starpu-max/examples/cg/cg_kernels.c

/* StarPU --- Runtime system for heterogeneous multicore architectures.
 *
 * Copyright (C) 2010, 2012-2016  Université de Bordeaux
 *
 * StarPU is free software; you can redistribute it and/or modify
 * it under the terms of the GNU Lesser General Public License as published by
 * the Free Software Foundation; either version 2.1 of the License, or (at
 * your option) any later version.
 *
 * StarPU is distributed in the hope that it will be useful, but
 * WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
 *
 * See the GNU Lesser General Public License in COPYING.LGPL for more details.
 */

/*
 * Standard BLAS kernels used by CG
 */

#include "cg.h"
#include <math.h>
#include <limits.h>

#ifdef STARPU_USE_CUDA
#include <starpu_cublas_v2.h>
static const TYPE p1 = 1.0;
static const TYPE m1 = -1.0;
#endif

#if 0
static void print_vector_from_descr(unsigned nx, TYPE *v)
{
	unsigned i;
	for (i = 0; i < nx; i++)
	{
		fprintf(stderr, "%2.2e ", v[i]);
	}
	fprintf(stderr, "\n");
}


static void print_matrix_from_descr(unsigned nx, unsigned ny, unsigned ld, TYPE *mat)
{
	unsigned i, j;
	for (j = 0; j < nx; j++)
	{
		for (i = 0; i < ny; i++)
		{
			fprintf(stderr, "%2.2e ", mat[j+i*ld]);
		}
		fprintf(stderr, "\n");
	}
}
#endif

static int can_execute(unsigned workerid, struct starpu_task *task, unsigned nimpl)
{
	enum starpu_worker_archtype type = starpu_worker_get_type(workerid);
	if (type == STARPU_CPU_WORKER || type == STARPU_OPENCL_WORKER || type == STARPU_MIC_WORKER || type == STARPU_SCC_WORKER)
		return 1;

#ifdef STARPU_USE_CUDA
#ifdef STARPU_SIMGRID
	/* We don't know, let's assume it can */
	return 1;
#else
	/* Cuda device */
	const struct cudaDeviceProp *props;
	props = starpu_cuda_get_device_properties(workerid);
	if (props->major >= 2 || props->minor >= 3)
		/* At least compute capability 1.3, supports doubles */
		return 1;
#endif
#endif
	/* Old card, does not support doubles */
	return 0;
}

/*
 *	Reduction accumulation methods
 */

#ifdef STARPU_USE_CUDA
static void accumulate_variable_cuda(void *descr[], void *cl_arg)
{
	TYPE *v_dst = (TYPE *)STARPU_VARIABLE_GET_PTR(descr[0]);
	TYPE *v_src = (TYPE *)STARPU_VARIABLE_GET_PTR(descr[1]);
 
	cublasStatus_t status = cublasaxpy(starpu_cublas_get_local_handle(), 1, &p1, v_src, 1, v_dst, 1);
	if (status != CUBLAS_STATUS_SUCCESS)
		STARPU_CUBLAS_REPORT_ERROR(status);
}
#endif

void accumulate_variable_cpu(void *descr[], void *cl_arg)
{
	TYPE *v_dst = (TYPE *)STARPU_VARIABLE_GET_PTR(descr[0]);
	TYPE *v_src = (TYPE *)STARPU_VARIABLE_GET_PTR(descr[1]);
 
	*v_dst = *v_dst + *v_src;
}

static struct starpu_perfmodel accumulate_variable_model =
{
	.type = STARPU_HISTORY_BASED,
	.symbol = "accumulate_variable"
};

struct starpu_codelet accumulate_variable_cl =
{
	.can_execute = can_execute,
	.cpu_funcs = {accumulate_variable_cpu},
	.cpu_funcs_name = {"accumulate_variable_cpu"},
#ifdef STARPU_USE_CUDA
	.cuda_funcs = {accumulate_variable_cuda},
	.cuda_flags = {STARPU_CUDA_ASYNC},
#endif
	.modes = {STARPU_RW, STARPU_R},
	.nbuffers = 2,
	.model = &accumulate_variable_model
};

