@c -*-texinfo-*-

@c This file is part of the StarPU Handbook.
@c Copyright (C) 2009--2011  Universit@'e de Bordeaux 1
@c Copyright (C) 2010, 2011, 2012, 2013  Centre National de la Recherche Scientifique
@c Copyright (C) 2011, 2012 Institut National de Recherche en Informatique et Automatique
@c See the file starpu.texi for copying conditions.

@menu
* Installing a Binary Package::
* Installing from Source::
* Setting up Your Own Code::
* Benchmarking StarPU::
@end menu

@node Installing a Binary Package
@section Installing a Binary Package

One of the StarPU developers being a Debian Developer, the packages
are well integrated and very uptodate. To see which packages are
available, simply type:

@example
$ apt-cache search starpu
@end example

To install what you need, type:

@example
$ sudo apt-get install libstarpu-1.0 libstarpu-dev
@end example

@node Installing from Source
@section Installing from Source

StarPU can be built and installed by the standard means of the GNU
autotools. The following chapter is intended to briefly remind how these tools
can be used to install StarPU.

@menu
* Optional Dependencies::
* Getting Sources::
* Configuring StarPU::
* Building StarPU::
* Installing StarPU::
@end menu

@node Optional Dependencies
@subsection Optional Dependencies

The @url{http://www.open-mpi.org/software/hwloc, @code{hwloc} topology
discovery library} is not mandatory to use StarPU but strongly
recommended.  It allows for topology aware scheduling, which improves
performance.  @code{hwloc} is available in major free operating system
distributions, and for most operating systems.

If @code{hwloc} is not available on your system, the option
@code{--without-hwloc} should be explicitely given when calling the
@code{configure} script. If @code{hwloc} is installed with a @code{pkg-config} file,
no option is required, it will be detected automatically, otherwise
@code{with-hwloc=prefix} should be used to specify the location
of @code{hwloc}.

@node Getting Sources
@subsection Getting Sources

StarPU's sources can be obtained from the
@url{http://runtime.bordeaux.inria.fr/StarPU/files/,download page} of
the StarPU website.

All releases and the development tree of StarPU are freely available
on INRIA's gforge under the LGPL license. Some releases are available
under the BSD license.

The latest release can be downloaded from the
@url{http://gforge.inria.fr/frs/?group_id=1570,INRIA's gforge} or
directly from the @url{http://runtime.bordeaux.inria.fr/StarPU/files/,StarPU download page}.

The latest nightly snapshot can be downloaded from the @url{http://starpu.gforge.inria.fr/testing/,StarPU gforge website}.

@example
$ wget http://starpu.gforge.inria.fr/testing/starpu-nightly-latest.tar.gz
@end example

And finally, current development version is also accessible via svn.
It should be used only if you need the very latest changes (i.e. less
than a day!)@footnote{The client side of the software Subversion can
be obtained from @url{http://subversion.tigris.org}. If you
are running on Windows, you will probably prefer to use
@url{http://tortoisesvn.tigris.org/, TortoiseSVN}.}.

@example
svn checkout svn://scm.gforge.inria.fr/svn/starpu/trunk StarPU
@end example

@node Configuring StarPU
@subsection Configuring StarPU

Running @code{autogen.sh} is not necessary when using the tarball
releases of StarPU.  If you are using the source code from the svn
repository, you first need to generate the configure scripts and the
Makefiles. This requires the availability of @code{autoconf},
@code{automake} >= 2.60, and @code{makeinfo}.

@example
$ ./autogen.sh
@end example

You then need to configure StarPU. Details about options that are
useful to give to @code{./configure} are given in @ref{Compilation
configuration}.

@example
$ ./configure
@end example

If @code{configure} does not detect some software or produces errors, please
make sure to post the content of @code{config.log} when reporting the issue.

By default, the files produced during the compilation are placed in
the source directory. As the compilation generates a lot of files, it
is advised to to put them all in a separate directory. It is then
easier to cleanup, and this allows to compile several configurations
out of the same source tree. For that, simply enter the directory
where you want the compilation to produce its files, and invoke the
@code{configure} script located in the StarPU source directory.

@example
$ mkdir build
$ cd build
$ ../configure
@end example

@node Building StarPU
@subsection Building StarPU

@example
$ make
@end example

Once everything is built, you may want to test the result. An
extensive set of regression tests is provided with StarPU. Running the
tests is done by calling @code{make check}. These tests are run every night
and the result from the main profile is publicly
@url{http://starpu.gforge.inria.fr/testing/,available}.

@example
$ make check
@end example

@node Installing StarPU
@subsection Installing StarPU

In order to install StarPU at the location that was specified during
configuration:

@example
$ make install
@end example

Libtool interface versioning information are included in
libraries names (libstarpu-1.0.so, libstarpumpi-1.0.so and
libstarpufft-1.0.so).

@node Setting up Your Own Code
@section Setting up Your Own Code

@menu
* Setting Flags for Compiling::
* Running a Basic StarPU Application::
* Kernel Threads Started by StarPU::
* Enabling OpenCL::
@end menu

@node Setting Flags for Compiling
@subsection Setting Flags for Compiling, Linking and Running Applications

StarPU provides a pkg-config executable to obtain relevant compiler
and linker flags.
Compiling and linking an application against StarPU may require to use
specific flags or libraries (for instance @code{CUDA} or @code{libspe2}).
To this end, it is possible to use the @code{pkg-config} tool.

