starpu-max/doc/starpu.texi

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@setfilename starpu.info
@settitle StarPU
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@titlepage
@title StarPU
@page
@vskip 0pt plus 1filll
@comment For the @value{version-GCC} Version*
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@summarycontents
@contents
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@node Top
@top Preface
@cindex Preface

This manual documents the usage of StarPU.


@comment
@comment  When you add a new menu item, please keep the right hand
@comment  aligned to the same column.  Do not use tabs.  This provides
@comment  better formatting.
@comment
@menu
* Introduction::                A basic introduction to using StarPU
* Installing StarPU::           How to configure, build and install StarPU
* Using StarPU::                How to run StarPU application
* Configuring StarPU::          How to configure StarPU
* StarPU API::                  The API to use StarPU
* Advanced Topics::             Advanced use of StarPU
* Basic Examples::              Basic examples of the use of StarPU
* Full source code for the 'Scaling a Vector' example::  
@end menu

@c ---------------------------------------------------------------------
@c Introduction to StarPU
@c ---------------------------------------------------------------------

@node Introduction
@chapter Introduction to StarPU

@menu
* Motivation::                  Why StarPU ?
* StarPU in a Nutshell::        The Fundamentals of StarPU
@end menu

@node Motivation
@section Motivation

@c complex machines with heterogeneous cores/devices
The use of specialized hardware such as accelerators or coprocessors offers an
interesting approach to overcome the physical limits encountered by processor
architects. As a result, many machines are now equipped with one or several
accelerators (e.g. a GPU), in addition to the usual processor(s). While a lot of
efforts have been devoted to offload computation onto such accelerators, very
little attention as been paid to portability concerns on the one hand, and to the
possibility of having heterogeneous accelerators and processors to interact on the other hand.

StarPU is a runtime system that offers support for heterogeneous multicore
architectures, it not only offers a unified view of the computational resources
(i.e. CPUs and accelerators at the same time), but it also takes care of
efficiently mapping and executing tasks onto an heterogeneous machine while
transparently handling low-level issues in a portable fashion.

@c this leads to a complicated distributed memory design
@c which is not (easily) manageable by hand

@c added value/benefits of StarPU
@c   - portability
@c   - scheduling, perf. portability

@node StarPU in a Nutshell
@section StarPU in a Nutshell

@menu
* Codelet and Tasks::           
* StarPU Data Management Library::  
@end menu

From a programming point of view, StarPU is not a new language but a library
that executes tasks explicitly submitted by the application.  The data that a
task manipulates are automatically transferred onto the accelerator so that the
programmer does not have to take care of complex data movements.  StarPU also
takes particular care of scheduling those tasks efficiently and allows
scheduling experts to implement custom scheduling policies in a portable
fashion.

@c explain the notion of codelet and task (i.e. g(A, B)
@node Codelet and Tasks
@subsection Codelet and Tasks

One of StarPU primary data structure is the @b{codelet}. A codelet describes a
computational kernel that can possibly be implemented on multiple architectures
such as a CPU, a CUDA device or a Cell's SPU.

@c TODO insert illustration f : f_spu, f_cpu, ...

Another important data structure is the @b{task}. Executing a StarPU task
consists in applying a codelet on a data set, on one of the architectures on
which the codelet is implemented. In addition to the codelet that a task
implements, it also describes which data are accessed, and how they are
accessed during the computation (read and/or write).
StarPU tasks are asynchronous: submitting a task to StarPU is a non-blocking
operation. The task structure can also specify a @b{callback} function that is
called once StarPU has properly executed the task. It also contains optional
fields that the application may use to give hints to the scheduler (such as
priority levels).

A task may be identified by a unique 64-bit number which we refer as a @b{tag}.
Task dependencies can be enforced either by the means of callback functions, or
by expressing dependencies between tags.

@c TODO insert illustration f(Ar, Brw, Cr) + ..

@c DSM
@node StarPU Data Management Library
@subsection StarPU Data Management Library

Because StarPU schedules tasks at runtime, data transfers have to be
done automatically and ``just-in-time'' between processing units,
relieving the application programmer from explicit data transfers.
Moreover, to avoid unnecessary transfers, StarPU keeps data
where it was last needed, even if was modified there, and it
allows multiple copies of the same data to reside at the same time on
several processing units as long as it is not modified.

@c ---------------------------------------------------------------------
@c Installing StarPU
@c ---------------------------------------------------------------------

@node Installing StarPU
@chapter Installing StarPU

@menu
* Configuration of StarPU::     
* Building and Installing StarPU::  
@end menu

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.

@node Configuration of StarPU
@section Configuration of StarPU

@menu
* Generating Makefiles and configuration scripts::  
* Running the configuration::   
@end menu

@node Generating Makefiles and configuration scripts
@subsection Generating Makefiles and configuration scripts

This step 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.

@example
% autoreconf -vfi
@end example

@node Running the configuration
@subsection Running the configuration

@example
% ./configure
@end example

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

@node Building and Installing StarPU
@section Building and Installing StarPU

@menu
* Building::                    
* Sanity Checks::               
* Installing::                  
@end menu

@node Building
@subsection Building

@example
% make
@end example

@node Sanity Checks
@subsection Sanity Checks

In order to make sure that StarPU is working properly on the system, it is also
possible to run a test suite.

@example
% make check
@end example

@node Installing
@subsection Installing

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

@example
% make install
@end example

@c ---------------------------------------------------------------------
@c Using StarPU
@c ---------------------------------------------------------------------

@node Using StarPU
@chapter Using StarPU

@menu
* Setting flags for compiling and linking applications::  
* Running a basic StarPU application::  
@end menu

@node Setting flags for compiling and linking applications
@section Setting flags for compiling and linking applications

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:

@example
% pkg-config --cflags libstarpu  # options for the compiler
% pkg-config --libs libstarpu    # options for the linker
@end example

@node Running a basic StarPU application
@section Running a basic StarPU application

Basic examples using StarPU have been built in the directory
@code{$prefix_dir/lib/starpu/examples/}. You can for example run the
example @code{vector_scal}.

@example
% $prefix_dir/lib/starpu/examples/vector_scal
BEFORE : First element was 1.000000
AFTER First element is 3.140000
%
@end example


@c ---------------------------------------------------------------------
@c Configuration options
@c ---------------------------------------------------------------------

@node Configuring StarPU
@chapter Configuring StarPU


@menu
* Compilation configuration::   
* Execution configuration through environment variables::  
@end menu

@node Compilation configuration
@section Compilation configuration

The following arguments can be given to the @code{configure} script.

@menu
* Common configuration::        
* Configuring workers::         
* Advanced configuration::      
@end menu

@node Common configuration
@subsection Common configuration


@menu
* --enable-debug::              
* --enable-fast::               
* --enable-verbose::            
* --enable-coverage::           
@end menu

@node --enable-debug
@subsubsection @code{--enable-debug}
@table @asis
@item @emph{Description}:
Enable debugging messages.
@end table

@node --enable-fast
@subsubsection @code{--enable-fast}
@table @asis
@item @emph{Description}:
Do not enforce assertions, saves a lot of time spent to compute them otherwise.
@end table

@node --enable-verbose
@subsubsection @code{--enable-verbose}
@table @asis
@item @emph{Description}:
Augment the verbosity of the debugging messages.
@end table

@node --enable-coverage
@subsubsection @code{--enable-coverage}
@table @asis
@item @emph{Description}:
Enable flags for the coverage tool.
@end table

@node Configuring workers
@subsection Configuring workers

@menu
* --disable-cpu::               
* --enable-maxcudadev::         
* --disable-cuda::              
* --with-cuda-dir::             
* --enable-maxopencldev::       
* --disable-opencl::            
* --with-opencl-dir::           
* --enable-gordon::             
* --with-gordon-dir::           
@end menu

@node --disable-cpu
@subsubsection @code{--disable-cpu}
@table @asis
@item @emph{Description}:
Disable the use of CPUs of the machine. Only GPUs etc. will be used.
@end table

@node --enable-maxcudadev
@subsubsection @code{--enable-maxcudadev=<number>}
@table @asis
@item @emph{Description}:
Defines the maximum number of CUDA devices that StarPU will support, then
available as the @code{STARPU_MAXCUDADEVS} macro.
@end table

@node --disable-cuda
@subsubsection @code{--disable-cuda}
@table @asis
@item @emph{Description}:
Disable the use of CUDA, even if a valid CUDA installation was detected.
@end table

@node --with-cuda-dir
@subsubsection @code{--with-cuda-dir=<path>}
@table @asis
@item @emph{Description}:
Specify the directory where CUDA is installed. This directory should notably contain
@code{include/cuda.h}.
@end table

@node --enable-maxopencldev
@subsubsection @code{--enable-maxopencldev=<number>}
@table @asis
@item @emph{Description}:
Defines the maximum number of OpenCL devices that StarPU will support, then
available as the @code{STARPU_MAXOPENCLDEVS} macro.
@end table

@node --disable-opencl
@subsubsection @code{--disable-opencl}
@table @asis
@item @emph{Description}:
Disable the use of OpenCL, even if the SDK is detected.
@end table

@node --with-opencl-dir
@subsubsection @code{--with-opencl-dir=<path>}
@table @asis
@item @emph{Description}:
Specify the location of the OpenCL SDK. This directory should notably contain
@code{include/CL/cl.h}.
@end table

@node --enable-gordon
@subsubsection @code{--enable-gordon}
@table @asis
@item @emph{Description}:
Enable the use of the Gordon runtime for Cell SPUs.
@c TODO: rather default to enabled when detected
@end table

@node --with-gordon-dir
@subsubsection @code{--with-gordon-dir=<path>}
@table @asis
@item @emph{Description}:
Specify the location of the Gordon SDK.
@end table

@node Advanced configuration
@subsection Advanced configuration

@menu
* --enable-perf-debug::         
* --enable-model-debug::        
* --enable-stats::              
* --enable-maxbuffers::         
* --enable-allocation-cache::   
* --enable-opengl-render::      
* --enable-blas-lib::           
* --with-magma::                
* --with-fxt::                  
* --with-perf-model-dir::       
* --with-mpicc::                
* --with-mpi::                  
* --with-goto-dir::             
* --with-atlas-dir::            
@end menu

@node --enable-perf-debug
@subsubsection @code{--enable-perf-debug}
@table @asis
@item @emph{Description}:
Enable performance debugging.
@end table

