starpu-max/doc/doxygen/chapters/basic_examples.doxy

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

/*! \page basicExamples Basic Examples

\section Hello_World_using_the_C_Extension Hello World using the C Extension

This section shows how to implement a simple program that submits a task
to StarPU using the StarPU C extension (\ref cExtensions). The complete example, and additional examples,
is available in the <c>gcc-plugin/examples</c> directory of the StarPU
distribution. A similar example showing how to directly use the StarPU's API is shown
in \ref Hello_World_using_StarPU_API.

GCC from version 4.5 permit to use the StarPU GCC plug-in (\ref cExtensions). This makes writing a task both simpler and less error-prone.
In a nutshell, all it takes is to declare a task, declare and define its
implementations (for CPU, OpenCL, and/or CUDA), and invoke the task like
a regular C function.  The example below defines <c>my_task</c> which
has a single implementation for CPU:

\include hello_pragma.c

The code can then be compiled and linked with GCC and the <c>-fplugin</c> flag:

\verbatim
$ gcc `pkg-config starpu-1.1 --cflags` hello-starpu.c \
    -fplugin=`pkg-config starpu-1.1 --variable=gccplugin` \
    `pkg-config starpu-1.1 --libs`
\endverbatim

The code can also be compiled without the StarPU C extension and will
behave as a normal sequential code.

\verbatim
$ gcc hello-starpu.c
hello-starpu.c:33:1: warning: ‘task’ attribute directive ignored [-Wattributes]
$ ./a.out
Hello, world! With x = 42
\endverbatim

As can be seen above, the C extensions allows programmers to
use StarPU tasks by essentially annotating ``regular'' C code.

\section Hello_World_using_StarPU_API Hello World using StarPU's API

This section shows how to achieve the same result as in the previous
section using StarPU's standard C API.

\subsection Required_Headers Required Headers

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

\code{.c}
#include <starpu.h>
\endcode

\subsection Defining_a_Codelet Defining a Codelet

\code{.c}
struct params
{
    int i;
    float f;
};
void cpu_func(void *buffers[], void *cl_arg)
{
    struct params *params = cl_arg;

    printf("Hello world (params = {%i, %f} )\n", params->i, params->f);
}

struct starpu_codelet cl =
{
    .where = STARPU_CPU,
    .cpu_funcs = { cpu_func, NULL },
    .nbuffers = 0
};
\endcode

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, x86, ...). For compatibility, make sure that the whole
structure is properly initialized to zero, either by using the
function starpu_codelet_init (@pxref{starpu_codelet_init}), or by letting the
compiler implicitly do it as examplified above.

The <c>nbuffers</c> 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 <c>cl_arg</c> field of the <c>starpu_task</c>
structure) does not count as a buffer since it is not managed by our data
management library, but just contain trivial parameters.

\internal
TODO need a crossref to the proper description of "where" see bla for more ...
\endinternal

We create a codelet which may only be executed on the CPUs. The <c>where</c>
field is a bitmask that defines where the codelet may be executed. Here, the
<c>STARPU_CPU</c> value means that only CPUs can execute this codelet
(@pxref{Codelets and Tasks} for more details on this field). Note that
the <c>where</c> field is optional, when unset its value is
automatically set based on the availability of the different
<c>XXX_funcs</c> fields.
When a CPU core executes a codelet, it calls the <c>cpu_func</c> function,
which \em must have the following prototype:

\code{.c}
void (*cpu_func)(void *buffers[], void *cl_arg);
\endcode

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) since there is none.
The second argument is a pointer to a buffer passed as an
argument to the codelet by the means of the <c>cl_arg</c> field of the
<c>starpu_task</c> structure.

\internal
TODO rewrite so that it is a little clearer ?
\endinternal

Be aware that this may be a pointer to a
\em 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. If synchronization is needed, data
has to be registered to StarPU, see \ref Vector_Scaling_Using_StarPU_API.

\subsection Submitting_a_Task Submitting a Task

\code{.c}
void callback_func(void *callback_arg)
{
    printf("Callback function (arg %x)\n", callback_arg);
}

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

    struct starpu_task *task = starpu_task_create();

    task->cl = &cl; /* Pointer to the codelet defined above */

    struct params params = { 1, 2.0f };
    task->cl_arg = &params;
    task->cl_arg_size = sizeof(params);

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

    /* starpu_task_submit will be a blocking call */
    task->synchronous = 1;

    /* submit the task to StarPU */
    starpu_task_submit(task);

    /* terminate StarPU */
    starpu_shutdown();

    return 0;
}
\endcode

Before submitting any tasks to StarPU, starpu_init() must be called. The
<c>NULL</c> argument specifies that we use default configuration. Tasks cannot
be submitted after the termination of StarPU by a call to
starpu_shutdown().