#ifdef STARPU_USE_CUDA
static void accumulate_vector_cuda(void *descr[], void *cl_arg)
{
	TYPE *v_dst = (TYPE *)STARPU_VECTOR_GET_PTR(descr[0]);
	TYPE *v_src = (TYPE *)STARPU_VECTOR_GET_PTR(descr[1]);
	unsigned n = STARPU_VECTOR_GET_NX(descr[0]);

	cublasStatus_t status = cublasaxpy(starpu_cublas_get_local_handle(), n, &p1, v_src, 1, v_dst, 1);
	if (status != CUBLAS_STATUS_SUCCESS)
		STARPU_CUBLAS_REPORT_ERROR(status);
}
#endif

void accumulate_vector_cpu(void *descr[], void *cl_arg)
{
	TYPE *v_dst = (TYPE *)STARPU_VECTOR_GET_PTR(descr[0]);
	TYPE *v_src = (TYPE *)STARPU_VECTOR_GET_PTR(descr[1]);
	unsigned n = STARPU_VECTOR_GET_NX(descr[0]);
 
	AXPY(n, (TYPE)1.0, v_src, 1, v_dst, 1);
}

static struct starpu_perfmodel accumulate_vector_model =
{
	.type = STARPU_HISTORY_BASED,
	.symbol = "accumulate_vector"
};

struct starpu_codelet accumulate_vector_cl =
{
	.can_execute = can_execute,
	.cpu_funcs = {accumulate_vector_cpu},
	.cpu_funcs_name = {"accumulate_vector_cpu"},
#ifdef STARPU_USE_CUDA
	.cuda_funcs = {accumulate_vector_cuda},
	.cuda_flags = {STARPU_CUDA_ASYNC},
#endif
	.modes = {STARPU_RW, STARPU_R},
	.nbuffers = 2,
	.model = &accumulate_vector_model
};

/*
 *	Reduction initialization methods
 */

#ifdef STARPU_USE_CUDA
extern void zero_vector(TYPE *x, unsigned nelems);

static void bzero_variable_cuda(void *descr[], void *cl_arg)
{
	TYPE *v = (TYPE *)STARPU_VARIABLE_GET_PTR(descr[0]);
	size_t size = STARPU_VARIABLE_GET_ELEMSIZE(descr[0]);

	cudaMemsetAsync(v, 0, size, starpu_cuda_get_local_stream());
}
#endif

void bzero_variable_cpu(void *descr[], void *cl_arg)
{
	TYPE *v = (TYPE *)STARPU_VARIABLE_GET_PTR(descr[0]);
	*v = (TYPE)0.0;
}

static struct starpu_perfmodel bzero_variable_model =
{
	.type = STARPU_HISTORY_BASED,
	.symbol = "bzero_variable"
};

struct starpu_codelet bzero_variable_cl =
{
	.can_execute = can_execute,
	.cpu_funcs = {bzero_variable_cpu},
	.cpu_funcs_name = {"bzero_variable_cpu"},
#ifdef STARPU_USE_CUDA
	.cuda_funcs = {bzero_variable_cuda},
	.cuda_flags = {STARPU_CUDA_ASYNC},
#endif
	.modes = {STARPU_W},
	.nbuffers = 1,
	.model = &bzero_variable_model
};

#ifdef STARPU_USE_CUDA
static void bzero_vector_cuda(void *descr[], void *cl_arg)
{
	TYPE *v = (TYPE *)STARPU_VECTOR_GET_PTR(descr[0]);
	unsigned n = STARPU_VECTOR_GET_NX(descr[0]);
	size_t elemsize = STARPU_VECTOR_GET_ELEMSIZE(descr[0]);

	cudaMemsetAsync(v, 0, n * elemsize, starpu_cuda_get_local_stream());
}
#endif

void bzero_vector_cpu(void *descr[], void *cl_arg)
{
	TYPE *v = (TYPE *)STARPU_VECTOR_GET_PTR(descr[0]);
	unsigned n = STARPU_VECTOR_GET_NX(descr[0]);
 
	memset(v, 0, n*sizeof(TYPE));
}

static struct starpu_perfmodel bzero_vector_model =
{
	.type = STARPU_HISTORY_BASED,
	.symbol = "bzero_vector"
};

struct starpu_codelet bzero_vector_cl =
{
	.can_execute = can_execute,
	.cpu_funcs = {bzero_vector_cpu},
	.cpu_funcs_name = {"bzero_vector_cpu"},
#ifdef STARPU_USE_CUDA
	.cuda_funcs = {bzero_vector_cuda},
	.cuda_flags = {STARPU_CUDA_ASYNC},
#endif
	.modes = {STARPU_W},
	.nbuffers = 1,
	.model = &bzero_vector_model
};