@node --enable-model-debug
@subsubsection @code{--enable-model-debug}
@table @asis
@item @emph{Description}:
Enable performance model debugging.
@end table

@node --enable-stats
@subsubsection @code{--enable-stats}
@table @asis
@item @emph{Description}:
Enable statistics.
@end table

@node --enable-maxbuffers
@subsubsection @code{--enable-maxbuffers=<nbuffers>}
@table @asis
@item @emph{Description}:
Define the maximum number of buffers that tasks will be able to take
as parameters, then available as the @code{STARPU_NMAXBUFS} macro.
@end table

@node --enable-allocation-cache
@subsubsection @code{--enable-allocation-cache}
@table @asis
@item @emph{Description}:
Enable the use of a data allocation cache to avoid the cost of it with
CUDA. Still experimental.
@end table

@node --enable-opengl-render
@subsubsection @code{--enable-opengl-render}
@table @asis
@item @emph{Description}:
Enable the use of OpenGL for the rendering of some examples.
@c TODO: rather default to enabled when detected
@end table

@node --enable-blas-lib
@subsubsection @code{--enable-blas-lib=<name>}
@table @asis
@item @emph{Description}:
Specify the blas library to be used by some of the examples. The
library has to be 'atlas' or 'goto'.
@end table

@node --with-magma
@subsubsection @code{--with-magma=<path>}
@table @asis
@item @emph{Description}:
Specify where magma is installed.
@end table

@node --with-fxt
@subsubsection @code{--with-fxt=<path>}
@table @asis
@item @emph{Description}:
Specify the location of FxT (for generating traces and rendering them
using ViTE). This directory should notably contain
@code{include/fxt/fxt.h}.
@end table

@node --with-perf-model-dir
@subsubsection @code{--with-perf-model-dir=<dir>}
@table @asis
@item @emph{Description}:
Specify where performance models should be stored (instead of defaulting to the
current user's home).
@end table

@node --with-mpicc
@subsubsection @code{--with-mpicc=<path to mpicc>}
@table @asis
@item @emph{Description}:
Specify the location of the @code{mpicc} compiler to be used for starpumpi.
@c TODO: also just use AC_PROG
@end table

@node --with-mpi
@subsubsection @code{--with-mpi}
@table @asis
@item @emph{Description}:
Enable building libstarpumpi.
@c TODO: rather just use the availability of mpicc instead of a second option
@end table

@node --with-goto-dir
@subsubsection @code{--with-goto-dir=<dir>}
@table @asis
@item @emph{Description}:
Specify the location of GotoBLAS.
@end table

@node --with-atlas-dir
@subsubsection @code{--with-atlas-dir=<dir>}
@table @asis
@item @emph{Description}:
Specify the location of ATLAS. This directory should notably contain
@code{include/cblas.h}.
@end table

@c ---------------------------------------------------------------------
@c Environment variables
@c ---------------------------------------------------------------------

@node Execution configuration through environment variables
@section Execution configuration through environment variables

@menu
* Workers::                     Configuring workers
* Scheduling::                  Configuring the Scheduling engine
* Misc::                        Miscellaneous and debug
@end menu

Note: the values given in @code{starpu_conf} structure passed when
calling @code{starpu_init} will override the values of the environment
variables.

@node Workers
@subsection Configuring workers

@menu
* STARPU_NCPUS::                Number of CPU workers
* STARPU_NCUDA::                Number of CUDA workers
* STARPU_NOPENCL::              Number of OpenCL workers
* STARPU_NGORDON::              Number of SPU workers (Cell)
* STARPU_WORKERS_CPUID::        Bind workers to specific CPUs
* STARPU_WORKERS_CUDAID::       Select specific CUDA devices
* STARPU_WORKERS_OPENCLID::     Select specific OpenCL devices
@end menu

@node STARPU_NCPUS
@subsubsection @code{STARPU_NCPUS} -- Number of CPU workers
@table @asis

@item @emph{Description}:
Specify the maximum number of CPU workers. Note that StarPU will not allocate
more CPUs than there are physical CPUs, and that some CPUs are used to control
the accelerators.

@end table

@node STARPU_NCUDA
@subsubsection @code{STARPU_NCUDA} -- Number of CUDA workers
@table @asis

@item @emph{Description}:
Specify the maximum number of CUDA devices that StarPU can use. If
@code{STARPU_NCUDA} is lower than the number of physical devices, it is
possible to select which CUDA devices should be used by the means of the
@code{STARPU_WORKERS_CUDAID} environment variable.

@end table

@node STARPU_NOPENCL
@subsubsection @code{STARPU_NOPENCL} -- Number of OpenCL workers
@table @asis

@item @emph{Description}:
OpenCL equivalent of the @code{STARPU_NCUDA} environment variable.
@end table

@node STARPU_NGORDON
@subsubsection @code{STARPU_NGORDON} -- Number of SPU workers (Cell)
@table @asis

@item @emph{Description}:
Specify the maximum number of SPUs that StarPU can use.
@end table


@node STARPU_WORKERS_CPUID
@subsubsection @code{STARPU_WORKERS_CPUID} -- Bind workers to specific CPUs
@table @asis

@item @emph{Description}:
Passing an array of integers (starting from 0) in @code{STARPU_WORKERS_CPUID}
specifies on which logical CPU the different workers should be
bound. For instance, if @code{STARPU_WORKERS_CPUID = "1 3 0 2"}, the first
worker will be bound to logical CPU #1, the second CPU worker will be bound to
logical CPU #3 and so on.  Note that the logical ordering of the CPUs is either
determined by the OS, or provided by the @code{hwloc} library in case it is
available.

Note that the first workers correspond to the CUDA workers, then come the
OpenCL and the SPU, and finally the CPU workers. For example if
we have @code{STARPU_NCUDA=1}, @code{STARPU_NOPENCL=1}, @code{STARPU_NCPUS=2}
and @code{STARPU_WORKERS_CPUID = "0 2 1 3"}, the CUDA device will be controlled
by logical CPU #0, the OpenCL device will be controlled by logical CPU #2, and
the logical CPUs #1 and #3 will be used by the CPU workers.

If the number of workers is larger than the array given in
@code{STARPU_WORKERS_CPUID}, the workers are bound to the logical CPUs in a
round-robin fashion: if @code{STARPU_WORKERS_CPUID = "0 1"}, the first and the
third (resp. second and fourth) workers will be put on CPU #0 (resp. CPU #1).

@end table

@node STARPU_WORKERS_CUDAID
@subsubsection @code{STARPU_WORKERS_CUDAID} -- Select specific CUDA devices
@table @asis

@item @emph{Description}:
Similarly to the @code{STARPU_WORKERS_CPUID} environment variable, it is
possible to select which CUDA devices should be used by StarPU. On a machine
equipped with 4 GPUs, setting @code{STARPU_WORKERS_CUDAID = "1 3"} and
@code{STARPU_NCUDA=2} specifies that 2 CUDA workers should be created, and that
they should use CUDA devices #1 and #3 (the logical ordering of the devices is
the one reported by CUDA).
@end table

@node STARPU_WORKERS_OPENCLID
@subsubsection @code{STARPU_WORKERS_OPENCLID} -- Select specific OpenCL devices
@table @asis

@item @emph{Description}:
OpenCL equivalent of the @code{STARPU_WORKERS_CUDAID} environment variable.
@end table

@node Scheduling
@subsection Configuring the Scheduling engine

@menu
* STARPU_SCHED::                Scheduling policy
* STARPU_CALIBRATE::            Calibrate performance models
* STARPU_PREFETCH::             Use data prefetch
* STARPU_SCHED_ALPHA::          Computation factor
* STARPU_SCHED_BETA::           Communication factor
@end menu

@node STARPU_SCHED
@subsubsection @code{STARPU_SCHED} -- Scheduling policy
@table @asis

@item @emph{Description}:

This chooses between the different scheduling policies proposed by StarPU: work
random, stealing, greedy, with performance models, etc.

Use @code{STARPU_SCHED=help} to get the list of available schedulers.

@end table

@node STARPU_CALIBRATE
@subsubsection @code{STARPU_CALIBRATE} -- Calibrate performance models
@table @asis

@item @emph{Description}:
If this variable is set to 1, the performance models are calibrated during
the execution. If it is set to 2, the previous values are dropped to restart
calibration from scratch.

Note: this currently only applies to dm and dmda scheduling policies.

@end table

@node STARPU_PREFETCH
@subsubsection @code{STARPU_PREFETCH} -- Use data prefetch
@table @asis

@item @emph{Description}:
If this variable is set, data prefetching will be enabled, that is when a task is
scheduled to be executed e.g. on a GPU, StarPU will request an asynchronous
transfer in advance, so that data is already present on the GPU when the task
starts. As a result, computation and data transfers are overlapped.

@end table

@node STARPU_SCHED_ALPHA
@subsubsection @code{STARPU_SCHED_ALPHA} -- Computation factor
@table @asis

@item @emph{Description}:
To estimate the cost of a task StarPU takes into account the estimated
computation time (obtained thanks to performance models). The alpha factor is
the coefficient to be applied to it before adding it to the communication part.

@end table

@node STARPU_SCHED_BETA
@subsubsection @code{STARPU_SCHED_BETA} -- Communication factor
@table @asis

@item @emph{Description}:
To estimate the cost of a task StarPU takes into account the estimated
data transfer time (obtained thanks to performance models). The beta factor is
the coefficient to be applied to it before adding it to the computation part.

@end table

@node Misc
@subsection Miscellaneous and debug

@menu
* STARPU_LOGFILENAME::          Select debug file name
@end menu

@node STARPU_LOGFILENAME
@subsubsection @code{STARPU_LOGFILENAME} -- Select debug file name
@table @asis

@item @emph{Description}:
This variable specify in which file the debugging output should be saved to.

