|
@@ -0,0 +1,384 @@
|
|
|
+/*/* 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;
|
|
|
+}
|