/*
 *	DOT kernel : s = dot(v1, v2)
 */

#ifdef STARPU_USE_CUDA
static void dot_kernel_cuda(void *descr[], void *cl_arg)
{
	TYPE *dot = (TYPE *)STARPU_VARIABLE_GET_PTR(descr[0]); 
	TYPE *v1 = (TYPE *)STARPU_VECTOR_GET_PTR(descr[1]);
	TYPE *v2 = (TYPE *)STARPU_VECTOR_GET_PTR(descr[2]);

	unsigned n = STARPU_VECTOR_GET_NX(descr[1]);

	cublasHandle_t handle = starpu_cublas_get_local_handle();
	cublasSetPointerMode(handle, CUBLAS_POINTER_MODE_DEVICE);
	cublasStatus_t status = cublasdot(handle,
		n, v1, 1, v2, 1, dot);
	if (status != CUBLAS_STATUS_SUCCESS)
		STARPU_CUBLAS_REPORT_ERROR(status);
	cublasSetPointerMode(handle, CUBLAS_POINTER_MODE_HOST);
}
#endif

void dot_kernel_cpu(void *descr[], void *cl_arg)
{
	TYPE *dot = (TYPE *)STARPU_VARIABLE_GET_PTR(descr[0]); 
	TYPE *v1 = (TYPE *)STARPU_VECTOR_GET_PTR(descr[1]);
	TYPE *v2 = (TYPE *)STARPU_VECTOR_GET_PTR(descr[2]);

	unsigned n = STARPU_VECTOR_GET_NX(descr[1]);

	TYPE local_dot;
	/* Note that we explicitely cast the result of the DOT kernel because
	 * some BLAS library will return a double for sdot for instance. */
	local_dot = (TYPE)DOT(n, v1, 1, v2, 1);

	*dot = *dot + local_dot;
}

static struct starpu_perfmodel dot_kernel_model =
{
	.type = STARPU_HISTORY_BASED,
	.symbol = "dot_kernel"
};

static struct starpu_codelet dot_kernel_cl =
{
	.can_execute = can_execute,
	.cpu_funcs = {dot_kernel_cpu},
	.cpu_funcs_name = {"dot_kernel_cpu"},
#ifdef STARPU_USE_CUDA
	.cuda_funcs = {dot_kernel_cuda},
#endif
	.cuda_flags = {STARPU_CUDA_ASYNC},
	.nbuffers = 3,
	.model = &dot_kernel_model
};

int dot_kernel(starpu_data_handle_t v1,
	       starpu_data_handle_t v2,
	       starpu_data_handle_t s,
	       unsigned nblocks,
	       int use_reduction)
{
	int ret;

	/* Blank the accumulation variable */
	if (use_reduction)
		starpu_data_invalidate_submit(s);
	else
	{
		ret = starpu_task_insert(&bzero_variable_cl, STARPU_W, s, 0);
		if (ret == -ENODEV) return ret;
		STARPU_CHECK_RETURN_VALUE(ret, "starpu_task_insert");
	}

	unsigned b;
	for (b = 0; b < nblocks; b++)
	{
		ret = starpu_task_insert(&dot_kernel_cl,
					 use_reduction?STARPU_REDUX:STARPU_RW, s,
					 STARPU_R, starpu_data_get_sub_data(v1, 1, b),
					 STARPU_R, starpu_data_get_sub_data(v2, 1, b),
					 STARPU_TAG_ONLY, (starpu_tag_t) b,
					 0);
		STARPU_CHECK_RETURN_VALUE(ret, "starpu_task_insert");
	}
	return 0;
}

/*
 *	SCAL kernel : v1 = p1 v1
 */

#ifdef STARPU_USE_CUDA
static void scal_kernel_cuda(void *descr[], void *cl_arg)
{
	TYPE p1;
	starpu_codelet_unpack_args(cl_arg, &p1);

	TYPE *v1 = (TYPE *)STARPU_VECTOR_GET_PTR(descr[0]);
	unsigned n = STARPU_VECTOR_GET_NX(descr[0]);
 
	/* v1 = p1 v1 */
	TYPE alpha = p1;
	cublasStatus_t status = cublasscal(starpu_cublas_get_local_handle(), n, &alpha, v1, 1);
	if (status != CUBLAS_STATUS_SUCCESS)
		STARPU_CUBLAS_REPORT_ERROR(status);
}
#endif

void scal_kernel_cpu(void *descr[], void *cl_arg)
{
	TYPE alpha;
	starpu_codelet_unpack_args(cl_arg, &alpha);

