@c -*-texinfo-*-

@c This file is part of the StarPU Handbook.
@c Copyright (C) 2011, 2012 Institut National de Recherche en Informatique et Automatique
@c See the file starpu.texi for copying conditions.

@cindex C extensions
@cindex GCC plug-in

When GCC plug-in support is available, StarPU builds a plug-in for the
GNU Compiler Collection (GCC), which defines extensions to languages of
the C family (C, C++, Objective-C) that make it easier to write StarPU
code@footnote{This feature is only available for GCC 4.5 and later; it
is known to work with GCC 4.5, 4.6, and 4.7.  You
may need to install a specific @code{-dev} package of your distro, such
as @code{gcc-4.6-plugin-dev} on Debian and derivatives.  In addition,
the plug-in's test suite is only run when
@url{http://www.gnu.org/software/guile/, GNU@tie{}Guile} is found at
@code{configure}-time.  Building the GCC plug-in
can be disabled by configuring with @code{--disable-gcc-extensions}.}.

Those extensions include syntactic sugar for defining
tasks and their implementations, invoking a task, and manipulating data
buffers.  Use of these extensions can be made conditional on the
availability of the plug-in, leading to valid C sequential code when the
plug-in is not used (@pxref{Conditional Extensions}).

When StarPU has been installed with its GCC plug-in, programs that use
these extensions can be compiled this way:

@example
$ gcc -c -fplugin=`pkg-config starpu-1.0 --variable=gccplugin` foo.c
@end example

@noindent
When the plug-in is not available, the above @command{pkg-config}
command returns the empty string.

In addition, the @code{-fplugin-arg-starpu-verbose} flag can be used to
obtain feedback from the compiler as it analyzes the C extensions used
in source files.

This section describes the C extensions implemented by StarPU's GCC
plug-in.  It does not require detailed knowledge of the StarPU library.

Note: as of StarPU @value{VERSION}, this is still an area under
development and subject to change.

@menu
* Defining Tasks::              Defining StarPU tasks
* Synchronization and Other Pragmas:: Synchronization, and more.
* Registered Data Buffers::     Manipulating data buffers
* Conditional Extensions::      Using C extensions only when available
@end menu

@node Defining Tasks
@section Defining Tasks

@cindex task
@cindex task implementation

The StarPU GCC plug-in views @dfn{tasks} as ``extended'' C functions:

@enumerate
@item
tasks may have several implementations---e.g., one for CPUs, one written
in OpenCL, one written in CUDA;
@item
tasks may have several implementations of the same target---e.g.,
several CPU implementations;
@item
when a task is invoked, it may run in parallel, and StarPU is free to
choose any of its implementations.
@end enumerate

Tasks and their implementations must be @emph{declared}.  These
declarations are annotated with @dfn{attributes} (@pxref{Attribute
Syntax, attributes in GNU C,, gcc, Using the GNU Compiler Collection
(GCC)}): the declaration of a task is a regular C function declaration
with an additional @code{task} attribute, and task implementations are
declared with a @code{task_implementation} attribute.

The following function attributes are provided:

@table @code

@item task
@cindex @code{task} attribute
Declare the given function as a StarPU task.  Its return type must be
@code{void}.  When a function declared as @code{task} has a user-defined
body, that body is interpreted as the @dfn{implicit definition of the
task's CPU implementation} (see example below).  In all cases, the
actual definition of a task's body is automatically generated by the
compiler.

Under the hood, declaring a task leads to the declaration of the
corresponding @code{codelet} (@pxref{Codelet and Tasks}).  If one or
more task implementations are declared in the same compilation unit,
then the codelet and the function itself are also defined; they inherit
the scope of the task.

Scalar arguments to the task are passed by value and copied to the
target device if need be---technically, they are passed as the
@code{cl_arg} buffer (@pxref{Codelets and Tasks, @code{cl_arg}}).

@cindex @code{output} type attribute
Pointer arguments are assumed to be registered data buffers---the
@code{buffers} argument of a task (@pxref{Codelets and Tasks,
@code{buffers}}); @code{const}-qualified pointer arguments are viewed as
read-only buffers (@code{STARPU_R}), and non-@code{const}-qualified
buffers are assumed to be used read-write (@code{STARPU_RW}).  In
addition, the @code{output} type attribute can be as a type qualifier
for output pointer or array parameters (@code{STARPU_W}).

@item task_implementation (@var{target}, @var{task})
@cindex @code{task_implementation} attribute
Declare the given function as an implementation of @var{task} to run on
@var{target}.  @var{target} must be a string, currently one of
@code{"cpu"}, @code{"opencl"}, or @code{"cuda"}.
@c FIXME: Update when OpenCL support is ready.

