StarPU Handbook
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Check List When Performance Are Not There

TODO: improve!

To achieve good performance, we give below a list of features which should be checked.

For a start, you can use Offline Performance Tools to get a Gantt chart which will show roughly where time is spent, and focus correspondingly.

Configuration That May Improve Performance

The --enable-fast configuration option disables all assertions. This makes StarPU more performant for really small tasks by disabling all sanity checks. Only use this for measurements and production, not for development, since this will drop all basic checks.

Data Related Features That May Improve Performance

link to Data Management

link to Data Prefetch

Task Related Features That May Improve Performance

link to Task Granularity

link to Task Submission

link to Task Priorities

Scheduling Related Features That May Improve Performance

link to Task Scheduling Policies

link to Task Distribution Vs Data Transfer

link to Energy-based Scheduling

link to Static Scheduling

CUDA-specific Optimizations

Due to CUDA limitations, StarPU will have a hard time overlapping its own communications and the codelet computations if the application does not use a dedicated CUDA stream for its computations instead of the default stream, which synchronizes all operations of the GPU. StarPU provides one by the use of starpu_cuda_get_local_stream() which can be used by all CUDA codelet operations to avoid this issue. For instance:

func <<<grid,block,0,starpu_cuda_get_local_stream()>>> (foo, bar);
cudaStreamSynchronize(starpu_cuda_get_local_stream());

Unfortunately, some CUDA libraries do not have stream variants of kernels. That will lower the potential for overlapping.

Calling starpu_cublas_init() makes StarPU already do appropriate calls for the CUBLAS library. Some libraries like Magma may however change the current stream of CUBLAS v1, one then has to call cublasSetKernelStream(starpu_cuda_get_local_stream()) at the beginning of the codelet to make sure that CUBLAS is really using the proper stream. When using CUBLAS v2, starpu_cublas_get_local_handle() can be called to queue CUBLAS kernels with the proper configuration.

Similarly, calling starpu_cusparse_init() makes StarPU create CUSPARSE handles on each CUDA device, starpu_cusparse_get_local_handle() can then be used to queue CUSPARSE kernels with the proper configuration.

If the kernel can be made to only use this local stream or other self-allocated streams, i.e. the whole kernel submission can be made asynchronous, then one should enable asynchronous execution of the kernel. That means setting the flag STARPU_CUDA_ASYNC in the corresponding field starpu_codelet::cuda_flags, and dropping the cudaStreamSynchronize() call at the end of the cuda_func function, so that it returns immediately after having queued the kernel to the local stream. That way, StarPU will be able to submit and complete data transfers while kernels are executing, instead of only at each kernel submission. The kernel just has to make sure that StarPU can use the local stream to synchronize with the kernel startup and completion.

If the kernel uses its own non-default stream, one can synchronize that stream with the StarPU-provided stream this way:

cudaEvent_t event;
call_kernel_with_its_own_stream()
cudaEventCreateWithFlags(&event, cudaEventDisableTiming);
cudaEventRecord(event, get_kernel_stream());
cudaStreamWaitEvent(starpu_cuda_get_local_stream(), event, 0);
cudaEventDestroy(event);

That code makes the StarPU-provided stream wait for a new event, which will be triggered by the completion of the kernel.

Using the flag STARPU_CUDA_ASYNC also permits to enable concurrent kernel execution, on cards which support it (Kepler and later, notably). This is enabled by setting the environment variable STARPU_NWORKER_PER_CUDA to the number of kernels to execute concurrently. This is useful when kernels are small and do not feed the whole GPU with threads to run.

OpenCL-specific Optimizations

If the kernel can be made to only use the StarPU-provided command queue or other self-allocated queues, i.e. the whole kernel submission can be made asynchronous, then one should enable asynchronous execution of the kernel. This means setting the flag STARPU_OPENCL_ASYNC in the corresponding field starpu_codelet::opencl_flags and dropping the clFinish() and starpu_opencl_collect_stats() calls at the end of the kernel, so that it returns immediately after having queued the kernel to the provided queue. That way, StarPU will be able to submit and complete data transfers while kernels are executing, instead of only at each kernel submission. The kernel just has to make sure that StarPU can use the command queue it has provided to synchronize with the kernel startup and completion.