	TYPE *v1 = (TYPE *)STARPU_VECTOR_GET_PTR(descr[0]);
	unsigned n = STARPU_VECTOR_GET_NX(descr[0]);

	/* v1 = alpha v1 */
	SCAL(n, alpha, v1, 1);
}

static struct starpu_perfmodel scal_kernel_model =
{
	.type = STARPU_HISTORY_BASED,
	.symbol = "scal_kernel"
};

static struct starpu_codelet scal_kernel_cl =
{
	.can_execute = can_execute,
	.cpu_funcs = {scal_kernel_cpu},
	.cpu_funcs_name = {"scal_kernel_cpu"},
#ifdef STARPU_USE_CUDA
	.cuda_funcs = {scal_kernel_cuda},
	.cuda_flags = {STARPU_CUDA_ASYNC},
#endif
	.nbuffers = 1,
	.model = &scal_kernel_model
};

/*
 *	GEMV kernel : v1 = p1 * v1 + p2 * M v2 
 */

#ifdef STARPU_USE_CUDA
static void gemv_kernel_cuda(void *descr[], void *cl_arg)
{
	TYPE *v1 = (TYPE *)STARPU_VECTOR_GET_PTR(descr[0]);
	TYPE *v2 = (TYPE *)STARPU_VECTOR_GET_PTR(descr[2]);
	TYPE *M = (TYPE *)STARPU_MATRIX_GET_PTR(descr[1]);

	unsigned ld = STARPU_MATRIX_GET_LD(descr[1]);
	unsigned nx = STARPU_MATRIX_GET_NX(descr[1]);
	unsigned ny = STARPU_MATRIX_GET_NY(descr[1]);
 
	TYPE alpha, beta;
	starpu_codelet_unpack_args(cl_arg, &beta, &alpha);

	/* Compute v1 = alpha M v2 + beta v1 */
	cublasStatus_t status = cublasgemv(starpu_cublas_get_local_handle(),
			CUBLAS_OP_N, nx, ny, &alpha, M, ld, v2, 1, &beta, v1, 1);
	if (status != CUBLAS_STATUS_SUCCESS)
		STARPU_CUBLAS_REPORT_ERROR(status);
}
#endif

void gemv_kernel_cpu(void *descr[], void *cl_arg)
{
	TYPE *v1 = (TYPE *)STARPU_VECTOR_GET_PTR(descr[0]);
	TYPE *v2 = (TYPE *)STARPU_VECTOR_GET_PTR(descr[2]);
	TYPE *M = (TYPE *)STARPU_MATRIX_GET_PTR(descr[1]);

	unsigned ld = STARPU_MATRIX_GET_LD(descr[1]);
	unsigned nx = STARPU_MATRIX_GET_NX(descr[1]);
	unsigned ny = STARPU_MATRIX_GET_NY(descr[1]);

	TYPE alpha, beta;
	starpu_codelet_unpack_args(cl_arg, &beta, &alpha);

	int worker_size = starpu_combined_worker_get_size();

	if (worker_size > 1)
	{
		/* Parallel CPU task */
		unsigned rank = starpu_combined_worker_get_rank();
		
		unsigned block_size = (ny + worker_size - 1)/worker_size;
		unsigned new_nx = STARPU_MIN(nx, block_size*(rank+1)) - block_size*rank;

		nx = new_nx;
		v1 = &v1[block_size*rank];
		M = &M[block_size*rank];
	}

	/* Compute v1 = alpha M v2 + beta v1 */
	GEMV("N", nx, ny, alpha, M, ld, v2, 1, beta, v1, 1);
}

static struct starpu_perfmodel gemv_kernel_model =
{
	.type = STARPU_HISTORY_BASED,
	.symbol = "gemv_kernel"
};

static struct starpu_codelet gemv_kernel_cl =
{
	.can_execute = can_execute,
	.type = STARPU_SPMD,
	.max_parallelism = INT_MAX,
	.cpu_funcs = {gemv_kernel_cpu},
	.cpu_funcs_name = {"gemv_kernel_cpu"},
#ifdef STARPU_USE_CUDA
	.cuda_funcs = {gemv_kernel_cuda},
	.cuda_flags = {STARPU_CUDA_ASYNC},
#endif
	.nbuffers = 3,
	.model = &gemv_kernel_model
};

