/* StarPU --- Runtime system for heterogeneous multicore architectures.
 *
 * Copyright (C) 2011-2013,2015,2017                      Inria
 * Copyright (C) 2010-2019                                CNRS
 * Copyright (C) 2009-2011,2013-2019                      Université de Bordeaux
 *
 * StarPU is free software; you can redistribute it and/or modify
 * it under the terms of the GNU Lesser General Public License as published by
 * the Free Software Foundation; either version 2.1 of the License, or (at
 * your option) any later version.
 *
 * StarPU is distributed in the hope that it will be useful, but
 * WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
 *
 * See the GNU Lesser General Public License in COPYING.LGPL for more details.
 */

/*! \page CheckListWhenPerformanceAreNotThere 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 \ref OfflinePerformanceTools to get a Gantt chart which
will show roughly where time is spent, and focus correspondingly.

\section CheckTaskSize Check Task Size

Make sure that your tasks are not too small, because the StarPU runtime overhead
is not completely zero. You can run the tasks_size_overhead.sh script to get an
idea of the scalability of tasks depending on their duration (in µs), on your
own system.

Typically, 10µs-ish tasks are definitely too small, the CUDA overhead itself is
much bigger than this.

1ms-ish tasks may be a good start, but will not necessarily scale to many dozens
of cores, so it's better to try to get 10ms-ish tasks.

Tasks durations can easily be observed when performance models are defined (see
\ref PerformanceModelExample) by using the <c>starpu_perfmodel_plot</c> or
<c>starpu_perfmodel_display</c> tool (see \ref PerformanceOfCodelets)

When using parallel tasks, the problem is even worse since StarPU has to
synchronize the execution of tasks.

\section ConfigurationImprovePerformance Configuration Which May Improve Performance

The \ref enable-fast "--enable-fast" \c configure 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.


\section DataRelatedFeaturesToImprovePerformance Data Related Features Which May Improve Performance

link to \ref DataManagement

link to \ref DataPrefetch

\section TaskRelatedFeaturesToImprovePerformance Task Related Features Which May Improve Performance

link to \ref TaskGranularity

link to \ref TaskSubmission

link to \ref TaskPriorities

\section SchedulingRelatedFeaturesToImprovePerformance Scheduling Related Features Which May Improve Performance

link to \ref TaskSchedulingPolicy

link to \ref TaskDistributionVsDataTransfer

link to \ref Energy-basedScheduling

link to \ref StaticScheduling

\section CUDA-specificOptimizations CUDA-specific Optimizations

For proper overlapping of asynchronous GPU data transfers, data has to be pinned
by CUDA. Data allocated with starpu_malloc() is always properly pinned. If the
application is registering to StarPU some data which has not been allocated with
starpu_malloc(), it should use starpu_memory_pin() to pin it.

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

as well as the use of \c cudaMemcpyAsync(), etc. for each CUDA operation one needs
to use a version that takes the a stream parameter.

Unfortunately, some CUDA libraries do not have stream variants of
kernels. This will seriously lower the potential for overlapping.
If some CUDA calls are made without specifying this local stream,
synchronization needs to be explicited with cudaThreadSynchronize() around these
calls, to make sure that they get properly synchronized with the calls using
the local stream. Notably, \c cudaMemcpy() and \c cudaMemset() are actually
asynchronous and need such explicit synchronization! Use cudaMemcpyAsync() and
cudaMemsetAsync() instead.

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 <c>cublasSetKernelStream(</c>starpu_cuda_get_local_stream()<c>)</c> 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.  This means setting
the flag ::STARPU_CUDA_ASYNC in the corresponding field starpu_codelet::cuda_flags, and dropping the
<c>cudaStreamSynchronize()</c> call at the end of the <c>cuda_func</c> 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 this stream
with the StarPU-provided stream this way:

\code{.c}
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);
\endcode

This 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 \ref 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.

Concerning memory allocation, you should really not use \c cudaMalloc/ \c cudaFree
within the kernel, since \c cudaFree introduces a awfully lot of synchronizations
within CUDA itself. You should instead add a parameter to the codelet with the
::STARPU_SCRATCH mode access. You can then pass to the task a handle registered
with the desired size but with the \c NULL pointer, that handle can even be the
shared between tasks, StarPU will allocate per-task data on the fly before task
execution, and reuse the allocated data between tasks.

