Remove the mult_impl example, the vector_scal example makes much more sense

examples/Makefile.am

@@ -152,7 +152,6 @@ examplebin_PROGRAMS +=				\
 	basic_examples/mult			\
 	basic_examples/block			\
 	basic_examples/variable			\
-	basic_examples/mult_impl                \
 	filters/fvector				\
 	filters/fblock				\
 	filters/fmatrix				\

examples/basic_examples/mult_impl.c

@@ -1,384 +0,0 @@
-/*/* StarPU --- Runtime system for heterogeneous multicore architectures.
- *
- * Copyright (C) 2009, 2010, 2011  Université de Bordeaux 1
- * Copyright (C) 2010, 2011  Télécom-SudParis
- *
- * 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.
- */
-
-
-#include <string.h>
-#include <math.h>
-#include <sys/types.h>
-#include <sys/time.h>
-#include <pthread.h>
-#include <signal.h>
-
-#include <starpu.h>
-
-static float *A, *B, *C;
-static starpu_data_handle A_handle, B_handle, C_handle;
-
-static unsigned nslicesx = 4;
-static unsigned nslicesy = 4;
-static unsigned xdim = 1024;
-static unsigned ydim = 1024;
-static unsigned zdim = 512;
-
-
-double mult_gemm_cost(starpu_buffer_descr *descr)
-{
-	/* C = A * B */
-	uint32_t nxC, nyC, nxA;
-
-
-	nxC = starpu_matrix_get_nx(descr[2].handle);
-	nyC = starpu_matrix_get_ny(descr[2].handle);
-	nxA = starpu_matrix_get_nx(descr[0].handle);
-
-	//printf("nxC %d nxC %d nxA %d\n", nxC, nyC, nxA);
-
-	double cost = ((double)nxC)*((double)nyC)*((double)nxA/1000.0f/4.11f);
-
-	printf("cost %e \n", cost);
-
-	return cost;
-}
-
-static void cpu_mult(void *descr[], __attribute__((unused))  void *arg)
-{
-	float *subA, *subB, *subC;
-	uint32_t nxC, nyC, nyA;
-	uint32_t ldA, ldB, ldC;
-	printf("On application: Hello, this is kernel cpu_mult\n\n");
-	/* .blas.ptr gives a pointer to the first element of the local copy */
-	subA = (float *)STARPU_MATRIX_GET_PTR(descr[0]);
-	subB = (float *)STARPU_MATRIX_GET_PTR(descr[1]);
-	subC = (float *)STARPU_MATRIX_GET_PTR(descr[2]);
-
-	/* .blas.nx is the number of rows (consecutive elements) and .blas.ny
-	 * is the number of lines that are separated by .blas.ld elements (ld
-	 * stands for leading dimension).
-	 * NB: in case some filters were used, the leading dimension is not
-	 * guaranteed to be the same in main memory (on the original matrix)
-	 * and on the accelerator! */
-	nxC = STARPU_MATRIX_GET_NX(descr[2]);
-	nyC = STARPU_MATRIX_GET_NY(descr[2]);
-	nyA = STARPU_MATRIX_GET_NY(descr[0]);
-
-	ldA = STARPU_MATRIX_GET_LD(descr[0]);
-	ldB = STARPU_MATRIX_GET_LD(descr[1]);
-	ldC = STARPU_MATRIX_GET_LD(descr[2]);
-
-	/* we assume a FORTRAN-ordering! */
-	unsigned i,j,k;
-	for (i = 0; i < nyC; i++)
-	{
-		for (j = 0; j < nxC; j++)
-		{
-			float sum = 0.0;
-
-			for (k = 0; k < nyA; k++)
-			{
-				sum += subA[j+k*ldA]*subB[k+i*ldB];
-			}
-
-			subC[j + i*ldC] = sum;
-		}
-	}
-}
-
-static void cpu_mult_2(void *descr[], __attribute__((unused))  void *arg)
-{
-	float *subA, *subB, *subC;
-	uint32_t nxC, nyC, nyA;
-	uint32_t ldA, ldB, ldC;
-	printf("On application: this is kernel cpu_mult_2\n\n");
-	/* .blas.ptr gives a pointer to the first element of the local copy */
-	subA = (float *)STARPU_MATRIX_GET_PTR(descr[0]);
-	subB = (float *)STARPU_MATRIX_GET_PTR(descr[1]);
-	subC = (float *)STARPU_MATRIX_GET_PTR(descr[2]);
-
-	nxC = STARPU_MATRIX_GET_NX(descr[2]);
-	nyC = STARPU_MATRIX_GET_NY(descr[2]);
-	nyA = STARPU_MATRIX_GET_NY(descr[0]);
-
-	ldA = STARPU_MATRIX_GET_LD(descr[0]);
-	ldB = STARPU_MATRIX_GET_LD(descr[1]);
-	ldC = STARPU_MATRIX_GET_LD(descr[2]);
-
-	/* we assume a FORTRAN-ordering! */
-	unsigned i,j,k;
-	for (j = 0; j < nxC; j++)
-	{
-		for (i = 0; i < nyC; i++)
-		{
-			float sum = 0.0;
-
-			for (k = 0; k < nyA; k++)
-			{