int gemv_kernel(starpu_data_handle_t v1,
		starpu_data_handle_t matrix,
		starpu_data_handle_t v2,
		TYPE p1, TYPE p2,
		unsigned nblocks,
		int use_reduction)
{
	unsigned b1, b2;
	int ret;

	for (b2 = 0; b2 < nblocks; b2++)
	{
		ret = starpu_task_insert(&scal_kernel_cl,
					 STARPU_RW, starpu_data_get_sub_data(v1, 1, b2),
					 STARPU_VALUE, &p1, sizeof(p1),
					 STARPU_TAG_ONLY, (starpu_tag_t) b2,
					 0);
		if (ret == -ENODEV) return ret;
		STARPU_CHECK_RETURN_VALUE(ret, "starpu_task_insert");
	}

	for (b2 = 0; b2 < nblocks; b2++)
	{
		for (b1 = 0; b1 < nblocks; b1++)
		{
			TYPE one = 1.0;
			ret = starpu_task_insert(&gemv_kernel_cl,
						 use_reduction?STARPU_REDUX:STARPU_RW,	starpu_data_get_sub_data(v1, 1, b2),
						 STARPU_R,	starpu_data_get_sub_data(matrix, 2, b2, b1),
						 STARPU_R,	starpu_data_get_sub_data(v2, 1, b1),
						 STARPU_VALUE,	&one,	sizeof(one),
						 STARPU_VALUE,	&p2,	sizeof(p2),
						 STARPU_TAG_ONLY, ((starpu_tag_t)b2) * nblocks + b1,
						 0);
			STARPU_CHECK_RETURN_VALUE(ret, "starpu_task_insert");
		}
	}
	return 0;
}

/*
 *	AXPY + SCAL kernel : v1 = p1 * v1 + p2 * v2 
 */
#ifdef STARPU_USE_CUDA
static void scal_axpy_kernel_cuda(void *descr[], void *cl_arg)
{
	TYPE p1, p2;
	starpu_codelet_unpack_args(cl_arg, &p1, &p2);

	TYPE *v1 = (TYPE *)STARPU_VECTOR_GET_PTR(descr[0]);
	TYPE *v2 = (TYPE *)STARPU_VECTOR_GET_PTR(descr[1]);

	unsigned n = STARPU_VECTOR_GET_NX(descr[0]);
 
	/* Compute v1 = p1 * v1 + p2 * v2.
	 *	v1 = p1 v1
	 *	v1 = v1 + p2 v2
	 */
	cublasStatus_t status;
	status = cublasscal(starpu_cublas_get_local_handle(), n, &p1, v1, 1);
	if (status != CUBLAS_STATUS_SUCCESS)
		STARPU_CUBLAS_REPORT_ERROR(status);
	status = cublasaxpy(starpu_cublas_get_local_handle(), n, &p2, v2, 1, v1, 1);
	if (status != CUBLAS_STATUS_SUCCESS)
		STARPU_CUBLAS_REPORT_ERROR(status);
}
#endif

void scal_axpy_kernel_cpu(void *descr[], void *cl_arg)
{
	TYPE p1, p2;
	starpu_codelet_unpack_args(cl_arg, &p1, &p2);

	TYPE *v1 = (TYPE *)STARPU_VECTOR_GET_PTR(descr[0]);
	TYPE *v2 = (TYPE *)STARPU_VECTOR_GET_PTR(descr[1]);

	unsigned nx = STARPU_VECTOR_GET_NX(descr[0]);
 
	/* Compute v1 = p1 * v1 + p2 * v2.
	 *	v1 = p1 v1
	 *	v1 = v1 + p2 v2
	 */
	SCAL(nx, p1, v1, 1);
	AXPY(nx, p2, v2, 1, v1, 1);
}

static struct starpu_perfmodel scal_axpy_kernel_model =
{
	.type = STARPU_HISTORY_BASED,
	.symbol = "scal_axpy_kernel"
};

static struct starpu_codelet scal_axpy_kernel_cl =
{
	.can_execute = can_execute,
	.cpu_funcs = {scal_axpy_kernel_cpu},
	.cpu_funcs_name = {"scal_axpy_kernel_cpu"},
#ifdef STARPU_USE_CUDA
	.cuda_funcs = {scal_axpy_kernel_cuda},
	.cuda_flags = {STARPU_CUDA_ASYNC},
#endif
	.nbuffers = 2,
	.model = &scal_axpy_kernel_model
};

