the terminal:

xcode-select --install

With that all done you will need to edit your ~/.R/Makvars to point to the correct files with the correct flags. On my machine (new MacBook Pro running OS X 10.12 Siera) the Makevars file looks like this:

CC = /usr/local/Cellar/llvm/4.0.0/bin/clang
CXX = /usr/local/Cellar/llvm/4.0.0/bin/clang++
CXX1X = /usr/local/Cellar/llvm/4.0.0/bin/clang++
CXX11 = /usr/local/Cellar/llvm/4.0.0/bin/clang++

CFLAGS = -O3 -Wall -mtune=native -march=native -Ofast
CXXFLAGS = -O3 -Wall -mtune=native -march=native -Ofast
CXX1XFLAGS = -O3 -Wall -mtune=native -march=native  -Ofast
CXX11FLAGS = -O3 -Wall -mtune=native -march=native  -Ofast
LDFLAGS = -L/usr/local/Cellar/llvm/4.0.0/lib -Wl,-rpath,/usr/local/Cellar/llvm/4.0.0/lib

This took a bit of fiddling to figure out (especially the last line), and is basically a side-effect of the compiler linking getting more complicated as soon as you start using a non-standard Clang compiler.

Hopefully in near future versions of OS X the system Clang will support OpenMP, and these fiddly setup issues will go away.

Microsoft Windows

I do not know how you should go about building OpenMP supported packages on Windows. Get in touch if you have success at this, otherwise I assume it will not work easily (perhaps I am being overly pessimistic here, so please feel encouraged to have a crack).

After setting up

Once you have setup your Makevars files you can now build ProFit from source with OpenMP support. If you download a recent tarball, then this is done from the command line with the following command:

R CMD INSTALL --preclean ProFit_X.X.X.tar.gz 

Where X.X.X is the relevant version numbering for ProFit (note this support only exists from v1.0.3+).

You should see some helpful outputs when this starts which indicates whether you are building with OpenCL and OpenMP support, e.g.:

==> R CMD INSTALL ProFit

* installing to library ‘/Users/aaron/Library/R/3.3/library’
* installing *source* package ‘ProFit’ ...
- Found OpenCL headers
- Found OpenCL libs
- Looking for OpenCL version 2.0
- Looking for OpenCL version 1.2
- Compiling with OpenCL 1.2 support
- Compiling with OpenMP support

Speed comparisons:

You can see the kind of speed up this offers us by comparing these timings.

First a single big image:

system.time(profitMakeModel(modellist=modellist, dim=c(2000,2000)))
system.time(profitMakeModel(modellist=modellist, dim=c(2000,2000), omp_threads=4))

Next 100 smaller images:

system.time(for(i in 1:100){profitMakeModel(modellist=modellist, dim=c(200,200))})
system.time(for(i in 1:100){profitMakeModel(modellist=modellist, dim=c(200,200), omp_threads=4)})

On my (ASGR's) MacBook Pro circa 2012 with a quad 2.6 GHz Intel Core i7 CPU I see a speed up of a factor ~2.5 for the first example (a single big image) and ~4 for the second example (looped smaller images).

Foreach support

The other way you might make use of multiple cores is using foreach, so we can compare the runtime to this easily, also using 4 cores.

library(doParallel)
library(foreach)
registerDoParallel(cores=4)

Again, for 100 smaller images:

system.time(foreach(i=1:100)%do%{profitMakeModel(modellist=modellist, dim=c(200,200))})
system.time(foreach(i=1:100)%dopar%{profitMakeModel(modellist=modellist, dim=c(200,200))})

On my (ASGR's) MacBook Pro circa 2012 with a quad 2.6 GHz Intel Core i7 CPU I again see a speed up of a factor ~4.

Depending on your use case any of the three strategies might be most sensible. For fitting a single object you will get the most speed-up from using OpenCL or OpenMP. For fitting a large number of galaxies running an embarrassingly parallel foreach loop should offer a similar speed-up to OpenMP using the same number of cores, but it will use much more memory (foreach effectively copies the session for each core, which produces additional overheads).



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ProFit documentation built on July 23, 2019, 9:04 a.m.