Detection Stuck Conditions

It may happen that for some reason, StarPU does not make progress for a long period of time. Reason are sometimes due to contention inside StarPU, but sometimes this is due to external reasons, such as stuck MPI driver, or CUDA driver, etc.

export STARPU_WATCHDOG_TIMEOUT=10000 (STARPU_WATCHDOG_TIMEOUT)

allows to make StarPU print an error message whenever StarPU does not terminate any task for 10ms, but lets the application continue normally. In addition to that,

export STARPU_WATCHDOG_CRASH=1 (STARPU_WATCHDOG_CRASH)

raises SIGABRT in that condition, thus allowing to catch the situation in gdb. It can also be useful to type handle SIGABRT nopass in gdb to be able to let the process continue, after inspecting the state of the process.

How to Limit Memory Used By StarPU And Cache Buffer Allocations

By default, StarPU makes sure to use at most 90% of the memory of GPU devices, moving data in and out of the device as appropriate and with prefetch and writeback optimizations. Concerning the main memory, by default it will not limit its consumption, since by default it has nowhere to push the data to when memory gets tight. This also means that by default StarPU will not cache buffer allocations in main memory, since it does not know how much of the system memory it can afford.

In the case of GPUs, the STARPU_LIMIT_CUDA_MEM, STARPU_LIMIT_CUDA_devid_MEM, STARPU_LIMIT_OPENCL_MEM, and STARPU_LIMIT_OPENCL_devid_MEM environment variables can be used to control how much (in MiB) of the GPU device memory should be used at most by StarPU (their default values are 90% of the available memory).

In the case of the main memory, the STARPU_LIMIT_CPU_MEM environment variable can be used to specify how much (in MiB) of the main memory should be used at most by StarPU for buffer allocations. This way, StarPU will be able to cache buffer allocations (which can be a real benefit if a lot of bufferes are involved, or if allocation fragmentation can become a problem), and when using Out Of Core, StarPU will know when it should evict data out to the disk.

It should be noted that by default only buffer allocations automatically done by StarPU are accounted here, i.e. allocations performed through starpu_malloc_on_node() which are used by the data interfaces (matrix, vector, etc.). This does not include allocations performed by the application through e.g. malloc(). It does not include allocations performed through starpu_malloc() either, only allocations performed explicitly with the STARPU_MALLOC_COUNT flag, i.e. by calling

are taken into account. If the application wants to make StarPU aware of its own allocations, so that StarPU knows precisely how much data is allocated, and thus when to evict allocation caches or data out to the disk, starpu_memory_allocate() can be used to specify an amount of memory to be accounted for. starpu_memory_deallocate() can be used to account freed memory back. Those can for instance be used by data interfaces with dynamic data buffers: instead of using starpu_malloc_on_node(), they would dynamically allocate data with malloc/realloc, and notify starpu of the delta thanks to starpu_memory_allocate() and starpu_memory_deallocate() calls.

starpu_memory_get_total() and starpu_memory_get_available() can be used to get an estimation of how much memory is available. starpu_memory_wait_available() can also be used to block until an amount of memory becomes available, but it may be preferrable to call

to reserve that amount immediately.

How To Reduce The Memory Footprint Of Internal Data Structures

It is possible to reduce the memory footprint of the task and data internal structures of StarPU by describing the shape of your machine and/or your application at the configure step.

To reduce the memory footprint of the data internal structures of StarPU, one can set the --enable-maxcpus, --enable-maxcudadev, --enable-maxopencldev and --enable-maxnodes configure parameters to give StarPU the architecture of the machine it will run on, thus tuning the size of the structures to the machine.