int scal_axpy_kernel(starpu_data_handle_t v1, TYPE p1,
		     starpu_data_handle_t v2, TYPE p2,
		     unsigned nblocks)
{
	unsigned b;
	for (b = 0; b < nblocks; b++)
	{
		int ret;
		ret = starpu_task_insert(&scal_axpy_kernel_cl,
					 STARPU_RW, starpu_data_get_sub_data(v1, 1, b),
					 STARPU_R,  starpu_data_get_sub_data(v2, 1, b),
					 STARPU_VALUE, &p1, sizeof(p1),
					 STARPU_VALUE, &p2, sizeof(p2),
					 STARPU_TAG_ONLY, (starpu_tag_t) b,
					 0);
		if (ret == -ENODEV) return ret;
		STARPU_CHECK_RETURN_VALUE(ret, "starpu_task_insert");
	}
	return 0;
}


/*
 *	AXPY kernel : v1 = v1 + p1 * v2 
 */
#ifdef STARPU_USE_CUDA
static void axpy_kernel_cuda(void *descr[], void *cl_arg)
{
	TYPE p1;
	starpu_codelet_unpack_args(cl_arg, &p1);

	TYPE *v1 = (TYPE *)STARPU_VECTOR_GET_PTR(descr[0]);
	TYPE *v2 = (TYPE *)STARPU_VECTOR_GET_PTR(descr[1]);

	unsigned n = STARPU_VECTOR_GET_NX(descr[0]);
 
	/* Compute v1 = v1 + p1 * v2.
	 */
	cublasStatus_t status = cublasaxpy(starpu_cublas_get_local_handle(),
			n, &p1, v2, 1, v1, 1);
	if (status != CUBLAS_STATUS_SUCCESS)
		STARPU_CUBLAS_REPORT_ERROR(status);
}
#endif

void axpy_kernel_cpu(void *descr[], void *cl_arg)
{
	TYPE p1;
	starpu_codelet_unpack_args(cl_arg, &p1);

	TYPE *v1 = (TYPE *)STARPU_VECTOR_GET_PTR(descr[0]);
	TYPE *v2 = (TYPE *)STARPU_VECTOR_GET_PTR(descr[1]);

	unsigned nx = STARPU_VECTOR_GET_NX(descr[0]);
 
	/* Compute v1 = p1 * v1 + p2 * v2.
	 */
	AXPY(nx, p1, v2, 1, v1, 1);
}

static struct starpu_perfmodel axpy_kernel_model =
{
	.type = STARPU_HISTORY_BASED,
	.symbol = "axpy_kernel"
};

static struct starpu_codelet axpy_kernel_cl =
{
	.can_execute = can_execute,
	.cpu_funcs = {axpy_kernel_cpu},
	.cpu_funcs_name = {"axpy_kernel_cpu"},
#ifdef STARPU_USE_CUDA
	.cuda_funcs = {axpy_kernel_cuda},
	.cuda_flags = {STARPU_CUDA_ASYNC},
#endif
	.nbuffers = 2,
	.model = &axpy_kernel_model
};

int axpy_kernel(starpu_data_handle_t v1,
		starpu_data_handle_t v2, TYPE p1,
		unsigned nblocks)
{
	unsigned b;
	for (b = 0; b < nblocks; b++)
	{
		int ret;
		ret = starpu_task_insert(&axpy_kernel_cl,
					 STARPU_RW, starpu_data_get_sub_data(v1, 1, b),
					 STARPU_R,  starpu_data_get_sub_data(v2, 1, b),
					 STARPU_VALUE, &p1, sizeof(p1),
					 STARPU_TAG_ONLY, (starpu_tag_t) b,
					 0);
		if (ret == -ENODEV) return ret;
		STARPU_CHECK_RETURN_VALUE(ret, "starpu_task_insert");
	}
	return 0;
}

int copy_handle(starpu_data_handle_t dst, starpu_data_handle_t src, unsigned nblocks)
{
	unsigned b;
	for (b = 0; b < nblocks; b++)
		starpu_data_cpy(starpu_data_get_sub_data(dst, 1, b), starpu_data_get_sub_data(src, 1, b), 1, NULL, NULL);
	return 0;
}