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

\internal
not really clear ;)
\endinternal

The <c>cl</c> 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 <c>cl_arg</c> field is a pointer to a buffer (of size
<c>cl_arg_size</c>) 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 as a StarPU
data. It must however be noted that StarPU avoids making copy whenever possible
and rather passes the pointer as such, so the buffer which is pointed at must
kept allocated until the task terminates, and if several tasks are submitted
with various parameters, each of them must be given a pointer to their own
buffer.

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

\code{.c}
void (*callback_function)(void *);
\endcode

If the <c>synchronous</c> field is non-zero, task submission will be
synchronous: the starpu_task_submit() function will not return until the
task was executed. Note that the starpu_shutdown() function does not
guarantee that asynchronous tasks have been executed before it returns,
starpu_task_wait_for_all() can be used to that effect, or data can be
unregistered (starpu_data_unregister()), which will
implicitly wait for all the tasks scheduled to work on it, unless explicitly
disabled thanks to starpu_data_set_default_sequential_consistency_flag() or
starpu_data_set_sequential_consistency_flag().

\subsection Execution_of_Hello_World Execution of Hello World

\verbatim
$ make hello_world
cc $(pkg-config --cflags starpu-1.1)  $(pkg-config --libs starpu-1.1) hello_world.c -o hello_world
$ ./hello_world
Hello world (params = {1, 2.000000} )
Callback function (arg 42)
\endverbatim

\section Vector_Scaling_Using_the_C_Extension Vector Scaling Using the C Extension

The previous example has shown how to submit tasks. In this section,
we show how StarPU tasks can manipulate data.

We will first show how to use the C language extensions provided by
the GCC plug-in (\ref cExtensions). The complete example, and
additional examples, is available in the <c>gcc-plugin/examples</c>
directory of the StarPU distribution. These extensions map directly
to StarPU's main concepts: tasks, task implementations for CPU,
OpenCL, or CUDA, and registered data buffers. The standard C version
that uses StarPU's standard C programming interface is given in the
next section (\ref Vector_Scaling_Using_StarPU_API).

First of all, the vector-scaling task and its simple CPU implementation
has to be defined:

\code{.c}
/* Declare the `vector_scal' task.  */
static void vector_scal (unsigned size, float vector[size],
                         float factor)
  __attribute__ ((task));

/* Define the standard CPU implementation.  */
static void
vector_scal (unsigned size, float vector[size], float factor)
{
  unsigned i;
  for (i = 0; i < size; i++)
    vector[i] *= factor;
}
\endcode

Next, the body of the program, which uses the task defined above, can be
implemented:

\include hello_pragma2.c

The <c>main</c> function above does several things:

<ul>
<li>
It initializes StarPU.
</li>
<li>
It allocates <c>vector</c> in the heap; it will automatically be freed
when its scope is left.  Alternatively, good old <c>malloc</c> and
<c>free</c> could have been used, but they are more error-prone and
require more typing.
</li>
<li>
It registers the memory pointed to by <c>vector</c>.  Eventually,
when OpenCL or CUDA task implementations are added, this will allow
StarPU to transfer that memory region between GPUs and the main memory.
Removing this <c>pragma</c> is an error.
</li>
<li>
It invokes the <c>vector_scal</c> task.  The invocation looks the same
as a standard C function call.  However, it is an asynchronous
invocation, meaning that the actual call is performed in parallel with
the caller's continuation.
</li>
<li>
It waits for the termination of the <c>vector_scal</c>
asynchronous call.
</li>
<li>
Finally, StarPU is shut down.
</li>
</ul>

The program can be compiled and linked with GCC and the <c>-fplugin</c>
flag:

\verbatim
$ gcc `pkg-config starpu-1.1 --cflags` vector_scal.c \
    -fplugin=`pkg-config starpu-1.1 --variable=gccplugin` \
    `pkg-config starpu-1.1 --libs`
\endverbatim

And voilà!

\subsection Adding_an_OpenCL_Task_Implementation Adding an OpenCL Task Implementation

Now, this is all fine and great, but you certainly want to take
advantage of these newfangled GPUs that your lab just bought, don't you?