To reduce the memory footprint of the task internal structures of StarPU, one can set the --enable-maxbuffers configure parameter to give StarPU the maximum number of buffers that a task can use during an execution. For example, in the Cholesky factorization (dense linear algebra application), the GEMM task uses up to 3 buffers, so it is possible to set the maximum number of task buffers to 3 to run a Cholesky factorization on StarPU.

The size of the various structures of StarPU can be printed by tests/microbenchs/display_structures_size.

It is also often useless to submit *all* the tasks at the same time. One can make the starpu_task_submit() function block when a reasonable given number of tasks have been submitted, by setting the STARPU_LIMIT_MIN_SUBMITTED_TASKS and STARPU_LIMIT_MAX_SUBMITTED_TASKS environment variables, for instance:

export STARPU_LIMIT_MAX_SUBMITTED_TASKS=10000

export STARPU_LIMIT_MIN_SUBMITTED_TASKS=9000

To make StarPU block submission when 10000 tasks are submitted, and unblock submission when only 9000 tasks are still submitted, i.e. 1000 tasks have completed among the 10000 that were submitted when submission was blocked. Of course this may reduce parallelism if the threshold is set too low. The precise balance depends on the application task graph.

An idea of how much memory is used for tasks and data handles can be obtained by setting the STARPU_MAX_MEMORY_USE environment variable to 1.

How To Reuse Memory

When your application needs to allocate more data than the available amount of memory usable by StarPU (given by starpu_memory_get_available()), the allocation cache system can reuse data buffers used by previously executed tasks. For that system to work with MPI tasks, you need to submit tasks progressively instead of as soon as possible, because in the case of MPI receives, the allocation cache check for reusing data buffers will be done at submission time, not at execution time.

You have two options to control the task submission flow. The first one is by controlling the number of submitted tasks during the whole execution. This can be done whether by setting the environment variables STARPU_LIMIT_MAX_SUBMITTED_TASKS and STARPU_LIMIT_MIN_SUBMITTED_TASKS to tell StarPU when to stop submitting tasks and when to wake up and submit tasks again, or by explicitely calling starpu_task_wait_for_n_submitted() in your application code for finest grain control (for example, between two iterations of a submission loop).

The second option is to control the memory size of the allocation cache. This can be done in the application by using jointly starpu_memory_get_available() and starpu_memory_wait_available() to submit tasks only when there is enough memory space to allocate the data needed by the task, i.e when enough data are available for reuse in the allocation cache.

Performance Model Calibration

Most schedulers are based on an estimation of codelet duration on each kind of processing unit. For this to be possible, the application programmer needs to configure a performance model for the codelets of the application (see Performance Model Example for instance). History-based performance models use on-line calibration. StarPU will automatically calibrate codelets which have never been calibrated yet, and save the result in $STARPU_HOME/.starpu/sampling/codelets. The models are indexed by machine name.

By default, StarPU stores separate performance models according to the hostname of the system. To avoid having to calibrate performance models for each node of a homogeneous cluster for instance, the model can be shared by using export STARPU_HOSTNAME=some_global_name (STARPU_HOSTNAME), where some_global_name is the name of the cluster for instance, which thus overrides the hostname of the system.

By default, StarPU stores separate performance models for each GPU. To avoid having to calibrate performance models for each GPU of a homogeneous set of GPU devices for instance, the model can be shared by setting export STARPU_PERF_MODEL_HOMOGENEOUS_CUDA=1 , export STARPU_PERF_MODEL_HOMOGENEOUS_OPENCL=1 , export STARPU_PERF_MODEL_HOMOGENEOUS_MIC=1 , or export STARPU_PERF_MODEL_HOMOGENEOUS_SCC=1 (depending on your GPU device type).