@end table

@c ---------------------------------------------------------------------
@c StarPU API
@c ---------------------------------------------------------------------

@node StarPU API
@chapter StarPU API

@menu
* Initialization and Termination::  Initialization and Termination methods
* Workers' Properties::         Methods to enumerate workers' properties
* Data Library::                Methods to manipulate data
* Codelets and Tasks::          Methods to construct tasks
* Explicit Dependencies::       Explicit Dependencies
* Implicit Data Dependencies::  Implicit Data Dependencies
* Profiling API::               Profiling API
* CUDA extensions::             CUDA extensions
* OpenCL extensions::           OpenCL extensions
* Cell extensions::             Cell extensions
* Miscellaneous helpers::       
@end menu

@node Initialization and Termination
@section Initialization and Termination

@menu
* starpu_init::                 Initialize StarPU
* struct starpu_conf::          StarPU runtime configuration
* starpu_shutdown::             Terminate StarPU
@end menu

@node starpu_init
@subsection @code{starpu_init} -- Initialize StarPU
@table @asis

@item @emph{Description}:
This is StarPU initialization method, which must be called prior to any other
StarPU call.  It is possible to specify StarPU's configuration (e.g. scheduling
policy, number of cores, ...) by passing a non-null argument. Default
configuration is used if the passed argument is @code{NULL}.
@item @emph{Return value}:
Upon successful completion, this function returns 0. Otherwise, @code{-ENODEV}
indicates that no worker was available (so that StarPU was not initialized).

@item @emph{Prototype}:
@code{int starpu_init(struct starpu_conf *conf);}

@end table

@node struct starpu_conf
@subsection @code{struct starpu_conf} -- StarPU runtime configuration

@table @asis
@item @emph{Description}:
This structure is passed to the @code{starpu_init} function in order
to configure StarPU.
When the default value is used, StarPU automatically selects the number
of processing units and takes the default scheduling policy. This parameter
overwrites the equivalent environment variables.

@item @emph{Fields}:
@table @asis
@item @code{sched_policy_name} (default = NULL):
This is the name of the scheduling policy. This can also be specified with the
@code{STARPU_SCHED} environment variable.
@item @code{sched_policy} (default = NULL):
This is the definition of the scheduling policy. This field is ignored
if @code{sched_policy_name} is set.

@item @code{ncpus} (default = -1):
This is the maximum number of CPU cores that StarPU can use. This can also be
specified with the @code{STARPU_NCPUS} environment variable.
@item @code{ncuda} (default = -1):
This is the maximum number of CUDA devices that StarPU can use. This can also be
specified with the @code{STARPU_NCUDA} environment variable.
@item @code{nopencl} (default = -1):
This is the maximum number of OpenCL devices that StarPU can use. This can also be
specified with the @code{STARPU_NOPENCL} environment variable.
@item @code{nspus} (default = -1):
This is the maximum number of Cell SPUs that StarPU can use. This can also be
specified with the @code{STARPU_NGORDON} environment variable.

@item @code{use_explicit_workers_bindid} (default = 0)
If this flag is set, the @code{workers_bindid} array indicates where the
different workers are bound, otherwise StarPU automatically selects where to
bind the different workers unless the @code{STARPU_WORKERS_CPUID} environment
variable is set. The @code{STARPU_WORKERS_CPUID} environment variable is
ignored if the @code{use_explicit_workers_bindid} flag is set.
@item @code{workers_bindid[STARPU_NMAXWORKERS]}
If the @code{use_explicit_workers_bindid} flag is set, this array indicates
where to bind the different workers. The i-th entry of the
@code{workers_bindid} indicates the logical identifier of the processor which
should execute the i-th worker. Note that the logical ordering of the CPUs is
either determined by the OS, or provided by the @code{hwloc} library in case it
is available.
 
@item @code{use_explicit_workers_cuda_gpuid} (default = 0)
@item @code{workers_cuda_gpuid[STARPU_NMAXWORKERS]}
@item @code{use_explicit_workers_opencl_gpuid} (default = 0)
@item @code{workers_opencl_gpuid[STARPU_NMAXWORKERS]}:
These fields are explained in @ref{STARPU_WORKERS_CPUID}.

@item @code{calibrate} (default = 0):
If this flag is set, StarPU will calibrate the performance models when
executing tasks. This can also be specified with the @code{STARPU_CALIBRATE}
environment variable.
@end table

@end table



@node starpu_shutdown
@subsection @code{starpu_shutdown} -- Terminate StarPU
@table @asis

@item @emph{Description}:
This is StarPU termination method. It must be called at the end of the
application: statistics and other post-mortem debugging information are not
guaranteed to be available until this method has been called.

@item @emph{Prototype}:
@code{void starpu_shutdown(void);}

@end table

@node Workers' Properties
@section Workers' Properties

@menu
* starpu_worker_get_count::     Get the number of processing units
* starpu_cpu_worker_get_count::  Get the number of CPU controlled by StarPU
* starpu_cuda_worker_get_count::  Get the number of CUDA devices controlled by StarPU
* starpu_opencl_worker_get_count::  Get the number of OpenCL devices controlled by StarPU
* starpu_spu_worker_get_count::  Get the number of Cell SPUs controlled by StarPU
* starpu_worker_get_id::        Get the identifier of the current worker
* starpu_worker_get_devid::        Get the device identifier of a worker
* starpu_worker_get_type::      Get the type of processing unit associated to a worker
* starpu_worker_get_name::      Get the name of a worker
* starpu_worker_get_memory_node:: Get the memory node of a worker 
@end menu

@node starpu_worker_get_count
@subsection @code{starpu_worker_get_count} -- Get the number of processing units
@table @asis

@item @emph{Description}:
This function returns the number of workers (i.e. processing units executing
StarPU tasks). The returned value should be at most @code{STARPU_NMAXWORKERS}.

@item @emph{Prototype}:
@code{unsigned starpu_worker_get_count(void);}
@end table

@node starpu_cpu_worker_get_count
@subsection @code{starpu_cpu_worker_get_count} -- Get the number of CPU controlled by StarPU
@table @asis

@item @emph{Description}:
This function returns the number of CPUs controlled by StarPU. The returned
value should be at most @code{STARPU_NMAXCPUS}.

@item @emph{Prototype}:
@code{unsigned starpu_cpu_worker_get_count(void);}
@end table

@node starpu_cuda_worker_get_count
@subsection @code{starpu_cuda_worker_get_count} -- Get the number of CUDA devices controlled by StarPU
@table @asis

@item @emph{Description}:
This function returns the number of CUDA devices controlled by StarPU. The returned
value should be at most @code{STARPU_MAXCUDADEVS}.

@item @emph{Prototype}:
@code{unsigned starpu_cuda_worker_get_count(void);}
@end table

@node starpu_opencl_worker_get_count
@subsection @code{starpu_opencl_worker_get_count} -- Get the number of OpenCL devices controlled by StarPU
@table @asis

@item @emph{Description}:
This function returns the number of OpenCL devices controlled by StarPU. The returned
value should be at most @code{STARPU_MAXOPENCLDEVS}.

@item @emph{Prototype}:
@code{unsigned starpu_opencl_worker_get_count(void);}
@end table

@node starpu_spu_worker_get_count
@subsection @code{starpu_spu_worker_get_count} -- Get the number of Cell SPUs controlled by StarPU
@table @asis

@item @emph{Description}:
This function returns the number of Cell SPUs controlled by StarPU.

@item @emph{Prototype}:
@code{unsigned starpu_opencl_worker_get_count(void);}
@end table


@node starpu_worker_get_id
@subsection @code{starpu_worker_get_id} -- Get the identifier of the current worker
@table @asis

@item @emph{Description}:
This function returns the identifier of the worker associated to the calling
thread. The returned value is either -1 if the current context is not a StarPU
worker (i.e. when called from the application outside a task or a callback), or
an integer between 0 and @code{starpu_worker_get_count() - 1}.

@item @emph{Prototype}:
@code{int starpu_worker_get_id(void);}
@end table

@node starpu_worker_get_devid
@subsection @code{starpu_worker_get_devid} -- Get the device identifier of a worker
@table @asis

@item @emph{Description}:
This functions returns the device id of the worker associated to an identifier
(as returned by the @code{starpu_worker_get_id} function). In the case of a
CUDA worker, this device identifier is the logical device identifier exposed by
CUDA (used by the @code{cudaGetDevice} function for instance). The device
identifier of a CPU worker is the logical identifier of the core on which the
worker was bound; this identifier is either provided by the OS or by the
@code{hwloc} library in case it is available.

@item @emph{Prototype}:
@code{int starpu_worker_get_devid(int id);}
@end table

@node starpu_worker_get_type
@subsection @code{starpu_worker_get_type} -- Get the type of processing unit associated to a worker
@table @asis

@item @emph{Description}:
This function returns the type of worker associated to an identifier (as
returned by the @code{starpu_worker_get_id} function). The returned value
indicates the architecture of the worker: @code{STARPU_CPU_WORKER} for a CPU
core, @code{STARPU_CUDA_WORKER} for a CUDA device,
@code{STARPU_OPENCL_WORKER} for a OpenCL device, and
@code{STARPU_GORDON_WORKER} for a Cell SPU. The value returned for an invalid
identifier is unspecified.

@item @emph{Prototype}:
@code{enum starpu_archtype starpu_worker_get_type(int id);}

@end table

@node starpu_worker_get_name
@subsection @code{starpu_worker_get_name} -- Get the name of a worker
@table @asis

@item @emph{Description}:
StarPU associates a unique human readable string to each processing unit. This
function copies at most the @code{maxlen} first bytes of the unique string
associated to a worker identified by its identifier @code{id} into the
@code{dst} buffer. The caller is responsible for ensuring that the @code{dst}
is a valid pointer to a buffer of @code{maxlen} bytes at least. Calling this
function on an invalid identifier results in an unspecified behaviour.

@item @emph{Prototype}:
@code{void starpu_worker_get_name(int id, char *dst, size_t maxlen);}

@end table

@node starpu_worker_get_memory_node
@subsection @code{starpu_worker_get_memory_node} -- Get the memory node of a worker
@table @asis
@item @emph{Description}:
This function returns the identifier of the memory node associated to the
worker identified by @code{workerid}.

@item @emph{Prototype}:
@code{unsigned starpu_worker_get_memory_node(unsigned workerid);}

@end table


@node Data Library
@section Data Library

This section describes the data management facilities provided by StarPU.

TODO: We show how to use existing data interfaces in [ref], but developers can
design their own data interfaces if required.

