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

/* StarPU --- Runtime system for heterogeneous multicore architectures.
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 * Copyright (C) 2015-2021  Université de Bordeaux, CNRS (LaBRI UMR 5800), Inria
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/*! \page ClusteringAMachine Clustering A Machine

\section ClusteringGeneralIdeas General Ideas

Clusters are a concept introduced in this
<a href="https://hal.inria.fr/view/index/docid/1181135">paper</a>.

The granularity problem is tackled by using resource aggregation:
instead of dynamically splitting tasks, resources are aggregated
to process coarse grain tasks in a parallel fashion. This is built on
top of scheduling contexts to be able to handle any type of parallel
tasks.

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 \b cluster) on
top of which the parallel tasks (e.g. OpenMP tasks) will be run 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.

<br>

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

<br>

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 creation function
starpu_cluster_machine() takes optional parameters after the \c hwloc
object (always terminated by the value \c 0) which allow to parametrize the
cluster creation. These parameters can help creating clusters of a
type different from OpenMP, or create a more precise partition of the
machine.

This is explained in Section \ref CreatingCustomClusters.

\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 types 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.

The cluster type is set when calling the function
starpu_cluster_machine() with the parameter ::STARPU_CLUSTER_TYPE as
in the example below, which is creating a \c 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.

<br>

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

Parameters that can be given to starpu_cluster_machine() are
::STARPU_CLUSTER_MIN_NB,
::STARPU_CLUSTER_MAX_NB, ::STARPU_CLUSTER_NB,
::STARPU_CLUSTER_POLICY_NAME, ::STARPU_CLUSTER_POLICY_STRUCT,
::STARPU_CLUSTER_KEEP_HOMOGENEOUS, ::STARPU_CLUSTER_PREFERE_MIN,
::STARPU_CLUSTER_CREATE_FUNC, ::STARPU_CLUSTER_CREATE_FUNC_ARG,
::STARPU_CLUSTER_TYPE, ::STARPU_CLUSTER_AWAKE_WORKERS,
::STARPU_CLUSTER_PARTITION_ONE, ::STARPU_CLUSTER_NEW and
::STARPU_CLUSTER_NCORES.


\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 type
of 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 interface function between StarPU
and the other runtime must be specified through the field
starpu_task::prologue_callback_pop_func. Such a function can be
similar to the OpenMP thread team creation one (see above).

<br>

Note that the OpenMP mode is the default mode 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, the flag
::STARPU_SCHED_CTX_AWAKE_WORKERS must be set when using the scheduling
context API function starpu_sched_ctx_create(), or the flag
::STARPU_CLUSTER_AWAKE_WORKERS must be set when using the cluster API
function starpu_cluster_machine().

*/