StarPU Handbook
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Like any other runtime, StarPU has some overhead to manage tasks. Since it does smart scheduling and data management, this overhead is not always neglectable. The order of magnitude of the overhead is typically a couple of microseconds, which is actually quite smaller than the CUDA overhead itself. The amount of work that a task should do should thus be somewhat bigger, to make sure that the overhead becomes neglectible. The offline performance feedback can provide a measure of task length, which should thus be checked if bad performance are observed. To get a grasp at the scalability possibility according to task size, one can run tests/microbenchs/tasks_size_overhead.sh
which draws curves of the speedup of independent tasks of very small sizes. To determine what task size your application is actually using, one can use starpu_fxt_data_trace
, see Data trace and tasks length .
The choice of scheduler also has impact over the overhead: for instance, the scheduler dmda
takes time to make a decision, while eager
does not. tasks_size_overhead.sh
can again be used to get a grasp at how much impact that has on the target machine.
To let StarPU make online optimizations, tasks should be submitted asynchronously as much as possible. Ideally, all tasks should be submitted, and mere calls to starpu_task_wait_for_all() or starpu_data_unregister() be done to wait for termination. StarPU will then be able to rework the whole schedule, overlap computation with communication, manage accelerator local memory usage, etc.
By default, StarPU will consider the tasks in the order they are submitted by the application. If the application programmer knows that some tasks should be performed in priority (for instance because their output is needed by many other tasks and may thus be a bottleneck if not executed early enough), the field starpu_task::priority should be set to provide the priority information to StarPU.
By default, task dependencies are inferred from data dependency (sequential coherency) by StarPU. The application can however disable sequential coherency for some data, and dependencies can be specifically expressed.
Setting (or unsetting) sequential consistency can be done at the data level by calling starpu_data_set_sequential_consistency_flag() for a specific data or starpu_data_set_default_sequential_consistency_flag() for all datas.
Setting (or unsetting) sequential consistency can also be done at task level by setting the field starpu_task::sequential_consistency to 0
.
Sequential consistency can also be set (or unset) for each handle of a specific task, this is done by using the field starpu_task::handles_sequential_consistency. When set, its value should be a array with the number of elements being the number of handles for the task, each element of the array being the sequential consistency for the i-th
handle of the task. The field can easily be set when calling starpu_task_insert() with the flag STARPU_HANDLES_SEQUENTIAL_CONSISTENCY
The internal algorithm used by StarPU to set up implicit dependency is as follows:
One can explicitely set dependencies between tasks using starpu_task_declare_deps() or starpu_task_declare_deps_array(). Dependencies between tasks can be expressed through tags associated to a tag with the field starpu_task::tag_id and using the function starpu_tag_declare_deps() or starpu_tag_declare_deps_array().
The termination of a task can be delayed through the function starpu_task_end_dep_add() which specifies the number of calls to the function starpu_task_end_dep_release() needed to trigger the task termination. One can also use starpu_task_declare_end_deps() or starpu_task_declare_end_deps_array() to delay the termination of a task until the termination of other tasks.
The maximum number of data a task can manage is fixed by the macro STARPU_NMAXBUFS which has a default value which can be changed through the configure
option --enable-maxbuffers.
However, it is possible to define tasks managing more data by using the field starpu_task::dyn_handles when defining a task and the field starpu_codelet::dyn_modes when defining the corresponding codelet.
The whole code for this complex data interface is available in the file examples/basic_examples/dynamic_handles.c
.
Normally, the number of data handles given to a task is set with starpu_codelet::nbuffers. This field can however be set to STARPU_VARIABLE_NBUFFERS, in which case starpu_task::nbuffers must be set, and starpu_task::modes (or starpu_task::dyn_modes, see Setting Many Data Handles For a Task) should be used to specify the modes for the handles.
One may want to write multiple implementations of a codelet for a single type of device and let StarPU choose which one to run. As an example, we will show how to use SSE to scale a vector. The codelet can be written as follows:
Schedulers which are multi-implementation aware (only dmda
and pheft
for now) will use the performance models of all the provided implementations, and pick the one which seems to be the fastest.
Some implementations may not run on some devices. For instance, some CUDA devices do not support double floating point precision, and thus the kernel execution would just fail; or the device may not have enough shared memory for the implementation being used. The field starpu_codelet::can_execute permits to express this. For instance:
This can be essential e.g. when running on a machine which mixes various models of CUDA devices, to take benefit from the new models without crashing on old models.
Note: the function starpu_codelet::can_execute is called by the scheduler each time it tries to match a task with a worker, and should thus be very fast. The function starpu_cuda_get_device_properties() provides a quick access to CUDA properties of CUDA devices to achieve such efficiency.
Another example is to compile CUDA code for various compute capabilities, resulting with two CUDA functions, e.g. scal_gpu_13
for compute capability 1.3, and scal_gpu_20
for compute capability 2.0. Both functions can be provided to StarPU by using starpu_codelet::cuda_funcs, and starpu_codelet::can_execute can then be used to rule out the scal_gpu_20
variant on a CUDA device which will not be able to execute it:
Another example is having specialized implementations for some given common sizes, for instance here we have a specialized implementation for 1024x1024 matrices:
Note that the most generic variant should be provided first, as some schedulers are not able to try the different variants.
StarPU provides the wrapper function starpu_task_insert() to ease the creation and submission of tasks.
Here the implementation of a codelet:
And the call to the function starpu_task_insert():
The call to starpu_task_insert() is equivalent to the following code:
Here a similar call using STARPU_DATA_ARRAY.