To force continuing calibration, use export STARPU_CALIBRATE=1 (STARPU_CALIBRATE). This may be necessary if your application has not-so-stable performance. StarPU will force calibration (and thus ignore the current result) until 10 (_STARPU_CALIBRATION_MINIMUM) measurements have been made on each architecture, to avoid badly scheduling tasks just because the first measurements were not so good. Details on the current performance model status can be obtained from the tool starpu_perfmodel_display: the -l option lists the available performance models, and the -s option permits to choose the performance model to be displayed. The result looks like:

$ starpu_perfmodel_display -s starpu_slu_lu_model_11
performance model for cpu_impl_0
# hash    size     flops         mean          dev           n
914f3bef  1048576  0.000000e+00  2.503577e+04  1.982465e+02  8
3e921964  65536    0.000000e+00  5.527003e+02  1.848114e+01  7
e5a07e31  4096     0.000000e+00  1.717457e+01  5.190038e+00  14
...

Which shows that for the LU 11 kernel with a 1MiB matrix, the average execution time on CPUs was about 25ms, with a 0.2ms standard deviation, over 8 samples. It is a good idea to check this before doing actual performance measurements.

A graph can be drawn by using the tool starpu_perfmodel_plot:

$ starpu_perfmodel_plot -s starpu_slu_lu_model_11
4096 16384 65536 262144 1048576 4194304 
$ gnuplot starpu_starpu_slu_lu_model_11.gp
$ gv starpu_starpu_slu_lu_model_11.eps
starpu_starpu_slu_lu_model_11.png

If a kernel source code was modified (e.g. performance improvement), the calibration information is stale and should be dropped, to re-calibrate from start. This can be done by using export STARPU_CALIBRATE=2 (STARPU_CALIBRATE).

Note: history-based performance models get calibrated only if a performance-model-based scheduler is chosen.

The history-based performance models can also be explicitly filled by the application without execution, if e.g. the application already has a series of measurements. This can be done by using starpu_perfmodel_update_history(), for instance:

static struct starpu_perfmodel perf_model = {
.symbol = "my_perfmodel",
};
struct starpu_codelet cl = {
.cuda_funcs = { cuda_func1, cuda_func2 },
.nbuffers = 1,
.modes = {STARPU_W},
.model = &perf_model
};
void feed(void) {
struct my_measure *measure;
struct starpu_task task;
task.cl = &cl;
for (measure = &measures[0]; measure < measures[last]; measure++) {
starpu_vector_data_register(&handle, -1, 0, measure->size, sizeof(float));
task.handles[0] = handle;
starpu_perfmodel_update_history(&perf_model, &task,
STARPU_CUDA_DEFAULT + measure->cudadev, 0,
measure->implementation, measure->time);
}
}

Measurement has to be provided in milliseconds for the completion time models, and in Joules for the energy consumption models.

Profiling

A quick view of how many tasks each worker has executed can be obtained by setting export STARPU_WORKER_STATS=1 (STARPU_WORKER_STATS). This is a convenient way to check that execution did happen on accelerators, without penalizing performance with the profiling overhead.

A quick view of how much data transfers have been issued can be obtained by setting export STARPU_BUS_STATS=1 (STARPU_BUS_STATS).

More detailed profiling information can be enabled by using export STARPU_PROFILING=1 (STARPU_PROFILING) or by calling starpu_profiling_status_set() from the source code. Statistics on the execution can then be obtained by using export STARPU_BUS_STATS=1 and export STARPU_WORKER_STATS=1 . More details on performance feedback are provided in the next chapter.

Overhead Profiling

Offline Performance Tools can already provide an idea of to what extent and which part of StarPU bring overhead on the execution time. To get a more precise analysis of the parts of StarPU which bring most overhead, gprof can be used.

First, recompile and reinstall StarPU with gprof support:

./configure --enable-perf-debug --disable-shared --disable-build-tests --disable-build-examples

Make sure not to leave a dynamic version of StarPU in the target path: remove any remaining libstarpu-*.so

Then relink your application with the static StarPU library, make sure that running ldd on your application does not mention any libstarpu (i.e. it's really statically-linked).

gcc test.c -o test $(pkg-config --cflags starpu-1.3) $(pkg-config --libs starpu-1.3)

Now you can run your application, and a gmon.out file should appear in the current directory, you can process it by running gprof on your application:

gprof ./test

That will dump an analysis of the time spent in StarPU functions.