starpu-max/examples/sched_ctx/gpu_partition.c

/* StarPU --- Runtime system for heterogeneous multicore architectures.
 *
 * Copyright (C) 2016  Uppsala University
 *
 * 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.
 */

/*
 * This creates two dumb vectors & run axpy on them.
 * You have to set STARPU_NWORKER_PER_CUDA=2
 */

#include <starpu.h>
#include <stdlib.h>
#include <stdio.h>
#include <assert.h>
#include <math.h>

#include <common/blas.h>

#ifdef STARPU_USE_CUDA
#include <cublas.h>
#endif


#define N	512*512
#define NITER   100


#define FPRINTF(ofile, fmt, ...) do { if (!getenv("STARPU_SSILENT")) {fprintf(ofile, fmt, ## __VA_ARGS__); }} while(0)

#define EPSILON 1e-6

float *_vec_x[NITER], *_vec_y[NITER];
float _alpha = 3.41;

/* descriptors for StarPU */
starpu_data_handle_t _handle_y[NITER], _handle_x[NITER];

void axpy_cpu(void *descr[], STARPU_ATTRIBUTE_UNUSED void *arg)
{
	float alpha = *((float *)arg);

	unsigned n = STARPU_VECTOR_GET_NX(descr[0]);

	float *block_x = (float *)STARPU_VECTOR_GET_PTR(descr[0]);
	float *block_y = (float *)STARPU_VECTOR_GET_PTR(descr[1]);

	unsigned i;
	for( i = 0; i < n; i++)
		block_y[i] = alpha * block_x[i] + block_y[i];
}

#ifdef STARPU_USE_CUDA
extern void cuda_axpy(void *descr[], STARPU_ATTRIBUTE_UNUSED void *_args);
#endif

static struct starpu_perfmodel axpy_model =
{
	.type = STARPU_HISTORY_BASED,
	.symbol = "axpy"
};

static struct starpu_codelet axpy_cl =
{
	/* .cpu_funcs = {axpy_cpu}, */
	/* .cpu_funcs_name = {"axpy_cpu"}, */
#ifdef STARPU_USE_CUDA
	.cuda_funcs = {cuda_axpy},
#elif defined(STARPU_SIMGRID)
	.cuda_funcs = {(void*)1},
#endif
	.cuda_flags = {STARPU_CUDA_ASYNC},
	.nbuffers = 2,
	.modes = {STARPU_R, STARPU_RW},
	.name = "axpy",
	.model = &axpy_model
};

static int
check(int niter)
{
	int i;
	for (i = 0; i < N; i++)
	{
		float expected_value = _alpha * _vec_x[niter][i] + 4.0;
		if (fabs(_vec_y[niter][i] - expected_value) > expected_value * EPSILON)
		{
			FPRINTF(stderr,"[error] at %d, %f*%f+%f==%f, expected %f\n", i, _alpha, _vec_x[niter][i], 4.0, _vec_y[niter][i], expected_value);
			return EXIT_FAILURE;
		}
	}

	return EXIT_SUCCESS;
}

int main(int argc, char **argv)
{
	int ret, exit_value = 0;
	int iter;
	int ncuda = 0;
	int gpu_devid = -1;

	/* Check we have separate threads for streams */
	if (!starpu_get_env_number_default("STARPU_ONE_THREAD_PER_STREAM", 0))
		return 77;

	/* Initialize StarPU */
	ret = starpu_init(NULL);
	if (ret == -ENODEV)
		return 77;
	STARPU_CHECK_RETURN_VALUE(ret, "starpu_init");

#ifdef STARPU_USE_CUDA
	ncuda = starpu_worker_get_devids(STARPU_CUDA_WORKER, &gpu_devid, 1);
	FPRINTF(stderr, "gpu_devid found %d \n", gpu_devid);
#endif
	if (ncuda == 0)
	{
		starpu_shutdown();
		return 77;
	}

