/*
* This file is part of the StarPU Handbook.
* Copyright (C) 2009--2011 Universit@'e de Bordeaux 1
* Copyright (C) 2010, 2011, 2012, 2013, 2014 Centre National de la Recherche Scientifique
* Copyright (C) 2011, 2012 Institut National de Recherche en Informatique et Automatique
* See the file version.doxy for copying conditions.
*/
/*! \page CheckListWhenPerformanceAreNotThere Check List When Performance Are Not There
TODO: improve!
Simply encapsulating application kernels into tasks already permits to
seamlessly support CPU and GPUs at the same time. To achieve good
performance, we give below a list of features which should be checked.
\section DataRelatedFeaturesToImprovePerformance Data Related Features That May Improve Performance
link to \ref DataManagement
link to \ref DataPrefetch
\section TaskRelatedFeaturesToImprovePerformance Task Related Features That May Improve Performance
link to \ref TaskGranularity
link to \ref TaskSubmission
link to \ref TaskPriorities
\section SchedulingRelatedFeaturesToImprovePerformance Scheduling Related Features That May Improve Performance
link to \ref TaskSchedulingPolicy
link to \ref TaskDistributionVsDataTransfer
link to \ref Power-basedScheduling
link to \ref StaticScheduling
\section CUDA-specificOptimizations 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:
\code{.c}
func <<<grid,block,0,starpu_cuda_get_local_stream()>>> (foo, bar);
cudaStreamSynchronize(starpu_cuda_get_local_stream());
\endcode
Unfortunately, some CUDA libraries do not have stream variants of
kernels. That will lower the potential for overlapping.
StarPU already does appropriate calls for the CUBLAS library.
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 STARPU_CUDA_ASYNC flag in cuda_flags[] in the codelet, 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.
Using the STARPU_CUDA_ASYNC flag also permits to enable concurrent kernel
execution, on cards which support it (Kepler and later, notably). This is
enabled by setting the STARPU_NWORKER_PER_CUDA environment variable 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.
\section OpenCL-specificOptimizations 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 corresponding opencl_flags[] flag in the codelet 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.
\section DetectionStuckConditions 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.
<c>export STARPU_WATCHDOG_TIMEOUT=10000</c>
allows to make StarPU print an error message whenever StarPU does not terminate
any task for 10ms. In addition to that,
<c>export STARPU_WATCHDOG_CRASH=1</c>
triggers a crash in that condition, thus allowing to catch the situation in gdb
etc.
\section HowToLimitMemoryPerNode How to limit memory per node
TODO
Talk about
\ref STARPU_LIMIT_CUDA_devid_MEM, \ref STARPU_LIMIT_CUDA_MEM,
\ref STARPU_LIMIT_OPENCL_devid_MEM, \ref STARPU_LIMIT_OPENCL_MEM
and \ref STARPU_LIMIT_CPU_MEM
starpu_memory_get_total()
starpu_memory_get_available()
\section PerformanceModelCalibration 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
\ref PerformanceModelExample 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
<c>$STARPU_HOME/.starpu/sampling/codelets</c>.
The models are indexed by machine name. To share the models between
machines (e.g. for a homogeneous cluster), use <c>export
STARPU_HOSTNAME=some_global_name</c>. To force continuing calibration,
use <c>export STARPU_CALIBRATE=1</c> . This may be necessary if your application
has not-so-stable performance. StarPU will force calibration (and thus ignore
the current result) until 10 (<c>_STARPU_CALIBRATION_MINIMUM</c>) 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 command <c>starpu_perfmodel_display</c>: the <c>-l</c>
option lists the available performance models, and the <c>-s</c> option permits
to choose the performance model to be displayed. The result looks like:
\verbatim
$ 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
...
\endverbatim
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 <c>starpu_perfmodel_plot</c>:
\verbatim
$ 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
\endverbatim
\image html starpu_starpu_slu_lu_model_11.png
\image latex starpu_starpu_slu_lu_model_11.eps "" width=\textwidth
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 <c>export STARPU_CALIBRATE=2</c>.
Note: due to CUDA limitations, to be able to measure kernel duration,
calibration mode needs to disable asynchronous data transfers. Calibration thus
disables data transfer / computation overlapping, and should thus not be used
for eventual benchmarks. Note 2: 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:
\code{.c}
static struct starpu_perfmodel perf_model = {
.type = STARPU_HISTORY_BASED,
.symbol = "my_perfmodel",
};
struct starpu_codelet cl = {
.cuda_funcs = { cuda_func1, cuda_func2, NULL },
.nbuffers = 1,
.modes = {STARPU_W},
.model = &perf_model
};
void feed(void) {
struct my_measure *measure;
struct starpu_task task;
starpu_task_init(&task);
task.cl = &cl;
for (measure = &measures[0]; measure < measures[last]; measure++) {
starpu_data_handle_t handle;
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);
starpu_task_clean(&task);
starpu_data_unregister(handle);
}
}
\endcode
Measurement has to be provided in milliseconds for the completion time models,
and in Joules for the energy consumption models.
\section Profiling Profiling
A quick view of how many tasks each worker has executed can be obtained by setting
<c>export STARPU_WORKER_STATS=1</c> 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
<c>export STARPU_BUS_STATS=1</c> .
More detailed profiling information can be enabled by using <c>export STARPU_PROFILING=1</c> or by
calling starpu_profiling_status_set() from the source code.
Statistics on the execution can then be obtained by using <c>export
STARPU_BUS_STATS=1</c> and <c>export STARPU_WORKER_STATS=1</c> .
More details on performance feedback are provided by the next chapter.
*/