starpu-max/doc/doxygen/chapters/490_clustering_a_machine.doxy

/* StarPU --- Runtime system for heterogeneous multicore architectures.
 *
 * Copyright (C) 2015-2019                                CNRS
 * Copyright (C) 2015,2018                                Université de Bordeaux
 * Copyright (C) 2015,2016                                Inria
 *
 * 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.
 */

/*! \page ClusteringAMachine Clustering A Machine

TODO: clarify and put more explanations, express how to create clusters
using the context API.

\section GeneralIdeas General Ideas
Clusters are a concept introduced in this
<a href="https://hal.inria.fr/view/index/docid/1181135">paper</a>. This
comes from a basic idea, making use of two levels of parallelism in a DAG.
We keep the DAG parallelism but consider on top of it that a task can
contain internal parallelism. A good example is if each task in the DAG
is OpenMP enabled.

The particularity of such tasks is that we will combine the power of two
runtime systems: StarPU will manage the DAG parallelism and another
runtime (e.g. OpenMP) will manage the internal parallelism. The challenge
is in creating an interface between the two runtime systems so that StarPU
can regroup cores inside a machine (creating what we call a "cluster") on
top of which the parallel tasks (e.g. OpenMP tasks) will be ran in a
contained fashion.

The aim of the cluster API is to facilitate this process in an automatic
fashion. For this purpose, we depend on the \c hwloc tool to detect the
machine configuration and then partition it into usable clusters.

An example of code running on clusters is available in
<c>examples/sched_ctx/parallel_tasks_with_cluster_api.c</c>.

Let's first look at how to create a cluster.

To enable clusters in StarPU, one needs to set the configure option
\ref enable-cluster "--enable-cluster".

\section CreatingClusters Creating Clusters

Partitioning a machine into clusters with the cluster API is fairly
straightforward. The simplest way is to state under which machine
topology level we wish to regroup all resources. This level is an \c hwloc
object, of the type <c>hwloc_obj_type_t</c>. More information can be found in the
<a href="https://www.open-mpi.org/projects/hwloc/doc/v2.0.3/">hwloc
documentation</a>.

Once a cluster is created, the full machine is represented with an opaque
structure starpu_cluster_machine. This can be printed to show the
current machine state.

\code{.c}
struct starpu_cluster_machine *clusters;
clusters = starpu_cluster_machine(HWLOC_OBJ_SOCKET, 0);
starpu_cluster_print(clusters);

//... submit some tasks with OpenMP computations

starpu_uncluster_machine(clusters);
//... we are back in the default starpu state
\endcode

The following graphic is an example of what a particular machine can
look like once clusterized. The main difference is that we have less
worker queues and tasks which will be executed on several resources at
once. The execution of these tasks will be left to the internal runtime
system, represented with a dashed box around the resources.

\image latex runtime-par.eps "StarPU using parallel tasks" width=0.5\textwidth
\image html runtime-par.png "StarPU using parallel tasks"

Creating clusters as shown in the example above will create workers able to
execute OpenMP code by default. The cluster API aims in allowing to
parametrize the cluster creation and can take a <c>va_list</c> of arguments
as input after the \c hwloc object (always terminated by a 0 value). These can
help creating clusters of a type different from OpenMP, or create a more
precise partition of the machine.

\section ExampleOfConstrainingOpenMP Example Of Constraining OpenMP

Clusters require being able to constrain the runtime managing the internal
task parallelism (internal runtime) to the resources set by StarPU. The
purpose of this is to express how StarPU must communicate with the internal
runtime to achieve the required cooperation. In the case of OpenMP, StarPU
will provide an awake thread from the cluster to execute this liaison. It
will then provide on demand the process ids of the other resources supposed
to be in the region. Finally, thanks to an OpenMP region we can create the
required number of threads and bind each of them on the correct region.
These will then be reused each time we encounter a <c>\#pragma omp
parallel</c> in the following computations of our program.

The following graphic is an example of what an OpenMP-type cluster looks
like and how it represented in StarPU. We can see that one StarPU (black)
thread is awake, and we need to create on the other resources the OpenMP
threads (in pink).