If StarPU was not installed at some standard location, the path of StarPU's
library must be specified in the @code{PKG_CONFIG_PATH} environment variable so
that @code{pkg-config} can find it. For example if StarPU was installed in
@code{$prefix_dir}:

@example
$ PKG_CONFIG_PATH=$PKG_CONFIG_PATH:$prefix_dir/lib/pkgconfig
@end example

The flags required to compile or link against StarPU are then
accessible with the following commands@footnote{It is still possible to use the API
provided in the version 0.9 of StarPU by calling @code{pkg-config}
with the @code{libstarpu} package. Similar packages are provided for
@code{libstarpumpi} and @code{libstarpufft}.}:

@example
$ pkg-config --cflags starpu-1.1  # options for the compiler
$ pkg-config --libs starpu-1.1    # options for the linker
@end example

Make sure that @code{pkg-config --libs starpu-1.1} actually produces some output
before going further: @code{PKG_CONFIG_PATH} has to point to the place where
@code{starpu-1.1.pc} was installed during @code{make install}.

Also pass the @code{--static} option if the application is to be
linked statically.

It is also necessary to set the variable @code{LD_LIBRARY_PATH} to
locate dynamic libraries at runtime.

@example
$ LD_LIBRARY_PATH=$prefix_dir/lib:$LD_LIBRARY_PATH
@end example

When using a Makefile, the following lines can be added to set the
options for the compiler and the linker:

@cartouche
@example
CFLAGS          +=      $$(pkg-config --cflags starpu-1.1)
LDFLAGS         +=      $$(pkg-config --libs starpu-1.1)
@end example
@end cartouche

@node Running a Basic StarPU Application
@subsection Running a Basic StarPU Application

Basic examples using StarPU are built in the directory
@code{examples/basic_examples/} (and installed in
@code{$prefix_dir/lib/starpu/examples/}). You can for example run the example
@code{vector_scal}.

@example
$ ./examples/basic_examples/vector_scal
BEFORE: First element was 1.000000
AFTER: First element is 3.140000
@end example

When StarPU is used for the first time, the directory
@code{$STARPU_HOME/.starpu/} is created, performance models will be stored in
that directory (@pxref{STARPU_HOME}).

Please note that buses are benchmarked when StarPU is launched for the
first time. This may take a few minutes, or less if @code{hwloc} is
installed. This step is done only once per user and per machine.

@node Kernel Threads Started by StarPU
@subsection Kernel Threads Started by StarPU

StarPU automatically binds one thread per CPU core. It does not use
SMT/hyperthreading because kernels are usually already optimized for using a
full core, and using hyperthreading would make kernel calibration rather random.

Since driving GPUs is a CPU-consuming task, StarPU dedicates one core per GPU

While StarPU tasks are executing, the application is not supposed to do
computations in the threads it starts itself, tasks should be used instead.

TODO: add a StarPU function to bind an application thread (e.g. the main thread)
to a dedicated core (and thus disable the corresponding StarPU CPU worker).

@node Enabling OpenCL
@subsection Enabling OpenCL

When both CUDA and OpenCL drivers are enabled, StarPU will launch an
OpenCL worker for NVIDIA GPUs only if CUDA is not already running on them.
This design choice was necessary as OpenCL and CUDA can not run at the
same time on the same NVIDIA GPU, as there is currently no interoperability
between them.

To enable OpenCL, you need either to disable CUDA when configuring StarPU:

@example
$ ./configure --disable-cuda
@end example

or when running applications:

@example
$ STARPU_NCUDA=0 ./application
@end example

OpenCL will automatically be started on any device not yet used by
CUDA. So on a machine running 4 GPUS, it is therefore possible to
enable CUDA on 2 devices, and OpenCL on the 2 other devices by doing
so:

@example
$ STARPU_NCUDA=2 ./application
@end example

@node Benchmarking StarPU
@section Benchmarking StarPU

Some interesting benchmarks are installed among examples in
@code{$prefix_dir/lib/starpu/examples/}. Make sure to try various
schedulers, for instance STARPU_SCHED=dmda

@menu
* Task size overhead::
* Data transfer latency::
* Gemm::
* Cholesky::
* LU::
@end menu

@node Task size overhead
@subsection Task size overhead

This benchmark gives a glimpse into how big a size should be for StarPU overhead
to be low enough.  Run @code{tasks_size_overhead.sh}, it will generate a plot
of the speedup of tasks of various sizes, depending on the number of CPUs being
used.

@node Data transfer latency
@subsection Data transfer latency

@code{local_pingpong} performs a ping-pong between the first two CUDA nodes, and
prints the measured latency.

@node Gemm
@subsection Matrix-matrix multiplication

@code{sgemm} and @code{dgemm} perform a blocked matrix-matrix
multiplication using BLAS and cuBLAS. They output the obtained GFlops.

@node Cholesky
@subsection Cholesky factorization

@code{cholesky*} perform a Cholesky factorization (single precision). They use different dependency primitives.

@node LU
@subsection LU factorization

@code{lu*} perform an LU factorization. They use different dependency primitives.