	/* This is equivalent to
		vec_a = malloc(N*sizeof(float));
		vec_b = malloc(N*sizeof(float));
	*/
	for(iter = 0; iter < NITER; iter++)
	{
		starpu_malloc((void **)&_vec_x[iter], N*sizeof(float));
		assert(_vec_x[iter]);

		starpu_malloc((void **)&_vec_y[iter], N*sizeof(float));
		assert(_vec_y[iter]);

		unsigned i;
		for (i = 0; i < N; i++)
		{
			_vec_x[iter][i] = 1.0f; /*(float)starpu_drand48(); */
			_vec_y[iter][i] = 4.0f; /*(float)starpu_drand48(); */
		}

		/* Declare the data to StarPU */
		starpu_vector_data_register(&_handle_x[iter], STARPU_MAIN_RAM, (uintptr_t)_vec_x[iter], N, sizeof(float));
		starpu_vector_data_register(&_handle_y[iter], STARPU_MAIN_RAM, (uintptr_t)_vec_y[iter], N, sizeof(float));
	}

	double start;
	double end;
#ifdef STARPU_USE_CUDA
	unsigned nworkers = starpu_worker_get_count();
	int stream_workerids[nworkers];

	int nstreams = starpu_worker_get_stream_workerids(gpu_devid, stream_workerids, STARPU_CUDA_WORKER);

	int s;
	for(s = 0; s < nstreams; s++)
		FPRINTF(stderr, "stream w %d \n", stream_workerids[s]);

	int ncpus = starpu_cpu_worker_get_count();
	int workers[ncpus+nstreams];
	starpu_worker_get_ids_by_type(STARPU_CPU_WORKER, workers, ncpus);

	int sched_ctxs[nstreams];
	int nsms[nstreams];
	nsms[0] = 6;
	nsms[1] = 7;

	for(s = 0; s < nstreams; s++)
	{
		sched_ctxs[s] = starpu_sched_ctx_create(&stream_workerids[s], 1, "subctx",  STARPU_SCHED_CTX_CUDA_NSMS, nsms[s], 0);
		workers[ncpus+s] = stream_workerids[s];
	}
	unsigned sched_ctx1 = starpu_sched_ctx_create(workers, ncpus+nstreams, "ctx1", STARPU_SCHED_CTX_SUB_CTXS, sched_ctxs, nstreams, STARPU_SCHED_CTX_POLICY_NAME, "dmdas", 0);

	FPRINTF(stderr, "parent ctx %d\n", sched_ctx1);
	starpu_sched_ctx_set_context(&sched_ctx1);

#endif
	start = starpu_timing_now();

	for (iter = 0; iter < NITER; iter++)
	{
		struct starpu_task *task = starpu_task_create();

		task->cl = &axpy_cl;

		task->cl_arg = &_alpha;
		task->cl_arg_size = sizeof(_alpha);

		task->handles[0] = _handle_x[iter];
		task->handles[1] = _handle_y[iter];

		ret = starpu_task_submit(task);
		if (ret == -ENODEV)
		{
			exit_value = 77;
			goto enodev;
		}
		STARPU_CHECK_RETURN_VALUE(ret, "starpu_task_submit");
	}

	starpu_task_wait_for_all();

enodev:
	for(iter = 0; iter < NITER; iter++)
	{
		starpu_data_unregister(_handle_x[iter]);
		starpu_data_unregister(_handle_y[iter]);
	}
	end = starpu_timing_now();
        double timing = end - start;

	FPRINTF(stderr, "timing -> %2.2f us %2.2f MB/s\n", timing, 3*N*sizeof(float)/timing);

//	FPRINTF(stderr, "AFTER y[0] = %2.2f (ALPHA = %2.2f)\n", _vec_y[iter][0], _alpha);

	if (exit_value != 77)
	{
		for(iter = 0; iter < NITER; iter++)
		{
			exit_value = check(iter);
			if(exit_value != EXIT_SUCCESS)
				break;
		}
	}

	for(iter = 0; iter < NITER; iter++)
	{
		starpu_free((void *)_vec_x[iter]);
		starpu_free((void *)_vec_y[iter]);
	}

	/* Stop StarPU */
	starpu_shutdown();

	return exit_value;
}