If some part of the task insertion depends on the value of some computation, the macro STARPU_DATA_ACQUIRE_CB can be very convenient. For instance, assuming that the index variable i
was registered as handle A_handle[i]
:
The macro STARPU_DATA_ACQUIRE_CB submits an asynchronous request for acquiring data i
for the main application, and will execute the code given as third parameter when it is acquired. In other words, as soon as the value of i
computed by the codelet which_index
can be read, the portion of code passed as third parameter of STARPU_DATA_ACQUIRE_CB will be executed, and is allowed to read from i
to use it e.g. as an index. Note that this macro is only avaible when compiling StarPU with the compiler gcc
.
StarPU also provides a utility function starpu_codelet_unpack_args() to retrieve the STARPU_VALUE arguments passed to the task. There is several ways of calling this function starpu_codelet_unpack_args().
It may be interesting to get the list of tasks which depend on a given task, notably when using implicit dependencies, since this list is computed by StarPU. starpu_task_get_task_succs() provides it. For instance:
StarPU can leverage existing parallel computation libraries by the means of parallel tasks. A parallel task is a task which is run by a set of CPUs (called a parallel or combined worker) at the same time, by using an existing parallel CPU implementation of the computation to be achieved. This can also be useful to improve the load balance between slow CPUs and fast GPUs: since CPUs work collectively on a single task, the completion time of tasks on CPUs become comparable to the completion time on GPUs, thus relieving from granularity discrepancy concerns. hwloc
support needs to be enabled to get good performance, otherwise StarPU will not know how to better group cores.
Two modes of execution exist to accomodate with existing usages.
In the Fork mode, StarPU will call the codelet function on one of the CPUs of the combined worker. The codelet function can use starpu_combined_worker_get_size() to get the number of threads it is allowed to start to achieve the computation. The CPU binding mask for the whole set of CPUs is already enforced, so that threads created by the function will inherit the mask, and thus execute where StarPU expected, the OS being in charge of choosing how to schedule threads on the corresponding CPUs. The application can also choose to bind threads by hand, using e.g. sched_getaffinity
to know the CPU binding mask that StarPU chose.
For instance, using OpenMP (full source is available in examples/openmp/vector_scal.c
):
Other examples include for instance calling a BLAS parallel CPU implementation (see examples/mult/xgemm.c
).
In the SPMD mode, StarPU will call the codelet function on each CPU of the combined worker. The codelet function can use starpu_combined_worker_get_size() to get the total number of CPUs involved in the combined worker, and thus the number of calls that are made in parallel to the function, and starpu_combined_worker_get_rank() to get the rank of the current CPU within the combined worker. For instance:
Of course, this trivial example will not really benefit from parallel task execution, and was only meant to be simple to understand. The benefit comes when the computation to be done is so that threads have to e.g. exchange intermediate results, or write to the data in a complex but safe way in the same buffer.
To benefit from parallel tasks, a parallel-task-aware StarPU scheduler has to be used. When exposed to codelets with a flag STARPU_FORKJOIN or STARPU_SPMD, the schedulers pheft
(parallel-heft) and peager
(parallel eager) will indeed also try to execute tasks with several CPUs. It will automatically try the various available combined worker sizes (making several measurements for each worker size) and thus be able to avoid choosing a large combined worker if the codelet does not actually scale so much.
This is however for now only proof of concept, and has not really been optimized yet.
By default, StarPU creates combined workers according to the architecture structure as detected by hwloc
. It means that for each object of the hwloc
topology (NUMA node, socket, cache, ...) a combined worker will be created. If some nodes of the hierarchy have a big arity (e.g. many cores in a socket without a hierarchy of shared caches), StarPU will create combined workers of intermediate sizes. The variable STARPU_SYNTHESIZE_ARITY_COMBINED_WORKER permits to tune the maximum arity between levels of combined workers.
The combined workers actually produced can be seen in the output of the tool starpu_machine_display
(the environment variable STARPU_SCHED has to be set to a combined worker-aware scheduler such as pheft
or peager
).
Unfortunately, many environments and librairies do not support concurrent calls.
For instance, most OpenMP implementations (including the main ones) do not support concurrent pragma omp parallel
statements without nesting them in another pragma omp parallel
statement, but StarPU does not yet support creating its CPU workers by using such pragma.
Other parallel libraries are also not safe when being invoked concurrently from different threads, due to the use of global variables in their sequential sections for instance.
The solution is then to use only one combined worker at a time. This can be done by setting the field starpu_conf::single_combined_worker to 1
, or setting the environment variable STARPU_SINGLE_COMBINED_WORKER to 1
. StarPU will then run only one parallel task at a time (but other CPU and GPU tasks are not affected and can be run concurrently). The parallel task scheduler will however still try varying combined worker sizes to look for the most efficient ones.
For the application conveniency, it may be useful to define tasks which do not actually make any computation, but wear for instance dependencies between other tasks or tags, or to be submitted in callbacks, etc.
The obvious way is of course to make kernel functions empty, but such task will thus have to wait for a worker to become ready, transfer data, etc.
A much lighter way to define a synchronization task is to set its starpu_task::cl field to NULL
. The task will thus be a mere synchronization point, without any data access or execution content: as soon as its dependencies become available, it will terminate, call the callbacks, and release dependencies.
An intermediate solution is to define a codelet with its starpu_codelet::where field set to STARPU_NOWHERE, for instance:
will create a task which simply waits for the value of handle
to be available for read. This task can then be depended on, etc.