See <c>examples/pi/pi_redux.c</c> for an example of use.

\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 flag ::STARPU_OPENCL_ASYNC in the corresponding field starpu_codelet::opencl_flags and dropping the
<c>clFinish()</c> 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 Detecting 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> (\ref 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,

<c>export STARPU_WATCHDOG_CRASH=1</c> (\ref STARPU_WATCHDOG_CRASH)

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

\section HowToLimitMemoryPerNode 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 \ref STARPU_LIMIT_CUDA_MEM, \ref STARPU_LIMIT_CUDA_devid_MEM,
\ref STARPU_LIMIT_OPENCL_MEM, and \ref 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 \ref 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
\ref OutOfCore, 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 \ref STARPU_MALLOC_COUNT flag, i.e. by calling

\code{.c}
starpu_malloc_flags(STARPU_MALLOC_COUNT)
\endcode

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

\code{.c}
starpu_memory_allocate(STARPU_MEMORY_WAIT)
\endcode

to reserve this amount immediately.

\section HowToReduceTheMemoryFootprintOfInternalDataStructures 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 \c configure step.

To reduce the memory footprint of the data internal structures of StarPU, one
can set the
\ref enable-maxcpus "--enable-maxcpus",
\ref enable-maxnumanodes "--enable-maxnumanodes",
\ref enable-maxcudadev "--enable-maxcudadev",
\ref enable-maxopencldev "--enable-maxopencldev" and
\ref enable-maxnodes "--enable-maxnodes"
\c 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 \ref enable-maxbuffers "--enable-maxbuffers" \c 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 
<c>tests/microbenchs/display_structures_size</c>.

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 \ref STARPU_LIMIT_MIN_SUBMITTED_TASKS and
\ref STARPU_LIMIT_MAX_SUBMITTED_TASKS environment variables, for instance:

<c>
export STARPU_LIMIT_MAX_SUBMITTED_TASKS=10000

export STARPU_LIMIT_MIN_SUBMITTED_TASKS=9000
</c>

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 which 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 \ref STARPU_MAX_MEMORY_USE environment variable to <c>1</c>.

\section HowtoReuseMemory 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 this 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
\ref STARPU_LIMIT_MAX_SUBMITTED_TASKS and \ref 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.

\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.

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
<c>export STARPU_HOSTNAME=some_global_name</c> (\ref STARPU_HOSTNAME), where
<c>some_global_name</c> 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
<c>export STARPU_PERF_MODEL_HOMOGENEOUS_CUDA=1</c> (\ref STARPU_PERF_MODEL_HOMOGENEOUS_CUDA),
<c>export STARPU_PERF_MODEL_HOMOGENEOUS_OPENCL=1</c> (\ref STARPU_PERF_MODEL_HOMOGENEOUS_OPENCL),
<c>export STARPU_PERF_MODEL_HOMOGENEOUS_MIC=1</c> (\ref STARPU_PERF_MODEL_HOMOGENEOUS_MIC),
<c>export STARPU_PERF_MODEL_HOMOGENEOUS_MPI_MS=1</c> (\ref STARPU_PERF_MODEL_HOMOGENEOUS_MPI_MS), or
<c>export STARPU_PERF_MODEL_HOMOGENEOUS_SCC=1</c> (\ref STARPU_PERF_MODEL_HOMOGENEOUS_SCC), depending on your GPU device type.

To force continuing calibration,
use <c>export STARPU_CALIBRATE=1</c> (\ref 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 (<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 tool <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> (\ref 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:

\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 },
    .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> (\ref 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
<c>export STARPU_BUS_STATS=1</c> (\ref STARPU_BUS_STATS).

More detailed profiling information can be enabled by using <c>export STARPU_PROFILING=1</c> (\ref STARPU_PROFILING)
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 in the next chapter.

\section OverheadProfiling Overhead Profiling

\ref OfflinePerformanceTools 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, <c>gprof</c> can be used.

First, recompile and reinstall StarPU with <c>gprof</c> support:

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

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

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

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

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

\code
gprof ./test
\endcode

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

*/