So, let's add an OpenCL implementation of the <c>vector_scal</c> task.
We assume that the OpenCL kernel is available in a file,
<c>vector_scal_opencl_kernel.cl</c>, not shown here.  The OpenCL task
implementation is similar to that used with the standard C API
(\ref Definition_of_the_OpenCL_Kernel).  It is declared and defined
in our C file like this:

\code{.c}
/* The OpenCL programs, loaded from 'main' (see below). */
static struct starpu_opencl_program cl_programs;

static void vector_scal_opencl (unsigned size, float vector[size],
                                float factor)
  __attribute__ ((task_implementation ("opencl", vector_scal)));

static void
vector_scal_opencl (unsigned size, float vector[size], float factor)
{
  int id, devid, err;
  cl_kernel kernel;
  cl_command_queue queue;
  cl_event event;

  /* VECTOR is GPU memory pointer, not a main memory pointer. */
  cl_mem val = (cl_mem) vector;

  id = starpu_worker_get_id ();
  devid = starpu_worker_get_devid (id);

  /* Prepare to invoke the kernel.  In the future, this will be largely automated.  */
  err = starpu_opencl_load_kernel (&kernel, &queue, &cl_programs,
                                   "vector_mult_opencl", devid);
  if (err != CL_SUCCESS)
    STARPU_OPENCL_REPORT_ERROR (err);

  err = clSetKernelArg (kernel, 0, sizeof (size), &size);
  err |= clSetKernelArg (kernel, 1, sizeof (val), &val);
  err |= clSetKernelArg (kernel, 2, sizeof (factor), &factor);
  if (err)
    STARPU_OPENCL_REPORT_ERROR (err);

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

  clFinish (queue);
  starpu_opencl_collect_stats (event);
  clReleaseEvent (event);

  /* Done with KERNEL. */
  starpu_opencl_release_kernel (kernel);
}
\endcode

The OpenCL kernel itself must be loaded from <c>main</c>, sometime after
the <c>initialize</c> pragma:

\code{.c}
starpu_opencl_load_opencl_from_file ("vector_scal_opencl_kernel.cl",
                                       &cl_programs, "");
\endcode

And that's it.  The <c>vector_scal</c> task now has an additional
implementation, for OpenCL, which StarPU's scheduler may choose to use
at run-time.  Unfortunately, the <c>vector_scal_opencl</c> above still
has to go through the common OpenCL boilerplate; in the future,
additional extensions will automate most of it.

\subsection Adding_a_CUDA_Task_Implementation Adding a CUDA Task Implementation

Adding a CUDA implementation of the task is very similar, except that
the implementation itself is typically written in CUDA, and compiled
with <c>nvcc</c>.  Thus, the C file only needs to contain an external
declaration for the task implementation:

\code{.c}
extern void vector_scal_cuda (unsigned size, float vector[size],
                              float factor)
  __attribute__ ((task_implementation ("cuda", vector_scal)));
\endcode

The actual implementation of the CUDA task goes into a separate
compilation unit, in a <c>.cu</c> file.  It is very close to the
implementation when using StarPU's standard C API (\ref Definition_of_the_CUDA_Kernel).

\code{.c}
/* CUDA implementation of the `vector_scal' task, to be compiled with `nvcc'. */

#include <starpu.h>
#include <stdlib.h>

static __global__ void
vector_mult_cuda (unsigned n, float *val, float factor)
{
  unsigned i = blockIdx.x * blockDim.x + threadIdx.x;

  if (i < n)
    val[i] *= factor;
}

/* Definition of the task implementation declared in the C file. */
extern "C" void
vector_scal_cuda (size_t size, float vector[], float factor)
{
  unsigned threads_per_block = 64;
  unsigned nblocks = (size + threads_per_block - 1) / threads_per_block;

  vector_mult_cuda <<< nblocks, threads_per_block, 0,
    starpu_cuda_get_local_stream () >>> (size, vector, factor);

  cudaStreamSynchronize (starpu_cuda_get_local_stream ());
}
\endcode

The complete source code, in the <c>gcc-plugin/examples/vector_scal</c>
directory of the StarPU distribution, also shows how an SSE-specialized
CPU task implementation can be added.

For more details on the C extensions provided by StarPU's GCC plug-in,
\ref cExtensions.

\section Vector_Scaling_Using_StarPU_API Vector Scaling Using StarPU's API

This section shows how to achieve the same result as explained in the
previous section using StarPU's standard C API.

The full source code for
this example is given in @ref{Full source code for the 'Scaling a
Vector' example}.

\subsection Source_Code_of_Vector_Scaling 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 <c>starpu_*_data_register</c>
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</b>. There are different predefined interfaces available in StarPU:
here we will consider the <b>vector interface</b>.