@menu
* starpu_access_mode::          starpu_access_mode
* unsigned memory_node::        Memory node
* starpu_data_handle::          StarPU opaque data handle
* void *interface::             StarPU data interface
* starpu_data_register::        Register a piece of data to StarPU
* starpu_data_unregister::      Unregister a piece of data from StarPU
@end menu

@node starpu_access_mode
@subsection @code{starpu_access_mode} -- Data access mode
This datatype describes a data access mode. The different available modes are:
@table @asis
@table @asis 
@item @code{STARPU_R} read-only mode.
@item @code{STARPU_W} write-only mode.
@item @code{STARPU_RW} read-write mode. This is equivalent to @code{STARPU_R|STARPU_W}.
@item @code{STARPU_SCRATCH} scratch memory. A temporary buffer is allocated for the task, but StarPU does not enforce data consistency.
@end table
@end table

@node unsigned memory_node
@subsection @code{unsigned memory_node} -- Memory node
@table @asis
@item @emph{Description}:
Every worker is associated to a memory node which is a logical abstraction of
the address space from which the processing unit gets its data. For instance,
the memory node associated to the different CPU workers represents main memory
(RAM), the memory node associated to a GPU is DRAM embedded on the device.
Every memory node is identified by a logical index which is accessible from the
@code{starpu_worker_get_memory_node} function. When registering a piece of data
to StarPU, the specified memory node indicates where the piece of data
initially resides (we also call this memory node the home node of a piece of
data).
@end table


@node starpu_data_handle
@subsection @code{starpu_data_handle} -- StarPU opaque data handle
@table @asis
@item @emph{Description}:
StarPU uses @code{starpu_data_handle} as an opaque handle to manage a piece of
data. Once a piece of data has been registered to StarPU, it is associated to a
@code{starpu_data_handle} which keeps track of the state of the piece of data
over the entire machine, so that we can maintain data consistency and locate
data replicates for instance.
@end table

@node void *interface
@subsection @code{void *interface} -- StarPU data interface
@table @asis
@item @emph{Description}:
Data management is done at a high-level in StarPU: rather than accessing a mere
list of contiguous buffers, the tasks may manipulate data that are described by
a high-level construct which we call data interface.

An example of data interface is the "vector" interface which describes a
contiguous data array on a spefic memory node. This interface is a simple
structure containing the number of elements in the array, the size of the
elements, and the address of the array in the appropriate address space (this
address may be invalid if there is no valid copy of the array in the memory
node).

When a piece of data managed by StarPU is used by a task, the task
implementation is given a pointer to an interface describing a valid copy of
the data that is accessible from the current processing unit.
@end table

@node starpu_data_register
@subsection @code{starpu_data_register} -- Register a piece of data to StarPU
@table @asis
@item @emph{Description}:
Register a piece of data into the handle located at the @code{handleptr}
address. The @code{interface} buffer contains the initial description of the
data in the home node. The @code{ops} argument is a pointer to a structure
describing the different methods used to manipulate this type of interface. See
@ref{struct starpu_data_interface_ops_t} for more details on this structure.

If @code{home_node} is not a valid memory node, StarPU will automatically
allocate the memory described by the interface the data handle is used for the
first time in write-only mode. Once such data handle has been automatically
allocated, it is possible to access it using any access mode.

Note that StarPU supplies a set of predefined types of interface (eg. vector or
matrix) which can be registered by the means of helper functions (eg.
@code{starpu_vector_data_register} or @code{starpu_matrix_data_register}).
@item @emph{Prototype}:
@code{void starpu_data_register(starpu_data_handle *handleptr,
				uint32_t home_node,
				void *interface,
				struct starpu_data_interface_ops_t *ops);}
@end table

@node starpu_data_unregister
@subsection @code{starpu_data_unregister} -- Unregister a piece of data from StarPU
@table @asis
@item @emph{Description}:
This function unregisters a data handle from StarPU. If the data was
automatically allocated by StarPU because the home node was not valid, all
automatically allocated buffers are freed. Otherwise, a valid copy of the data
is put back into the home node in the buffer that was initially registered.
Using a data handle that has been unregistered from StarPU results in an
undefined behaviour.
@item @emph{Prototype}:
@code{void starpu_data_unregister(starpu_data_handle handle);}
@end table
@c void *starpu_data_get_interface_on_node(starpu_data_handle handle, unsigned memory_node); TODO
@c user interaction with the DSM
@c   void starpu_data_sync_with_mem(struct starpu_data_state_t *state);
@c   void starpu_notify_data_modification(struct starpu_data_state_t *state, uint32_t modifying_node);
@c   struct starpu_data_interface_ops_t *ops

@node Codelets and Tasks
@section Codelets and Tasks

@menu
* struct starpu_codelet::       StarPU codelet structure
* struct starpu_task::          StarPU task structure
* starpu_task_init::            Initialize a Task
* starpu_task_create::          Allocate and Initialize a Task
* starpu_task_deinit::          Release all the resources used by a Task
* starpu_task_destroy::         Destroy a dynamically allocated Task
* starpu_task_wait::            Wait for the termination of a Task
* starpu_task_submit::          Submit a Task
* starpu_task_wait_for_all::    Wait for the termination of all Tasks
@end menu

@node struct starpu_codelet
@subsection @code{struct starpu_codelet} -- StarPU codelet structure
@table @asis
@item @emph{Description}:
The codelet structure describes a kernel that is possibly implemented on
various targets.
@item @emph{Fields}:
@table @asis
@item @code{where}:
Indicates which types of processing units are able to execute the codelet.
@code{STARPU_CPU|STARPU_CUDA} for instance indicates that the codelet is
implemented for both CPU cores and CUDA devices while @code{STARPU_GORDON}
indicates that it is only available on Cell SPUs.

@item @code{cpu_func} (optional):
Is a function pointer to the CPU implementation of the codelet. Its prototype
must be: @code{void cpu_func(void *buffers[], void *cl_arg)}. The first
argument being the array of data managed by the data management library, and
the second argument is a pointer to the argument passed from the @code{cl_arg}
field of the @code{starpu_task} structure.
The @code{cpu_func} field is ignored if @code{STARPU_CPU} does not appear in
the @code{where} field, it must be non-null otherwise.

@item @code{cuda_func} (optional):
Is a function pointer to the CUDA implementation of the codelet. @emph{This
must be a host-function written in the CUDA runtime API}. Its prototype must
be: @code{void cuda_func(void *buffers[], void *cl_arg);}. The @code{cuda_func}
field is ignored if @code{STARPU_CUDA} does not appear in the @code{where}
field, it must be non-null otherwise.

@item @code{opencl_func} (optional):
Is a function pointer to the OpenCL implementation of the codelet. Its
prototype must be:
@code{void opencl_func(starpu_data_interface_t *descr, void *arg);}.
This pointer is ignored if @code{STARPU_OPENCL} does not appear in the
@code{where} field, it must be non-null otherwise.

@item @code{gordon_func} (optional):
This is the index of the Cell SPU implementation within the Gordon library.
See Gordon documentation for more details on how to register a kernel and
retrieve its index.

@item @code{nbuffers}:
Specifies the number of arguments taken by the codelet. These arguments are
managed by the DSM and are accessed from the @code{void *buffers[]}
array. The constant argument passed with the @code{cl_arg} field of the
@code{starpu_task} structure is not counted in this number.  This value should
not be above @code{STARPU_NMAXBUFS}.

@item @code{model} (optional):
This is a pointer to the performance model associated to this codelet. This
optional field is ignored when set to @code{NULL}. TODO

@end table
@end table

@node struct starpu_task
@subsection @code{struct starpu_task} -- StarPU task structure
@table @asis
@item @emph{Description}:
The @code{starpu_task} structure describes a task that can be offloaded on the various
processing units managed by StarPU. It instantiates a codelet. It can either be
allocated dynamically with the @code{starpu_task_create} method, or declared
statically. In the latter case, the programmer has to zero the
@code{starpu_task} structure and to fill the different fields properly. The
indicated default values correspond to the configuration of a task allocated
with @code{starpu_task_create}.

@item @emph{Fields}:
@table @asis
@item @code{cl}:
Is a pointer to the corresponding @code{starpu_codelet} data structure. This
describes where the kernel should be executed, and supplies the appropriate
implementations. When set to @code{NULL}, no code is executed during the tasks,
such empty tasks can be useful for synchronization purposes.

@item @code{buffers}:
TODO

@item @code{cl_arg} (optional) (default = NULL):
This pointer is passed to the codelet through the second argument
of the codelet implementation (e.g. @code{cpu_func} or @code{cuda_func}).
In the specific case of the Cell processor, see the @code{cl_arg_size}
argument.

@item @code{cl_arg_size} (optional, Cell specific):
In the case of the Cell processor, the @code{cl_arg} pointer is not directly
given to the SPU function. A buffer of size @code{cl_arg_size} is allocated on
the SPU. This buffer is then filled with the @code{cl_arg_size} bytes starting
at address @code{cl_arg}. In this case, the argument given to the SPU codelet
is therefore not the @code{cl_arg} pointer, but the address of the buffer in
local store (LS) instead. This field is ignored for CPU, CUDA and OpenCL
codelets.

@item @code{callback_func} (optional) (default = @code{NULL}):
This is a function pointer of prototype @code{void (*f)(void *)} which
specifies a possible callback. If this pointer is non-null, the callback
function is executed @emph{on the host} after the execution of the task. The
callback is passed the value contained in the @code{callback_arg} field. No
callback is executed if the field is set to @code{NULL}.

@item @code{callback_arg} (optional) (default = @code{NULL}):
This is the pointer passed to the callback function. This field is ignored if
the @code{callback_func} is set to @code{NULL}.

@item @code{use_tag} (optional) (default = 0):
If set, this flag indicates that the task should be associated with the tag
contained in the @code{tag_id} field. Tag allow the application to synchronize
with the task and to express task dependencies easily.

@item @code{tag_id}:
This fields contains the tag associated to the task if the @code{use_tag} field
was set, it is ignored otherwise.

@item @code{synchronous}:
If this flag is set, the @code{starpu_task_submit} function is blocking and
returns only when the task has been executed (or if no worker is able to
process the task). Otherwise, @code{starpu_task_submit} returns immediately.

@item @code{priority} (optional) (default = @code{STARPU_DEFAULT_PRIO}):
This field indicates a level of priority for the task. This is an integer value
that must be set between @code{STARPU_MIN_PRIO} (for the least important
tasks) and @code{STARPU_MAX_PRIO} (for the most important tasks) included.
Default priority is @code{STARPU_DEFAULT_PRIO}.  Scheduling strategies that
take priorities into account can use this parameter to take better scheduling
decisions, but the scheduling policy may also ignore it.

