StarPU Handbook
Clustering A Machine

General Ideas

Clusters are a concept introduced in this paper.

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 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 hwloc tool to detect the machine configuration and then partition it into usable clusters.

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

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

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

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 hwloc object, of the type hwloc_obj_type_t. More information can be found in the hwloc documentation.

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.

struct starpu_cluster_machine *clusters;
clusters = starpu_cluster_machine(HWLOC_OBJ_SOCKET, 0);
/* submit some tasks with OpenMP computations */
/* we are back in the default StarPU state */

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.

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 hwloc object (always terminated by the value 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 Creating Custom Clusters.

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 #pragma omp parallel 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).

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:

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
//grab all the ids inside the cluster
starpu_sched_ctx_get_available_cpuids(sched_ctx, &cpuids, &ncpuids);
//set the number of threads
#pragma omp parallel
//bind each threads to its respective resource

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 Scheduling Contexts.

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 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 MKL cluster.

struct starpu_cluster_machine *clusters;
clusters = starpu_cluster_machine(HWLOC_OBJ_SOCKET,

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.

void foo_func(void* foo_arg);
int foo_arg = 0;
struct starpu_cluster_machine *clusters;
clusters = starpu_cluster_machine(HWLOC_OBJ_SOCKET,


Clusters With Scheduling

As previously mentioned, the cluster API is implemented on top of Scheduling Contexts. Its main addition is to ease the creation of a machine CPU partition with no overlapping by using 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.

/* 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 */
/* submit the task to StarPU */

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).

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().