\image latex parallel_worker2.eps "StarPU with an OpenMP cluster" width=0.3\textwidth
\image html parallel_worker2.png "StarPU with an OpenMP cluster"

Finally, the following code shows how to force OpenMP to cooperate with StarPU
and create the aforementioned OpenMP threads constrained in the cluster's
resources set:
\code{.c}
void starpu_openmp_prologue(void * sched_ctx_id)
{
  int sched_ctx = *(int*)sched_ctx_id;
  int *cpuids = NULL;
  int ncpuids = 0;
  int workerid = starpu_worker_get_id();

  //we can target only CPU workers
  if (starpu_worker_get_type(workerid) == STARPU_CPU_WORKER)
  {
    //grab all the ids inside the cluster
    starpu_sched_ctx_get_available_cpuids(sched_ctx, &cpuids, &ncpuids);
    //set the number of threads
    omp_set_num_threads(ncpuids);
#pragma omp parallel
    {
      //bind each threads to its respective resource
      starpu_sched_ctx_bind_current_thread_to_cpuid(cpuids[omp_get_thread_num()]);
    }
    free(cpuids);
  }
  return;
}
\endcode

This function is the default function used when calling starpu_cluster_machine() without extra parameter.

Cluster are based on several tools and models already available within
StarPU contexts, and merely extend contexts. More on contexts can be
read in Section \ref SchedulingContexts.

\section CreatingCustomClusters Creating Custom Clusters

Clusters can be created either with the predefined functions provided
within StarPU, or with user-defined functions to bind another runtime
inside StarPU.

The predefined cluster types provided by StarPU are
::STARPU_CLUSTER_OPENMP, ::STARPU_CLUSTER_INTEL_OPENMP_MKL and
::STARPU_CLUSTER_GNU_OPENMP_MKL. The last one is only provided if
StarPU is compiled with the \c MKL library.  It uses MKL functions to
set the number of threads which is more reliable when using an OpenMP
implementation different from the Intel one.

Here an example creating a MKL cluster.
\code{.c}
struct starpu_cluster_machine *clusters;
clusters = starpu_cluster_machine(HWLOC_OBJ_SOCKET,
                                 STARPU_CLUSTER_TYPE, STARPU_CLUSTER_GNU_OPENMP_MKL,
                                 0);
\endcode

Using the default type ::STARPU_CLUSTER_OPENMP is similar to calling
starpu_cluster_machine() without any extra parameter.

Users can also define their own function.

\code{.c}
void foo_func(void* foo_arg);

\\...
int foo_arg = 0;
struct starpu_cluster_machine *clusters;
clusters = starpu_cluster_machine(HWLOC_OBJ_SOCKET,
                                  STARPU_CLUSTER_CREATE_FUNC, &foo_func,
                                  STARPU_CLUSTER_CREATE_FUNC_ARG, &foo_arg,
                                  0);
\endcode

\section ClustersWithSchedulingContextsAPI Clusters With Scheduling

As previously mentioned, the cluster API is implemented
on top of \ref SchedulingContexts. Its main addition is to ease the
creation of a machine CPU partition with no overlapping by using
\c hwloc, whereas scheduling contexts can use any number of any
resources.

It is therefore possible, but not recommended, to create clusters
using the scheduling contexts API. This can be useful mostly in the
most complex machine configurations where users have to dimension
precisely clusters by hand using their own algorithm.

\code{.c}
/* the list of resources the context will manage */
int workerids[3] = {1, 3, 10};

/* indicate the list of workers assigned to it, the number of workers,
the name of the context and the scheduling policy to be used within
the context */
int id_ctx = starpu_sched_ctx_create(workerids, 3, "my_ctx", 0);

/* let StarPU know that the following tasks will be submitted to this context */
starpu_sched_ctx_set_task_context(id);

task->prologue_callback_pop_func=&runtime_interface_function_here;

/* submit the task to StarPU */
starpu_task_submit(task);
\endcode

As this example illustrates, creating a context without scheduling
policy will create a cluster. The important change is that users
will have to specify an interface function between StarPU and the other runtime.
This can be done in the field starpu_task::prologue_callback_pop_func. Such a function
can be similar to the OpenMP thread team creation one (see above).

Note that the OpenMP mode is the default one both for clusters and
contexts. The result of a cluster creation is a woken up master worker
and sleeping "slaves" which allow the master to run tasks on their
resources. To create a cluster with woken up workers one can use the
flag \ref STARPU_SCHED_CTX_AWAKE_WORKERS with the scheduling context
API and \ref STARPU_CLUSTER_AWAKE_WORKERS with the cluster API as
parameter to the creation function.

*/