@item @code{execute_on_a_specific_worker} (default = 0):
If this flag is set, StarPU will bypass the scheduler and directly affect this
task to the worker specified by the @code{workerid} field.

@item @code{workerid} (optional):
If the @code{execute_on_a_specific_worker} field is set, this field indicates
which is the identifier of the worker that should process this task (as
returned by @code{starpu_worker_get_id}). This field is ignored if
@code{execute_on_a_specific_worker} field is set to 0.

@item @code{detach} (optional) (default = 1):
If this flag is set, it is not possible to synchronize with the task
by the means of @code{starpu_task_wait} later on. Internal data structures
are only guaranteed to be freed once @code{starpu_task_wait} is called if the
flag is not set.

@item @code{destroy} (optional) (default = 1):
If this flag is set, the task structure will automatically be freed, either
after the execution of the callback if the task is detached, or during
@code{starpu_task_wait} otherwise. If this flag is not set, dynamically
allocated data structures will not be freed until @code{starpu_task_destroy} is
called explicitly. Setting this flag for a statically allocated task structure
will result in undefined behaviour.

@end table
@end table

@node starpu_task_init
@subsection @code{starpu_task_init} -- Initialize a Task
@table @asis
@item @emph{Description}:
Initialize a task structure with default values. This function is implicitly
called by @code{starpu_task_create}. By default, tasks initialized with
@code{starpu_task_init} must be deinitialized explicitly with
@code{starpu_task_deinit}. Tasks can also be initialized statically, using the
constant @code{STARPU_TASK_INITIALIZER}.
@item @emph{Prototype}:
@code{void starpu_task_init(struct starpu_task *task);}
@end table

@node starpu_task_create
@subsection @code{starpu_task_create} -- Allocate and Initialize a Task
@table @asis
@item @emph{Description}:
Allocate a task structure and initialize it with default values. Tasks
allocated dynamically with @code{starpu_task_create} are automatically freed when the
task is terminated. If the destroy flag is explicitly unset, the resources used
by the task are freed by calling
@code{starpu_task_destroy}.

@item @emph{Prototype}:
@code{struct starpu_task *starpu_task_create(void);}
@end table

@node starpu_task_deinit
@subsection @code{starpu_task_deinit} -- Release all the resources used by a Task
@table @asis
@item @emph{Description}:
Release all the structures automatically allocated to execute the task. This is
called automatically by @code{starpu_task_destroy}, but the task structure itself is not
freed. This should be used for statically allocated tasks for instance.
@item @emph{Prototype}:
@code{void starpu_task_deinit(struct starpu_task *task);}
@end table



@node starpu_task_destroy
@subsection @code{starpu_task_destroy} -- Destroy a dynamically allocated Task
@table @asis
@item @emph{Description}:
Free the resource allocated during @code{starpu_task_create}. This function can be
called automatically after the execution of a task by setting the
@code{destroy} flag of the @code{starpu_task} structure (default behaviour).
Calling this function on a statically allocated task results in an undefined
behaviour.

@item @emph{Prototype}:
@code{void starpu_task_destroy(struct starpu_task *task);}
@end table

@node starpu_task_wait
@subsection @code{starpu_task_wait} -- Wait for the termination of a Task
@table @asis
@item @emph{Description}:
This function blocks until the task has been executed. It is not possible to
synchronize with a task more than once. It is not possible to wait for
synchronous or detached tasks.
@item @emph{Return value}:
Upon successful completion, this function returns 0. Otherwise, @code{-EINVAL}
indicates that the specified task was either synchronous or detached.
@item @emph{Prototype}:
@code{int starpu_task_wait(struct starpu_task *task);}
@end table



@node starpu_task_submit
@subsection @code{starpu_task_submit} -- Submit a Task
@table @asis
@item @emph{Description}:
This function submits a task to StarPU. Calling this function does
not mean that the task will be executed immediately as there can be data or task
(tag) dependencies that are not fulfilled yet: StarPU will take care of
scheduling this task with respect to such dependencies.
This function returns immediately if the @code{synchronous} field of the
@code{starpu_task} structure was set to 0, and block until the termination of
the task otherwise. It is also possible to synchronize the application with
asynchronous tasks by the means of tags, using the @code{starpu_tag_wait}
function for instance.
@item @emph{Return value}:
In case of success, this function returns 0, a return value of @code{-ENODEV}
means that there is no worker able to process this task (e.g. there is no GPU
available and this task is only implemented for CUDA devices).
@item @emph{Prototype}:
@code{int starpu_task_submit(struct starpu_task *task);}
@end table

@node starpu_task_wait_for_all
@subsection @code{starpu_task_wait_for_all} -- Wait for the termination of all Tasks
@table @asis
@item @emph{Description}:
This function blocks until all the tasks that were submitted are terminated.
@item @emph{Prototype}:
@code{void starpu_task_wait_for_all(void);}
@end table




@c Callbacks : what can we put in callbacks ?

@node Explicit Dependencies
@section Explicit Dependencies

@menu
* starpu_task_declare_deps_array::        starpu_task_declare_deps_array
* starpu_tag_t::                Task logical identifier
* starpu_tag_declare_deps::     Declare the Dependencies of a Tag
* starpu_tag_declare_deps_array::  Declare the Dependencies of a Tag
* starpu_tag_wait::             Block until a Tag is terminated
* starpu_tag_wait_array::       Block until a set of Tags is terminated
* starpu_tag_remove::           Destroy a Tag
* starpu_tag_notify_from_apps::  Feed a tag explicitly
@end menu

@node starpu_task_declare_deps_array
@subsection @code{starpu_task_declare_deps_array} -- Declare task dependencies
@table @asis
@item @emph{Description}:
Declare task dependencies between a @code{task} and an array of tasks of length
@code{ndeps}. This function must be called prior to the submission of the task,
but it may called after the submission or the execution of the tasks in the
array provided the tasks are still valid (ie. they were not automatically
destroyed). Calling this function on a task that was already submitted or with
an entry of @code{task_array} that is not a valid task anymore results in an
undefined behaviour. If @code{ndeps} is null, no dependency is added. It is
possible to call @code{starpu_task_declare_deps_array} multiple times on the
same task, in this case, the dependencies are added. It is possible to have
redundancy in the task dependencies.
@item @emph{Prototype}:
@code{void starpu_task_declare_deps_array(struct starpu_task *task, unsigned ndeps, struct starpu_task *task_array[]);}
@end table



@node starpu_tag_t
@subsection @code{starpu_tag_t} -- Task logical identifier
@table @asis
@item @emph{Description}:
It is possible to associate a task with a unique ``tag'' and to express
dependencies between tasks by the means of those tags. To do so, fill the
@code{tag_id} field of the @code{starpu_task} structure with a tag number (can
be arbitrary) and set the @code{use_tag} field to 1.

If @code{starpu_tag_declare_deps} is called with this tag number, the task will
not be started until the tasks which holds the declared dependency tags are
completed.
@end table

@node starpu_tag_declare_deps
@subsection @code{starpu_tag_declare_deps} -- Declare the Dependencies of a Tag
@table @asis
@item @emph{Description}:
Specify the dependencies of the task identified by tag @code{id}. The first
argument specifies the tag which is configured, the second argument gives the
number of tag(s) on which @code{id} depends. The following arguments are the
tags which have to be terminated to unlock the task.

This function must be called before the associated task is submitted to StarPU
with @code{starpu_task_submit}.

@item @emph{Remark}
Because of the variable arity of @code{starpu_tag_declare_deps}, note that the
last arguments @emph{must} be of type @code{starpu_tag_t}: constant values
typically need to be explicitly casted. Using the
@code{starpu_tag_declare_deps_array} function avoids this hazard.

@item @emph{Prototype}:
@code{void starpu_tag_declare_deps(starpu_tag_t id, unsigned ndeps, ...);}

@item @emph{Example}:
@cartouche
@example
/*  Tag 0x1 depends on tags 0x32 and 0x52 */
starpu_tag_declare_deps((starpu_tag_t)0x1,
        2, (starpu_tag_t)0x32, (starpu_tag_t)0x52);

@end example
@end cartouche

@end table

@node starpu_tag_declare_deps_array
@subsection @code{starpu_tag_declare_deps_array} -- Declare the Dependencies of a Tag
@table @asis
@item @emph{Description}:
This function is similar to @code{starpu_tag_declare_deps}, except that its
does not take a variable number of arguments but an array of tags of size
@code{ndeps}.
@item @emph{Prototype}:
@code{void starpu_tag_declare_deps_array(starpu_tag_t id, unsigned ndeps, starpu_tag_t *array);}
@item @emph{Example}:
@cartouche
@example
/*  Tag 0x1 depends on tags 0x32 and 0x52 */
starpu_tag_t tag_array[2] = @{0x32, 0x52@};
starpu_tag_declare_deps_array((starpu_tag_t)0x1, 2, tag_array);
@end example
@end cartouche


@end table


@node starpu_tag_wait
@subsection @code{starpu_tag_wait} -- Block until a Tag is terminated
@table @asis
@item @emph{Description}:
This function blocks until the task associated to tag @code{id} has been
executed. This is a blocking call which must therefore not be called within
tasks or callbacks, but only from the application directly.  It is possible to
synchronize with the same tag multiple times, as long as the
@code{starpu_tag_remove} function is not called.  Note that it is still
possible to synchronize with a tag associated to a task which @code{starpu_task}
data structure was freed (e.g. if the @code{destroy} flag of the
@code{starpu_task} was enabled).