The following lines show how to declare an array of <c>NX</c> elements of type
<c>float</c> using the vector interface:

\code{.c}
float vector[NX];

starpu_data_handle_t vector_handle;
starpu_vector_data_register(&vector_handle, 0, (uintptr_t)vector, NX,
                            sizeof(vector[0]));
\endcode

The first argument, called the <b>data handle</b>, 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 originally resides. Here it is 0 since the <c>vector array</c> is in
the main memory. Then comes the pointer <c>vector</c> where the data can be found in main memory,
the number of elements in the vector and the size of each element.
The following shows how to construct a StarPU task that will manipulate the
vector and a constant factor.

\code{.c}
float factor = 3.14;
struct starpu_task *task = starpu_task_create();

task->cl = &cl;                      /* Pointer to the codelet defined below */
task->handles[0] = vector_handle;    /* First parameter of the codelet */
task->cl_arg = &factor;
task->cl_arg_size = sizeof(factor);
task->synchronous = 1;

starpu_task_submit(task);
\endcode

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

The definition of the codelet can be written as follows:

\code{.c}
void scal_cpu_func(void *buffers[], void *cl_arg)
{
    unsigned i;
    float *factor = cl_arg;

    /* length of the vector */
    unsigned n = STARPU_VECTOR_GET_NX(buffers[0]);
    /* CPU copy of the vector pointer */
    float *val = (float *)STARPU_VECTOR_GET_PTR(buffers[0]);

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

struct starpu_codelet cl =
{
    .cpu_funcs = { scal_cpu_func, NULL },
    .nbuffers = 1,
    .modes = { STARPU_RW }
};
\endcode

The first argument is an array that gives
a description of all the buffers passed in the <c>task->handles</c> array. The
size of this array is given by the <c>nbuffers</c> field of the codelet
structure. For the sake of genericity, this array contains pointers to the
different interfaces describing each buffer.  In the case of the <b>vector
interface</b>, the location of the vector (resp. its length) is accessible in the
\<c>ptr<c> (resp. <c>nx</c>) 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.

The second argument of the <c>scal_cpu_func</c> function contains a pointer to the
parameters of the codelet (given in <c>task->cl_arg</c>), so that we read the
constant factor from this pointer.

\subsection Execution_of_Vector_Scaling Execution of Vector Scaling

\verbatim
$ make vector_scal
cc $(pkg-config --cflags starpu-1.1)  $(pkg-config --libs starpu-1.1)  vector_scal.c   -o vector_scal
$ ./vector_scal
0.000000 3.000000 6.000000 9.000000 12.000000
\endverbatim

\section Vector_Scaling_on_an_Hybrid_CPU_GPU_Machine 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.

\subsection Definition_of_the_CUDA_Kernel Definition of the CUDA Kernel

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. It must be noted
that the vector pointer returned by STARPU_VECTOR_GET_PTR is here a pointer in GPU
memory, so that it can be passed as such to the <c>vector_mult_cuda</c> kernel
call.

\code{.c}
#include <starpu.h>

static __global__ void vector_mult_cuda(unsigned n, float *val,
                                        float factor)
{
    unsigned i =  blockIdx.x*blockDim.x + threadIdx.x;
    if (i < n)
        val[i] *= factor;
}

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

    /* length of the vector */
    unsigned n = STARPU_VECTOR_GET_NX(buffers[0]);
    /* CUDA copy of the vector pointer */
    float *val = (float *)STARPU_VECTOR_GET_PTR(buffers[0]);
    unsigned threads_per_block = 64;
    unsigned nblocks = (n + threads_per_block-1) / threads_per_block;

@i{    vector_mult_cuda<<<nblocks,threads_per_block, 0, starpu_cuda_get_local_stream()>>>}
@i{                    (n, val, *factor);}

@i{    cudaStreamSynchronize(starpu_cuda_get_local_stream());}
}
\endcode

\subsection Definition_of_the_OpenCL_Kernel Definition of the OpenCL Kernel

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

\code{.c}
__kernel void vector_mult_opencl(int nx, __global float* val, float factor)
{
        const int i = get_global_id(0);
        if (i < nx) {
                val[i] *= factor;
        }
}
\endcode

Contrary to CUDA and CPU, <c>STARPU_VECTOR_GET_DEV_HANDLE</c> has to be used,
which returns a <c>cl_mem</c> (which is not a device pointer, but an OpenCL
handle), which can be passed as such to the OpenCL kernel. The difference is
important when using partitioning, see @ref{Partitioning Data}.