-				sum += subA[j+k*ldA]*subB[k+i*ldB];
-			}
-
-			subC[j + i*ldC] = sum;
-		}
-	}
-}
-
-
-
-static void init_problem_data(void)
-{
-	unsigned i,j;
-
-	/* we initialize matrices A, B and C in the usual way */
-
-	A = malloc(zdim*ydim*sizeof(float));
-	B = malloc(xdim*zdim*sizeof(float));
-	C = malloc(xdim*ydim*sizeof(float));
-
-	/* fill the A and B matrices */
-	srand(2009);
-	for (j=0; j < ydim; j++) {
-		for (i=0; i < zdim; i++) {
-			A[j+i*ydim] = (float)(starpu_drand48());
-		}
-	}
-
-	for (j=0; j < zdim; j++) {
-		for (i=0; i < xdim; i++) {
-			B[j+i*zdim] = (float)(starpu_drand48());
-		}
-	}
-
-	for (j=0; j < ydim; j++) {
-		for (i=0; i < xdim; i++) {
-			C[j+i*ydim] = (float)(0);
-		}
-	}
-}
-
-static void partition_mult_data(void)
-{
-	/* note that we assume a FORTRAN ordering here! */
-
-	starpu_matrix_data_register(&A_handle, 0, (uintptr_t)A,
-		ydim, ydim, zdim, sizeof(float));
-	starpu_matrix_data_register(&B_handle, 0, (uintptr_t)B,
-		zdim, zdim, xdim, sizeof(float));
-	starpu_matrix_data_register(&C_handle, 0, (uintptr_t)C,
-		ydim, ydim, xdim, sizeof(float));
-
-	/* A filter is a method to partition a data into disjoint chunks, it is
-	 * described by the means of the "struct starpu_data_filter" structure that
-	 * contains a function that is applied on a data handle to partition it
-	 * into smaller chunks, and an argument that is passed to the function
-	 * (eg. the number of blocks to create here).
-	 */
-
-	struct starpu_data_filter vert = {
-		.filter_func = starpu_vertical_block_filter_func,
-		.nchildren = nslicesx,
-		.get_nchildren = NULL,
-		.get_child_ops = NULL
-	};
-
-	struct starpu_data_filter horiz = {
-		.filter_func = starpu_block_filter_func,
-		.nchildren = nslicesy,
-		.get_nchildren = NULL,
-		.get_child_ops = NULL
-	};
-
-/*
- *	Illustration with nslicex = 4 and nslicey = 2, it is possible to access
- *	sub-data by using the "starpu_data_get_sub_data" method, which takes a data handle,
- *	the number of filters to apply, and the indexes for each filters, for
- *	instance:
- *
- *		A' handle is starpu_data_get_sub_data(A_handle, 1, 1);
- *		B' handle is starpu_data_get_sub_data(B_handle, 1, 2);
- *		C' handle is starpu_data_get_sub_data(C_handle, 2, 2, 1);
- *
- *	Note that here we applied 2 filters recursively onto C.
- *
- *	"starpu_data_get_sub_data(C_handle, 1, 3)" would return a handle to the 4th column
- *	of blocked matrix C for example.
- *
- *		              |---|---|---|---|
- *		              |   |   | B'|   | B
- *		              |---|---|---|---|
- *		                0   1   2   3
- *		     |----|   |---|---|---|---|
- *		     |    |   |   |   |   |   |
- *		     |    | 0 |   |   |   |   |
- *		     |----|   |---|---|---|---|
- *		     | A' |   |   |   | C'|   |
- *		     |    |   |   |   |   |   |
- *		     |----|   |---|---|---|---|
- *		       A              C
- *
- *	IMPORTANT: applying filters is equivalent to partitionning a piece of
- *	data in a hierarchical manner, so that memory consistency is enforced
- *	for each of the elements independantly. The tasks should therefore NOT
- *	access inner nodes (eg. one column of C or the whole C) but only the
- *	leafs of the tree (ie. blocks here). Manipulating inner nodes is only
- *	possible by disapplying the filters (using starpu_data_unpartition), to
- *	enforce memory consistency.
- */
-
-	starpu_data_partition(B_handle, &vert);
-	starpu_data_partition(A_handle, &horiz);
-
-	/* starpu_data_map_filters is a variable-arity function, the first argument
-	 * is the handle of the data to partition, the second argument is the
-	 * number of filters to apply recursively. Filters are applied in the
-	 * same order as the arguments.