@end table

Here is an example:

@cartouche
@smallexample
#define __output  __attribute__ ((output))

static void matmul (const float *A, const float *B,
                    __output float *C,
                    unsigned nx, unsigned ny, unsigned nz)
  __attribute__ ((task));

static void matmul_cpu (const float *A, const float *B,
                        __output float *C,
                        unsigned nx, unsigned ny, unsigned nz)
  __attribute__ ((task_implementation ("cpu", matmul)));


static void
matmul_cpu (const float *A, const float *B, __output float *C,
            unsigned nx, unsigned ny, unsigned nz)
@{
  unsigned i, j, k;

  for (j = 0; j < ny; j++)
    for (i = 0; i < nx; i++)
      @{
        for (k = 0; k < nz; k++)
          C[j * nx + i] += A[j * nz + k] * B[k * nx + i];
      @}
@}
@end smallexample
@end cartouche

@noindent
A @code{matmult} task is defined; it has only one implementation,
@code{matmult_cpu}, which runs on the CPU.  Variables @var{A} and
@var{B} are input buffers, whereas @var{C} is considered an input/output
buffer.

@cindex implicit task CPU implementation
For convenience, when a function declared with the @code{task} attribute
has a user-defined body, that body is assumed to be that of the CPU
implementation of a task, which we call an @dfn{implicit task CPU
implementation}.  Thus, the above snippet can be simplified like this:

@cartouche
@smallexample
#define __output  __attribute__ ((output))

static void matmul (const float *A, const float *B,
                    __output float *C,
                    unsigned nx, unsigned ny, unsigned nz)
  __attribute__ ((task));

/* Implicit definition of the CPU implementation of the
   `matmul' task.  */
static void
matmul (const float *A, const float *B, __output float *C,
        unsigned nx, unsigned ny, unsigned nz)
@{
  unsigned i, j, k;

  for (j = 0; j < ny; j++)
    for (i = 0; i < nx; i++)
      @{
        for (k = 0; k < nz; k++)
          C[j * nx + i] += A[j * nz + k] * B[k * nx + i];
      @}
@}
@end smallexample
@end cartouche

@noindent
Use of implicit CPU task implementations as above has the advantage that
the code is valid sequential code when StarPU's GCC plug-in is not used
(@pxref{Conditional Extensions}).

CUDA and OpenCL implementations can be declared in a similar way:

@cartouche
@smallexample
static void matmul_cuda (const float *A, const float *B, float *C,
                         unsigned nx, unsigned ny, unsigned nz)
  __attribute__ ((task_implementation ("cuda", matmul)));

static void matmul_opencl (const float *A, const float *B, float *C,
                           unsigned nx, unsigned ny, unsigned nz)
  __attribute__ ((task_implementation ("opencl", matmul)));
@end smallexample
@end cartouche

@noindent
The CUDA and OpenCL implementations typically either invoke a kernel
written in CUDA or OpenCL (for similar code, @pxref{CUDA Kernel}, and
@pxref{OpenCL Kernel}), or call a library function that uses CUDA or
OpenCL under the hood, such as CUBLAS functions:

@cartouche
@smallexample
static void
matmul_cuda (const float *A, const float *B, float *C,
             unsigned nx, unsigned ny, unsigned nz)
@{
  cublasSgemm ('n', 'n', nx, ny, nz,
               1.0f, A, 0, B, 0,
               0.0f, C, 0);
  cudaStreamSynchronize (starpu_cuda_get_local_stream ());
@}
@end smallexample
@end cartouche

A task can be invoked like a regular C function:

@cartouche
@smallexample
matmul (&A[i * zdim * bydim + k * bzdim * bydim],
        &B[k * xdim * bzdim + j * bxdim * bzdim],
        &C[i * xdim * bydim + j * bxdim * bydim],
        bxdim, bydim, bzdim);
@end smallexample
@end cartouche

@noindent
This leads to an @dfn{asynchronous invocation}, whereby @code{matmult}'s
implementation may run in parallel with the continuation of the caller.

The next section describes how memory buffers must be handled in
StarPU-GCC code.  For a complete example, see the
@code{gcc-plugin/examples} directory of the source distribution, and
@ref{Vector Scaling Using the C Extension, the vector-scaling
example}.


@node Synchronization and Other Pragmas
@section Initialization, Termination, and Synchronization

The following pragmas allow user code to control StarPU's life time and
to synchronize with tasks.

@table @code

@item #pragma starpu initialize
Initialize StarPU.  This call is compulsory and is @emph{never} added
implicitly.  One of the reasons this has to be done explicitly is that
it provides greater control to user code over its resource usage.

@item #pragma starpu shutdown
Shut down StarPU, giving it an opportunity to write profiling info to a
file on disk, for instance (@pxref{Off-line, off-line performance
feedback}).

@item #pragma starpu wait
Wait for all task invocations to complete, as with
@code{starpu_wait_for_all} (@pxref{Codelets and Tasks,
starpu_wait_for_all}).

@end table

@node Registered Data Buffers
@section Registered Data Buffers

Data buffers such as matrices and vectors that are to be passed to tasks
must be @dfn{registered}.  Registration allows StarPU to handle data
transfers among devices---e.g., transferring an input buffer from the
CPU's main memory to a task scheduled to run a GPU (@pxref{StarPU Data
Management Library}).

The following pragmas are provided:

@table @code

@item #pragma starpu register @var{ptr} [@var{size}]
Register @var{ptr} as a @var{size}-element buffer.  When @var{ptr} has
an array type whose size is known, @var{size} may be omitted.