\code{.c}
#include <starpu.h>

extern struct starpu_opencl_program programs;

void scal_opencl_func(void *buffers[], void *_args)
{
    float *factor = _args;
    int id, devid, err;     /* OpenCL specific code */
    cl_kernel kernel;       /* OpenCL specific code */
    cl_command_queue queue; /* OpenCL specific code */
    cl_event event;         /* OpenCL specific code */

    /* length of the vector */
    unsigned n = STARPU_VECTOR_GET_NX(buffers[0]);
    /* OpenCL copy of the vector pointer */
    cl_mem val = (cl_mem) STARPU_VECTOR_GET_DEV_HANDLE(buffers[0]);

    { /* OpenCL specific code */
        id = starpu_worker_get_id();
        devid = starpu_worker_get_devid(id);

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

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

    {  /* OpenCL specific code */
        size_t global=n;
        size_t local=1;
        err = clEnqueueNDRangeKernel(queue, kernel, 1, NULL, &global, &local, 0, NULL, &event);
        if (err != CL_SUCCESS) STARPU_OPENCL_REPORT_ERROR(err);
    }

    {  /* OpenCL specific code */
        clFinish(queue);
        starpu_opencl_collect_stats(event);
        clReleaseEvent(event);

        starpu_opencl_release_kernel(kernel);
    }
}
\endcode


\subsection Definition_of_the_Main_Code 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 that the fields
<c>cuda_funcs</c> and <c>opencl_funcs</c> of the codelet are set to
define the pointers to the CUDA and OpenCL implementations of the
task.

\code{.c}
#include <starpu.h>

#define NX 2048

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

/* Definition of the codelet */
static struct starpu_codelet cl =
{
    .cuda_funcs = { scal_cuda_func, NULL },
    .cpu_funcs = { scal_cpu_func, NULL },
    .opencl_funcs = { scal_opencl_func, NULL },
    .nbuffers = 1,
    .modes = { STARPU_RW }
}

#ifdef STARPU_USE_OPENCL
/* The compiled version of the OpenCL program */
struct starpu_opencl_program programs;
#endif

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

    starpu_init(NULL);                            /* Initialising StarPU */

#ifdef STARPU_USE_OPENCL
    starpu_opencl_load_opencl_from_file(
            "examples/basic_examples/vector_scal_opencl_codelet.cl",
            &programs, NULL);
#endif

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

    /* Registering data within StarPU */
    starpu_vector_data_register(&vector_handle, 0, (uintptr_t)vector,
                                NX, sizeof(vector[0]));

    /* Definition of the task */
    task = starpu_task_create();
    task->cl = &cl;
    task->handles[0] = vector_handle;
    task->cl_arg = &factor;
    task->cl_arg_size = sizeof(factor);

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

    /* Waiting for its termination */
    starpu_task_wait_for_all();

    /* Update the vector in RAM */
    starpu_data_acquire(vector_handle, STARPU_R);

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

    /* Release the RAM view of the data before unregistering it and shutting down StarPU */
    starpu_data_release(vector_handle);
    starpu_data_unregister(vector_handle);
    starpu_shutdown();

    return 0;
}
\endcode

\subsection Execution_of_Hybrid_Vector_Scaling 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. Note that the source
file of the OpenCL kernel does not need to be compiled now, it will
be compiled at run-time when calling the function
starpu_opencl_load_opencl_from_file() (@pxref{starpu_opencl_load_opencl_from_file}).

\verbatim
CFLAGS  += $(shell pkg-config --cflags starpu-1.1)
LDFLAGS += $(shell pkg-config --libs starpu-1.1)
CC       = gcc

vector_scal: vector_scal.o vector_scal_cpu.o vector_scal_cuda.o vector_scal_opencl.o

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

clean:
       rm -f vector_scal *.o
\endverbatim

\verbatim
$ make
\endverbatim

and to execute it, with the default configuration:

\verbatim
$ ./vector_scal
0.000000 3.000000 6.000000 9.000000 12.000000
\endverbatim

or for example, by disabling CPU devices:

\verbatim
$ STARPU_NCPU=0 ./vector_scal
0.000000 3.000000 6.000000 9.000000 12.000000
\endverbatim

or by disabling CUDA devices (which may permit to enable the use of OpenCL,
see \ref Enabling_OpenCL) :

\verbatim
$ STARPU_NCUDA=0 ./vector_scal
0.000000 3.000000 6.000000 9.000000 12.000000
\endverbatim

*/