@item @emph{Prototype}:
@code{void starpu_tag_wait(starpu_tag_t id);}
@end table

@node starpu_tag_wait_array
@subsection @code{starpu_tag_wait_array} -- Block until a set of Tags is terminated
@table @asis
@item @emph{Description}:
This function is similar to @code{starpu_tag_wait} except that it blocks until
@emph{all} the @code{ntags} tags contained in the @code{id} array are
terminated.
@item @emph{Prototype}:
@code{void starpu_tag_wait_array(unsigned ntags, starpu_tag_t *id);}
@end table

@node starpu_tag_remove
@subsection @code{starpu_tag_remove} -- Destroy a Tag
@table @asis
@item @emph{Description}:
This function releases the resources associated to tag @code{id}. It can be
called once the corresponding task has been executed and when there is
no other tag that depend on this tag anymore.
@item @emph{Prototype}:
@code{void starpu_tag_remove(starpu_tag_t id);}
@end table

@node starpu_tag_notify_from_apps
@subsection @code{starpu_tag_notify_from_apps} -- Feed a Tag explicitly
@table @asis
@item @emph{Description}:
This function explicitly unlocks tag @code{id}. It may be useful in the
case of applications which execute part of their computation outside StarPU
tasks (e.g. third-party libraries).  It is also provided as a
convenient tool for the programmer, for instance to entirely construct the task
DAG before actually giving StarPU the opportunity to execute the tasks.
@item @emph{Prototype}:
@code{void starpu_tag_notify_from_apps(starpu_tag_t id);}
@end table

@node Implicit Data Dependencies
@section Implicit Data Dependencies

@menu
* starpu_data_set_default_sequential_consistency_flag::        starpu_data_set_default_sequential_consistency_flag
* starpu_data_get_default_sequential_consistency_flag::        starpu_data_get_default_sequential_consistency_flag
* starpu_data_set_sequential_consistency_flag::                starpu_data_set_sequential_consistency_flag
@end menu

In this section, we describe how StarPU makes it possible to insert implicit
task dependencies in order to enforce sequential data consistency. When this
data consistency is enabled on a specific data handle, any data access will
appear as sequentially consistent from the application. For instance, if the
application submits two tasks that access the same piece of data in read-only
mode, and then a third task that access it in write mode, dependencies will be
added between the two first tasks and the third one. Implicit data dependencies
are also inserted in the case of data accesses from the application.

@node starpu_data_set_default_sequential_consistency_flag
@subsection @code{starpu_data_set_default_sequential_consistency_flag} -- Set default sequential consistency flag
@table @asis
@item @emph{Description}:
Set the default sequential consistency flag. If a non-null value is passed, a
sequential data consistency will be enforced for all handles registered after
this function call, otherwise it is disabled. By default, StarPU enables
sequential data consistency. It is also possible to select the data consistency
mode of a specific data handle with the
@code{starpu_data_set_sequential_consistency_flag} function.
@item @emph{Prototype}:
@code{void starpu_data_set_default_sequential_consistency_flag(unsigned flag);}
@end table

@node starpu_data_get_default_sequential_consistency_flag
@subsection @code{starpu_data_get_default_sequential_consistency_flag} -- Get current default sequential consistency flag
@table @asis
@item @emph{Description}:
This function returns the current default sequential consistency flag.
@item @emph{Prototype}:
@code{unsigned starpu_data_set_default_sequential_consistency_flag(void);}
@end table

@node starpu_data_set_sequential_consistency_flag
@subsection @code{starpu_data_set_sequential_consistency_flag} -- Set data sequential consistency mode
@table @asis
@item @emph{Description}:
Select the data consistency mode associated to a data handle. The consistency
mode set using this function has the priority over the default mode which can
be set with @code{starpu_data_set_sequential_consistency_flag}.
@item @emph{Prototype}:
@code{void starpu_data_set_sequential_consistency_flag(starpu_data_handle handle, unsigned flag);}
@end table

@node Profiling API
@section Profiling API

@menu
* starpu_profiling_status_set::           starpu_profiling_status_set
* starpu_profiling_status_get::           starpu_profiling_status_get
* struct starpu_task_profiling_info::     task profiling information
* struct starpu_worker_profiling_info::   worker profiling information
* starpu_worker_get_profiling_info::      starpu_worker_get_profiling_info
* struct starpu_bus_profiling_info::      bus profiling information
@end menu

@node starpu_profiling_status_set
@subsection @code{starpu_profiling_status_set} -- Set current profiling status
@table @asis
@item @emph{Description}:
Thie function sets the profiling status. Profiling is activated by passing
@code{STARPU_PROFILING_ENABLE} in @code{status}. Passing
@code{STARPU_PROFILING_DISABLE} disables profiling. Calling this function
resets all profiling measurements. When profiling is enabled, the
@code{profiling_info} field of the @code{struct starpu_task} structure points
to a valid @code{struct starpu_task_profiling_info} structure containing
information about the execution of the task.
@item @emph{Return value}:
Negative return values indicate an error, otherwise the previous status is
returned.
@item @emph{Prototype}:
@code{int starpu_profiling_status_set(int status);}
@end table

@node starpu_profiling_status_get
@subsection @code{starpu_profiling_status_get} -- Get current profiling status
@table @asis
@item @emph{Description}:
Return the current profiling status or a negative value in case there was an error.
@item @emph{Prototype}:
@code{int starpu_profiling_status_get(void);}
@end table

@node struct starpu_task_profiling_info
@subsection @code{struct starpu_task_profiling_info} -- Task profiling information
@table @asis
@item @emph{Description}:
This structure contains information about the execution of a task. It is
accessible from the @code{.profiling_info} field of the @code{starpu_task}
structure if profiling was enabled.
@item @emph{Fields}:
@table @asis
@item @code{submit_time}:
Date of task submission (relative to the initialization of StarPU).
@item @code{start_time}:
Date of task execution beginning (relative to the initialization of StarPU).
@item @code{end_time}:
Date of task execution termination (relative to the initialization of StarPU).
@item @code{workerid}:
Identifier of the worker which has executed the task.
@end table
@end table

@node struct starpu_worker_profiling_info
@subsection @code{struct starpu_worker_profiling_info} -- Worker profiling information
@table @asis
@item @emph{Description}:
This structure contains the profiling information associated to a worker.
@item @emph{Fields}:
@table @asis
@item @code{start_time}:
Starting date for the reported profiling measurements.
@item @code{total_time}:
Duration of the profiling measurement interval.
@item @code{executing_time}:
Time spent by the worker to execute tasks during the profiling measurement interval.
@item @code{sleeping_time}:
Time spent idling by the worker during the profiling measurement interval.
@item @code{executed_tasks}:
Number of tasks executed by the worker during the profiling measurement interval.
@end table
@end table

@node starpu_worker_get_profiling_info
@subsection @code{starpu_worker_get_profiling_info} -- Get worker profiling info
@table @asis

@item @emph{Description}:
Get the profiling info associated to the worker identified by @code{workerid},
and reset the profiling measurements. If the @code{worker_info} argument is
NULL, only reset the counters associated to worker @code{workerid}.
@item @emph{Return value}:
Upon successful completion, this function returns 0. Otherwise, a negative
value is returned.

@item @emph{Prototype}:
@code{int starpu_worker_get_profiling_info(int workerid, struct starpu_worker_profiling_info *worker_info);}
@end table

@node struct starpu_bus_profiling_info
@subsection @code{struct starpu_bus_profiling_info} -- Bus profiling information
@table @asis
@item @emph{Description}:
TODO
@item @emph{Fields}:
@table @asis
@item @code{start_time}:
TODO
@item @code{total_time}:
TODO
@item @code{transferred_bytes}:
TODO
@item @code{transfer_count}:
TODO
@end table
@end table


@node CUDA extensions
@section CUDA extensions

@c void starpu_data_malloc_pinned_if_possible(float **A, size_t dim);

@menu
* starpu_cuda_get_local_stream::  Get current worker's CUDA stream
* starpu_helper_cublas_init::   Initialize CUBLAS on every CUDA device
* starpu_helper_cublas_shutdown::  Deinitialize CUBLAS on every CUDA device
@end menu

@node starpu_cuda_get_local_stream
@subsection @code{starpu_cuda_get_local_stream} -- Get current worker's CUDA stream
@table @asis
@item @emph{Description}:
StarPU provides a stream for every CUDA device controlled by StarPU. This
function is only provided for convenience so that programmers can easily use
asynchronous operations within codelets without having to create a stream by
hand. Note that the application is not forced to use the stream provided by
@code{starpu_cuda_get_local_stream} and may also create its own streams.

@item @emph{Prototype}:
@code{cudaStream_t *starpu_cuda_get_local_stream(void);}
@end table

@node starpu_helper_cublas_init
@subsection @code{starpu_helper_cublas_init} -- Initialize CUBLAS on every CUDA device
@table @asis
@item @emph{Description}:
The CUBLAS library must be initialized prior to any CUBLAS call. Calling
@code{starpu_helper_cublas_init} will initialize CUBLAS on every CUDA device
controlled by StarPU. This call blocks until CUBLAS has been properly
initialized on every device.

@item @emph{Prototype}:
@code{void starpu_helper_cublas_init(void);}
@end table

@node starpu_helper_cublas_shutdown
@subsection @code{starpu_helper_cublas_shutdown} -- Deinitialize CUBLAS on every CUDA device
@table @asis
@item @emph{Description}:
This function synchronously deinitializes the CUBLAS library on every CUDA device.

@item @emph{Prototype}:
@code{void starpu_helper_cublas_shutdown(void);}
@end table

@node OpenCL extensions
@section OpenCL extensions

@menu
* Enabling OpenCL::             Enabling OpenCL
* Compiling OpenCL codelets::   Compiling OpenCL codelets
@end menu

@node Enabling OpenCL
@subsection Enabling OpenCL

On GPU devices which can run both CUDA and OpenCL, CUDA will be
enabled by default. 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 Compiling OpenCL codelets
@subsection Compiling OpenCL codelets

TODO

@node Cell extensions
@section Cell extensions

nothing yet.

@node Miscellaneous helpers
@section Miscellaneous helpers

@menu
* starpu_execute_on_each_worker::  Execute a function on a subset of workers
@end menu

@node starpu_execute_on_each_worker
@subsection @code{starpu_execute_on_each_worker} -- Execute a function on a subset of workers
@table @asis
@item @emph{Description}:
When calling this method, the offloaded function specified by the first argument is
executed by every StarPU worker that may execute the function.
The second argument is passed to the offloaded function.
The last argument specifies on which types of processing units the function
should be executed. Similarly to the @code{where} field of the
@code{starpu_codelet} structure, it is possible to specify that the function
should be executed on every CUDA device and every CPU by passing
@code{STARPU_CPU|STARPU_CUDA}.
This function blocks until the function has been executed on every appropriate
processing units, so that it may not be called from a callback function for
instance.