-	 * This would be equivalent to starpu_data_partition(C_handle, &vert) and
-	 * then applying horiz on each sub-data (ie. each column of C)
-	 */
-	starpu_data_map_filters(C_handle, 2, &vert, &horiz);
-}
-
-static struct starpu_perfmodel_t starpu_dgemm_model_common = {
-	.cost_model = mult_gemm_cost,
-	.type = STARPU_HISTORY_BASED,//STARPU_COMMON, //STARPU_PER_ARCH,
-	.symbol = "mult_perf_model"
-};
-
-/*
-static struct starpu_perfmodel_t mult_perf_model = {
-	.type = STARPU_HISTORY_BASED,
-	.symbol = "mult_perf_model"
-};
-*/
-
-struct starpu_conf conf = {
-		.sched_policy_name = "heft",
-		.calibrate = 1,
-		.ncpus = 4
-};
-
-
-static starpu_codelet cl = {
-        /* we can only execute that kernel on a CPU yet */
-        .where = STARPU_CPU,
-        //.starpu_impl_multiple = 1,
-        /* CPU implementation of the codelet */
-        .cpu_func = STARPU_MULTIPLE_CPU_IMPLEMENTATIONS,
-        .cpu_funcs = {cpu_mult,cpu_mult_2},
-        /* the codelet manipulates 3 buffers that are managed by the
-         * DSM */
-        .nbuffers = 3,
-        /* in case the scheduling policy may use performance models */
-        .model = &starpu_dgemm_model_common
-};
-
-static void launch_tasks(void)
-{
-	/* partition the work into slices */
-	unsigned taskx, tasky;
-
-	for (taskx = 0; taskx < nslicesx; taskx++)
-	{
-		for (tasky = 0; tasky < nslicesy; tasky++)
-		{
-			/* C[taskx, tasky] = A[tasky] B[taskx] */
-
-			/* by default, starpu_task_create() returns an
- 			 * asynchronous task (ie. task->synchronous = 0) */
-			struct starpu_task *task = starpu_task_create();
-
-			/* this task implements codelet "cl" */
-			task->cl = &cl;
-
-			/*
-			 *              |---|---|---|---|
-			 *              |   | * |   |   | B
-			 *              |---|---|---|---|
-			 *                    X
-			 *     |----|   |---|---|---|---|
-			 *     |****| Y |   |***|   |   |
-			 *     |****|   |   |***|   |   |
-			 *     |----|   |---|---|---|---|
-			 *     |    |   |   |   |   |   |
-			 *     |    |   |   |   |   |   |
-			 *     |----|   |---|---|---|---|
-			 *       A              C
-			 */
-
-			/* there was a single filter applied to matrices A
-			 * (respectively B) so we grab the handle to the chunk
-			 * identified by "tasky" (respectively "taskx). The "1"
-			 * tells StarPU that there is a single argument to the
-			 * variable-arity function starpu_data_get_sub_data */
-			task->buffers[0].handle = starpu_data_get_sub_data(A_handle, 1, tasky);
-			task->buffers[0].mode = STARPU_R;
-			task->buffers[1].handle = starpu_data_get_sub_data(B_handle, 1, taskx);
-			task->buffers[1].mode = STARPU_R;
-
-			/* 2 filters were applied on matrix C, so we give
-			 * starpu_data_get_sub_data 2 arguments. The order of the arguments
-			 * must match the order in which the filters were
-			 * applied.
-			 * NB: starpu_data_get_sub_data(C_handle, 1, k) would have returned
-			 * a handle to the column number k of matrix C.
-			 * NB2: starpu_data_get_sub_data(C_handle, 2, taskx, tasky) is
-			 * equivalent to
-			 * starpu_data_get_sub_data(starpu_data_get_sub_data(C_handle, 1, taskx), 1, tasky)*/
-			task->buffers[2].handle = starpu_data_get_sub_data(C_handle, 2, taskx, tasky);
-			task->buffers[2].mode = STARPU_W;
-
-			/* this is not a blocking call since task->synchronous = 0 */
-			int summit_task;
-			summit_task = starpu_task_submit(task);
-			printf("task is submmited or not %d\n",summit_task);
-
-		}
-	}
-}
-
-int main(void)
-{
-	/* start the runtime */
-	starpu_init(&conf);
-
-	/* initialize matrices A, B and C and register them to StarPU */
-	init_problem_data();
-
-	/* partition matrices into blocks that can be manipulated by the
- 	 * codelets */
-	partition_mult_data();
-
-	/* submit all tasks in an asynchronous fashion */
-	launch_tasks();
-
-	/* wait for termination */
-	starpu_task_wait_for_all();
-
-	/* remove the filters applied by the means of starpu_data_map_filters; now
- 	 * it's not possible to manipulate a subset of C using starpu_data_get_sub_data until
-	 * starpu_data_map_filters is called again on C_handle.
-	 * The second argument is the memory node where the different subsets
-	 * should be reassembled, 0 = main memory (RAM) */
-	starpu_data_unpartition(C_handle, 0);
-
-	/* stop monitoring matrix C : after this, it is not possible to pass C
-	 * (or any subset of C) as a codelet input/output. This also implements
-	 * a barrier so that the piece of data is put back into main memory in
-	 * case it was only available on a GPU for instance. */
-	starpu_data_unregister(C_handle);
-
-	starpu_shutdown();
-
-	return 0;
-}