@item #pragma starpu unregister @var{ptr}
Unregister the previously-registered memory area pointed to by
@var{ptr}.  As a side-effect, @var{ptr} points to a valid copy in main
memory.

@item #pragma starpu acquire @var{ptr}
Acquire in main memory an up-to-date copy of the previously-registered
memory area pointed to by @var{ptr}, for read-write access.

@item #pragma starpu release @var{ptr}
Release the previously-register memory area pointed to by @var{ptr},
making it available to the tasks.

@end table

Additionally, the @code{heap_allocated} variable attribute offers a
simple way to allocate storage for arrays on the heap:

@table @code

@item heap_allocated
@cindex @code{heap_allocated} attribute
This attributes applies to local variables with an array type.  Its
effect is to automatically allocate the array's storage on
the heap, using @code{starpu_malloc} under the hood (@pxref{Basic Data
Library API, starpu_malloc}).  The heap-allocated array is automatically
freed when the variable's scope is left, as with
automatic variables.

@end table

@noindent
The following example illustrates use of the @code{heap_allocated}
attribute:

@example
extern void cholesky(unsigned nblocks, unsigned size,
                    float mat[nblocks][nblocks][size])
  __attribute__ ((task));

int
main (int argc, char *argv[])
@{
#pragma starpu initialize

  /* ... */

  int nblocks, size;
  parse_args (&nblocks, &size);

  /* Allocate an array of the required size on the heap,
     and register it.  */

  @{
    float matrix[nblocks][nblocks][size]
      __attribute__ ((heap_allocated));

#pragma starpu register matrix

    cholesky (nblocks, size, matrix);

#pragma starpu wait
#pragma starpu unregister matrix

  @}   /* MATRIX is automatically freed here.  */

#pragma starpu shutdown

  return EXIT_SUCCESS;
@}
@end example

@node Conditional Extensions
@section Using C Extensions Conditionally

The C extensions described in this chapter are only available when GCC
and its StarPU plug-in are in use.  Yet, it is possible to make use of
these extensions when they are available---leading to hybrid CPU/GPU
code---and discard them when they are not available---leading to valid
sequential code.

To that end, the GCC plug-in defines a C preprocessor macro when it is
being used:

@defmac STARPU_GCC_PLUGIN
Defined for code being compiled with the StarPU GCC plug-in.  When
defined, this macro expands to an integer denoting the version of the
supported C extensions.
@end defmac

The code below illustrates how to define a task and its implementations
in a way that allows it to be compiled without the GCC plug-in:

@smallexample
/* This program is valid, whether or not StarPU's GCC plug-in
   is being used.  */

#include <stdlib.h>

/* The attribute below is ignored when GCC is not used.  */
static void matmul (const float *A, const float *B, float * C,
                    unsigned nx, unsigned ny, unsigned nz)
  __attribute__ ((task));

static void
matmul (const float *A, const float *B, float * C,
        unsigned nx, unsigned ny, unsigned nz)
@{
  /* Code of the CPU kernel here...  */
@}

#ifdef STARPU_GCC_PLUGIN
/* Optional OpenCL task implementation.  */

static void matmul_opencl (const float *A, const float *B, float * C,
                           unsigned nx, unsigned ny, unsigned nz)
  __attribute__ ((task_implementation ("opencl", matmul)));

static void
matmul_opencl (const float *A, const float *B, float * C,
               unsigned nx, unsigned ny, unsigned nz)
@{
  /* Code that invokes the OpenCL kernel here...  */
@}
#endif

int
main (int argc, char *argv[])
@{
  /* The pragmas below are simply ignored when StarPU-GCC
     is not used.  */
#pragma starpu initialize

  float A[123][42][7], B[123][42][7], C[123][42][7];

#pragma starpu register A
#pragma starpu register B
#pragma starpu register C

  /* When StarPU-GCC is used, the call below is asynchronous;
     otherwise, it is synchronous.  */
  matmul ((float *) A, (float *) B, (float *) C, 123, 42, 7);

#pragma starpu wait
#pragma starpu shutdown

  return EXIT_SUCCESS;
@}
@end smallexample

@noindent
The above program is a valid StarPU program when StarPU's GCC plug-in is
used; it is also a valid sequential program when the plug-in is not
used.

Note that attributes such as @code{task} as well as @code{starpu}
pragmas are simply ignored by GCC when the StarPU plug-in is not loaded.
However, @command{gcc -Wall} emits a warning for unknown attributes and
pragmas, which can be inconvenient.  In addition, other compilers may be
unable to parse the attribute syntax@footnote{In practice, Clang and
several proprietary compilers implement attributes.}, so you may want to
wrap attributes in macros like this:

@smallexample
/* Use the `task' attribute only when StarPU's GCC plug-in
   is available.   */
#ifdef STARPU_GCC_PLUGIN
# define __task  __attribute__ ((task))
#else
# define __task
#endif

static void matmul (const float *A, const float *B, float *C,
                    unsigned nx, unsigned ny, unsigned nz) __task;
@end smallexample


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