@item @emph{Prototype}:
@code{void starpu_execute_on_each_worker(void (*func)(void *), void *arg, uint32_t where);}
@end table


@c ---------------------------------------------------------------------
@c Basic Examples
@c ---------------------------------------------------------------------

@node Basic Examples
@chapter Basic Examples

@menu
* Compiling and linking options::  
* Hello World::                 Submitting Tasks
* Scaling a Vector::  Manipulating Data
* Vector Scaling on an Hybrid CPU/GPU Machine::  Handling Heterogeneous Architectures
@end menu

@node Compiling and linking options
@section Compiling and linking options

Let's suppose StarPU has been installed in the directory
@code{$STARPU_DIR}. As explained in @ref{Setting flags for compiling and linking applications},
the variable @code{PKG_CONFIG_PATH} needs to be set. It is also
necessary to set the variable @code{LD_LIBRARY_PATH} to locate dynamic
libraries at runtime.

@example
% PKG_CONFIG_PATH=$STARPU_DIR/lib/pkgconfig:$PKG_CONFIG_PATH
% LD_LIBRARY_PATH=$STARPU_DIR/lib:$LD_LIBRARY_PATH
@end example

The Makefile could for instance contain the following lines to define which
options must be given to the compiler and to the linker:

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

@node Hello World
@section Hello World

@menu
* Required Headers::            
* Defining a Codelet::          
* Submitting a Task::           
* Execution of Hello World::    
@end menu

In this section, we show how to implement a simple program that submits a task to StarPU.

@node Required Headers
@subsection Required Headers

The @code{starpu.h} header should be included in any code using StarPU.

@cartouche
@smallexample
#include <starpu.h>
@end smallexample
@end cartouche


@node Defining a Codelet
@subsection Defining a Codelet

@cartouche
@smallexample
void cpu_func(void *buffers[], void *cl_arg)
@{
    float *array = cl_arg;

    printf("Hello world (array = @{%f, %f@} )\n", array[0], array[1]);
@}

starpu_codelet cl =
@{
    .where = STARPU_CPU,
    .cpu_func = cpu_func,
    .nbuffers = 0
@};
@end smallexample
@end cartouche

A codelet is a structure that represents a computational kernel. Such a codelet
may contain an implementation of the same kernel on different architectures
(e.g. CUDA, Cell's SPU, x86, ...).

The @code{nbuffers} field specifies the number of data buffers that are
manipulated by the codelet: here the codelet does not access or modify any data
that is controlled by our data management library. Note that the argument
passed to the codelet (the @code{cl_arg} field of the @code{starpu_task}
structure) does not count as a buffer since it is not managed by our data
management library.

@c TODO need a crossref to the proper description of "where" see bla for more ...
We create a codelet which may only be executed on the CPUs. The @code{where}
field is a bitmask that defines where the codelet may be executed. Here, the
@code{STARPU_CPU} value means that only CPUs can execute this codelet
(@pxref{Codelets and Tasks} for more details on this field).
When a CPU core executes a codelet, it calls the @code{cpu_func} function,
which @emph{must} have the following prototype:

@code{void (*cpu_func)(void *buffers[], void *cl_arg);}

In this example, we can ignore the first argument of this function which gives a
description of the input and output buffers (e.g. the size and the location of
the matrices). The second argument is a pointer to a buffer passed as an
argument to the codelet by the means of the @code{cl_arg} field of the
@code{starpu_task} structure.

@c TODO rewrite so that it is a little clearer ?
Be aware that this may be a pointer to a
@emph{copy} of the actual buffer, and not the pointer given by the programmer:
if the codelet modifies this buffer, there is no guarantee that the initial
buffer will be modified as well: this for instance implies that the buffer
cannot be used as a synchronization medium.

@node Submitting a Task
@subsection Submitting a Task

@cartouche
@smallexample
void callback_func(void *callback_arg)
@{
    printf("Callback function (arg %x)\n", callback_arg);
@}

int main(int argc, char **argv)
@{
    /* @b{initialize StarPU} */
    starpu_init(NULL);

    struct starpu_task *task = starpu_task_create();

    task->cl = &cl; /* @b{Pointer to the codelet defined above} */

    float array[2] = @{1.0f, -1.0f@};
    task->cl_arg = &array;
    task->cl_arg_size = sizeof(array);

    task->callback_func = callback_func;
    task->callback_arg = 0x42;

    /* @b{starpu_task_submit will be a blocking call} */
    task->synchronous = 1;

    /* @b{submit the task to StarPU} */
    starpu_task_submit(task);

    /* @b{terminate StarPU} */
    starpu_shutdown();

    return 0;
@}
@end smallexample
@end cartouche

Before submitting any tasks to StarPU, @code{starpu_init} must be called. The
@code{NULL} argument specifies that we use default configuration. Tasks cannot
be submitted after the termination of StarPU by a call to
@code{starpu_shutdown}.

In the example above, a task structure is allocated by a call to
@code{starpu_task_create}. This function only allocates and fills the
corresponding structure with the default settings (@pxref{starpu_task_create}),
but it does not submit the task to StarPU.

@c not really clear ;)
The @code{cl} field is a pointer to the codelet which the task will
execute: in other words, the codelet structure describes which computational
kernel should be offloaded on the different architectures, and the task
structure is a wrapper containing a codelet and the piece of data on which the
codelet should operate.

The optional @code{cl_arg} field is a pointer to a buffer (of size
@code{cl_arg_size}) with some parameters for the kernel
described by the codelet. For instance, if a codelet implements a computational
kernel that multiplies its input vector by a constant, the constant could be
specified by the means of this buffer, instead of registering it.

Once a task has been executed, an optional callback function can be called.
While the computational kernel could be offloaded on various architectures, the
callback function is always executed on a CPU. The @code{callback_arg}
pointer is passed as an argument of the callback. The prototype of a callback
function must be:

@code{void (*callback_function)(void *);}

If the @code{synchronous} field is non-null, task submission will be
synchronous: the @code{starpu_task_submit} function will not return until the
task was executed. Note that the @code{starpu_shutdown} method does not
guarantee that asynchronous tasks have been executed before it returns.

@node Execution of Hello World
@subsection Execution of Hello World

@smallexample
% make helloWorld
cc $(pkg-config --cflags libstarpu)  $(pkg-config --libs libstarpu) helloWorld.c -o helloWorld
% ./helloWorld
Hello world (array = @{1.000000, -1.000000@} )
Callback function (arg 42)
@end smallexample

@node Scaling a Vector
@section Manipulating Data: Scaling a Vector

The previous example has shown how to submit tasks. In this section,
we show how StarPU tasks can manipulate data. The full source code for
this example is given in @ref{Full source code for the 'Scaling a Vector' example}.

@menu
* Source code of Vector Scaling::  
* Execution of Vector Scaling::  
@end menu

@node Source code of Vector Scaling
@subsection Source code of Vector Scaling

Programmers can describe the data layout of their application so that StarPU is
responsible for enforcing data coherency and availability across the machine.
Instead of handling complex (and non-portable) mechanisms to perform data
movements, programmers only declare which piece of data is accessed and/or
modified by a task, and StarPU makes sure that when a computational kernel
starts somewhere (e.g. on a GPU), its data are available locally.

Before submitting those tasks, the programmer first needs to declare the
different pieces of data to StarPU using the @code{starpu_*_data_register}
functions. To ease the development of applications for StarPU, it is possible
to describe multiple types of data layout. A type of data layout is called an
@b{interface}. By default, there are different interfaces available in StarPU:
here we will consider the @b{vector interface}.

The following lines show how to declare an array of @code{NX} elements of type
@code{float} using the vector interface:

@cartouche
@smallexample
float vector[NX];

starpu_data_handle vector_handle;
starpu_vector_data_register(&vector_handle, 0, (uintptr_t)vector, NX,
                            sizeof(vector[0]));
@end smallexample
@end cartouche

The first argument, called the @b{data handle}, is an opaque pointer which
designates the array in StarPU. This is also the structure which is used to
describe which data is used by a task. The second argument is the node number
where the data currently resides. Here it is 0 since the @code{vector} array is in
the main memory. Then comes the pointer @code{vector} where the data can be found,
the number of elements in the vector and the size of each element.
It is possible to construct a StarPU task that will manipulate the
vector and a constant factor.

@cartouche
@smallexample
float factor = 3.14;
struct starpu_task *task = starpu_task_create();

task->cl = &cl;                          /* @b{Pointer to the codelet defined below} */
task->buffers[0].handle = vector_handle; /* @b{First parameter of the codelet} */
task->buffers[0].mode = STARPU_RW;
task->cl_arg = &factor;
task->cl_arg_size = sizeof(factor);
task->synchronous = 1;

starpu_task_submit(task);
@end smallexample
@end cartouche

Since the factor is a mere float value parameter, it does not need a preliminary registration, and
can just be passed through the @code{cl_arg} pointer like in the previous
example.  The vector parameter is described by its handle.
There are two fields in each element of the @code{buffers} array.
@code{handle} is the handle of the data, and @code{mode} specifies how the
kernel will access the data (@code{STARPU_R} for read-only, @code{STARPU_W} for
write-only and @code{STARPU_RW} for read and write access).

The definition of the codelet can be written as follows:

@cartouche
@smallexample
void scal_cpu_func(void *buffers[], void *cl_arg)
@{
    unsigned i;
    float *factor = cl_arg;

    /* length of the vector */
    unsigned n = STARPU_GET_VECTOR_NX(buffers[0]);
    /* local copy of the vector pointer */
    float *val = (float *)STARPU_GET_VECTOR_PTR(buffers[0]);

    for (i = 0; i < n; i++)
        val[i] *= *factor;
@}

starpu_codelet cl = @{
    .where = STARPU_CPU,
    .cpu_func = scal_cpu_func,
    .nbuffers = 1
@};
@end smallexample
@end cartouche

The second argument of the @code{scal_cpu_func} function contains a pointer to the
parameters of the codelet (given in @code{task->cl_arg}), so that we read the
constant factor from this pointer. The first argument is an array that gives
a description of all the buffers passed in the @code{task->buffers}@ array. The
size of this array is given by the @code{nbuffers} field of the codelet
structure. For the sake of generality, this array contains pointers to the
different interfaces describing each buffer.  In the case of the @b{vector
interface}, the location of the vector (resp. its length) is accessible in the
@code{ptr} (resp. @code{nx}) of this array. Since the vector is accessed in a
read-write fashion, any modification will automatically affect future accesses
to this vector made by other tasks.

@node Execution of Vector Scaling
@subsection Execution of Vector Scaling

@smallexample
% make vector
cc $(pkg-config --cflags libstarpu)  $(pkg-config --libs libstarpu)  vector.c   -o vector
% ./vector
0.000000 3.000000 6.000000 9.000000 12.000000
@end smallexample

@node Vector Scaling on an Hybrid CPU/GPU Machine
@section Vector Scaling on an Hybrid CPU/GPU Machine

Contrary to the previous examples, the task submitted in this example may not
only be executed by the CPUs, but also by a CUDA device.

@menu
* Definition of the CUDA Codelet::  
* Definition of the OpenCL Codelet::  
* Definition of the Main Code::  
* Compilation and execution of Hybrid Vector Scaling::  
@end menu

@node Definition of the CUDA Codelet
@subsection Definition of the CUDA Codelet

The CUDA implementation can be written as follows. It needs to be
compiled with a CUDA compiler such as nvcc, the NVIDIA CUDA compiler
driver.

@cartouche
@smallexample
#include <starpu.h>

static __global__ void vector_mult_cuda(float *val, unsigned n,
                                        float factor)
@{
    unsigned i;
    for(i = 0 ; i < n ; i++)
        val[i] *= factor;
@}

extern "C" void scal_cuda_func(void *buffers[], void *_args)
@{
    float *factor = (float *)_args;

    /* length of the vector */
    unsigned n = STARPU_GET_VECTOR_NX(buffers[0]);
    /* local copy of the vector pointer */
    float *val = (float *)STARPU_GET_VECTOR_PTR(buffers[0]);

@i{    vector_mult_cuda<<<1,1>>>(val, n, *factor);}

@i{    cudaThreadSynchronize();}
@}
@end smallexample
@end cartouche

@node Definition of the OpenCL Codelet
@subsection Definition of the OpenCL Codelet

The OpenCL implementation can be written as follows. StarPU provides
tools to compile a OpenCL codelet stored in a file.

@cartouche
@smallexample
__kernel void vector_mult_opencl(__global float* val, int nx, float factor)
@{
        const int i = get_global_id(0);
        if (i < nx) @{
                val[i] *= factor;
        @}
@}
@end smallexample
@end cartouche

@cartouche
@smallexample
#include <starpu.h>
@i{#include <starpu_opencl.h>}

@i{extern struct starpu_opencl_codelet codelet;}

void scal_opencl_func(void *buffers[], void *_args)
@{
    float *factor = _args;
@i{    int id, devid, err;}
@i{    cl_kernel kernel;}
@i{    cl_command_queue queue;}

    /* length of the vector */
    unsigned n = STARPU_GET_VECTOR_NX(buffers[0]);
    /* local copy of the vector pointer */
    float *val = (float *)STARPU_GET_VECTOR_PTR(buffers[0]);

@i{    id = starpu_worker_get_id();}
@i{    devid = starpu_worker_get_devid(id);}

@i{    err = starpu_opencl_load_kernel(&kernel, &queue, &codelet,}
@i{                    "vector_mult_opencl", devid);   /* @b{Name of the codelet defined above} */}
@i{    if (err != CL_SUCCESS) STARPU_OPENCL_REPORT_ERROR(err);}

@i{    err = 0;}
@i{    err = clSetKernelArg(kernel, 0, sizeof(cl_mem), &val);}
@i{    err = clSetKernelArg(kernel, 1, sizeof(n), &n);}
@i{    err |= clSetKernelArg(kernel, 2, sizeof(*factor), factor);}
@i{    if (err) STARPU_OPENCL_REPORT_ERROR(err);}

@i{    @{}
@i{        size_t global=1;}
@i{        size_t local=1;}
@i{        err = clEnqueueNDRangeKernel(queue, kernel, 1, NULL, &global, &local, 0, NULL, NULL);}
@i{        if (err != CL_SUCCESS) STARPU_OPENCL_REPORT_ERROR(err);}
@i{    @}}

@i{    clFinish(queue);}

@i{    starpu_opencl_release_kernel(kernel);}
@}
@end smallexample
@end cartouche


@node Definition of the Main Code
@subsection Definition of the Main Code

The CPU implementation is the same as in the previous section.

Here is the source of the main application. You can notice the value of the
field @code{where} for the codelet. We specify
@code{STARPU_CPU|STARPU_CUDA|STARPU_OPENCL} to indicate to StarPU that the codelet
can be executed either on a CPU or on a CUDA or an OpenCL device.

@cartouche
@smallexample
#include <starpu.h>

#define NX 2048

extern void scal_cuda_func(void *buffers[], void *_args);
extern void scal_cpu_func(void *buffers[], void *_args);

/* @b{Definition of the codelet} */
static starpu_codelet cl = @{
    .where = STARPU_CPU|STARPU_CUDA|STARPU_OPENCL; /* @b{It can be executed on a CPU} */
                                     /* @b{or on a CUDA device} */
    .cuda_func = scal_cuda_func;
    .cpu_func = scal_cpu_func;
    .opencl_func = scal_opencl_func;
    .nbuffers = 1;
@}

int main(int argc, char **argv)
@{
    float *vector;
    int i, ret;
    float factor=3.0;
    struct starpu_task *task;
    starpu_data_handle vector_handle;

    starpu_init(NULL);                            /* @b{Initialising StarPU} */

    vector = malloc(NX*sizeof(vector[0]));
    assert(vector);
    for(i=0 ; i<NX ; i++) vector[i] = i;
@end smallexample
@end cartouche

@cartouche
@smallexample
    /* @b{Registering data within StarPU} */
    starpu_vector_data_register(&vector_handle, 0, (uintptr_t)vector,
                                NX, sizeof(vector[0]));

    /* @b{Definition of the task} */
    task = starpu_task_create();
    task->cl = &cl;
    task->buffers[0].handle = vector_handle;
    task->buffers[0].mode = STARPU_RW;
    task->cl_arg = &factor;
    task->cl_arg_size = sizeof(factor);
@end smallexample
@end cartouche

@cartouche
@smallexample
    /* @b{Submitting the task} */
    ret = starpu_task_submit(task);
    if (ret == -ENODEV) @{
            fprintf(stderr, "No worker may execute this task\n");
            return 1;
    @}

    /* @b{Waiting for its termination} */
    starpu_task_wait_for_all();

    /* @b{Update the vector in RAM} */
    starpu_data_sync_with_mem(vector_handle, STARPU_R);
@end smallexample
@end cartouche

@cartouche
@smallexample
    /* @b{Access the data} */
    for(i=0 ; i<NX; i++) @{
      fprintf(stderr, "%f ", vector[i]);
    @}
    fprintf(stderr, "\n");

    /* @b{Release the data and shutdown StarPU} */
    starpu_data_release_from_mem(vector_handle);
    starpu_shutdown();

    return 0;
@}
@end smallexample
@end cartouche

@node Compilation and execution of Hybrid Vector Scaling
@subsection Compilation and execution of Hybrid Vector Scaling

The Makefile given at the beginning of the section must be extended to
give the rules to compile the CUDA source code.

@cartouche
@smallexample
CFLAGS	+=	$(shell pkg-config --cflags libstarpu)
LDFLAGS	+=	$(shell pkg-config --libs libstarpu)
CC	=	gcc

vector: vector.o vector_cpu.o vector_cuda.o

%.o: %.cu
       nvcc $(CFLAGS) $< -c $@

clean:
       rm -f vector *.o
@end smallexample
@end cartouche

@smallexample
% make
@end smallexample

and to execute it, with the default configuration:

@smallexample
% ./vector
0.000000 3.000000 6.000000 9.000000 12.000000
@end smallexample

or for example, by disabling CPU devices:

@smallexample
% STARPU_NCPUS=0 ./vector
0.000000 3.000000 6.000000 9.000000 12.000000
@end smallexample

or by disabling CUDA devices:

@smallexample
% STARPU_NCUDA=0 ./vector
0.000000 3.000000 6.000000 9.000000 12.000000
@end smallexample

@c TODO: Add performance model example (and update basic_examples)

@c ---------------------------------------------------------------------
@c Advanced Topics
@c ---------------------------------------------------------------------

@node Advanced Topics
@chapter Advanced Topics

@menu
* Defining a new data interface::
* Defining a new scheduling policy::
@end menu

@node Defining a new data interface
@section Defining a new data interface

@menu
* struct starpu_data_interface_ops_t::  Per-interface methods
* struct starpu_data_copy_methods::	Per-interface data transfer methods
* An example of data interface::        An example of data interface
@end menu

@node struct starpu_data_interface_ops_t
@subsection @code{struct starpu_data_interface_ops_t} -- Per-interface methods
@table @asis
@item @emph{Description}:
TODO describe all the different fields
@end table

@node struct starpu_data_copy_methods
@subsection @code{struct starpu_data_copy_methods} -- Per-interface data transfer methods
@table @asis
@item @emph{Description}:
TODO describe all the different fields
@end table

@node An example of data interface
@subsection An example of data interface
@table @asis
TODO
@end table

@node Defining a new scheduling policy
@section Defining a new scheduling policy

TODO

@c ---------------------------------------------------------------------
@c Appendices
@c ---------------------------------------------------------------------

@c ---------------------------------------------------------------------
@c Full source code for the 'Scaling a Vector' example
@c ---------------------------------------------------------------------

@node Full source code for the 'Scaling a Vector' example
@appendix Full source code for the 'Scaling a Vector' example

@menu
* Main application::            
* CPU Codelet::                 
* CUDA Codelet::                
* OpenCL Codelet::              
@end menu

@node Main application
@section Main application

@smallexample
@include vector_scal_c.texi
@end smallexample

@node CPU Codelet
@section CPU Codelet

@smallexample
@include vector_scal_cpu.texi
@end smallexample

@node CUDA Codelet
@section CUDA Codelet

@smallexample
@include vector_scal_cuda.texi
@end smallexample

@node OpenCL Codelet
@section OpenCL Codelet

@menu
* Invoking the kernel::         
* Source of the kernel::        
@end menu

@node Invoking the kernel
@subsection Invoking the kernel

@smallexample
@include vector_scal_opencl.texi
@end smallexample

@node Source of the kernel
@subsection Source of the kernel

@smallexample
@include vector_scal_opencl_codelet.texi
@end smallexample

@bye