/*
 * This file is part of the StarPU Handbook.
 * Copyright (C) 2009--2011  Universit@'e de Bordeaux 1
 * Copyright (C) 2010, 2011, 2012, 2013  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 PerformanceFeedback Performance Feedback

\section UsingTheTemanejoTaskDebugger Using The Temanejo Task Debugger

StarPU can connect to Temanejo >= 1.0rc2 (see
http://www.hlrs.de/temanejo), to permit
nice visual task debugging. To do so, build Temanejo's <c>libayudame.so</c>,
install <c>Ayudame.h</c> to e.g. <c>/usr/local/include</c>, apply the
<c>tools/patch-ayudame</c> to it to fix C build, re-<c>./configure</c>, make
sure that it found it, rebuild StarPU.  Run the Temanejo GUI, give it the path
to your application, any options you want to pass it, the path to <c>libayudame.so</c>.

Make sure to specify at least the same number of CPUs in the dialog box as your
machine has, otherwise an error will happen during execution. Future versions
of Temanejo should be able to tell StarPU the number of CPUs to use.

Tag numbers have to be below <c>4000000000000000000ULL</c> to be usable for
Temanejo (so as to distinguish them from tasks).

\section On-linePerformanceFeedback On-line Performance Feedback

\subsection EnablingOn-linePerformanceMonitoring Enabling On-line Performance Monitoring

In order to enable online performance monitoring, the application can
call starpu_profiling_status_set() with the parameter
::STARPU_PROFILING_ENABLE. It is possible to detect whether monitoring
is already enabled or not by calling starpu_profiling_status_get().
Enabling monitoring also reinitialize all previously collected
feedback. The environment variable \ref STARPU_PROFILING can also be
set to <c>1</c> to achieve the same effect. The function
starpu_profiling_init() can also be called during the execution to
reinitialize performance counters and to start the profiling if the
environment variable \ref STARPU_PROFILING is set to <c>1</c>.

Likewise, performance monitoring is stopped by calling
starpu_profiling_status_set() with the parameter
::STARPU_PROFILING_DISABLE. Note that this does not reset the
performance counters so that the application may consult them later
on.

More details about the performance monitoring API are available in \ref API_Profiling.

\subsection Per-taskFeedback Per-task Feedback

If profiling is enabled, a pointer to a structure
starpu_profiling_task_info is put in the field
starpu_task::profiling_info when a task terminates. This structure is
automatically destroyed when the task structure is destroyed, either
automatically or by calling starpu_task_destroy().

The structure starpu_profiling_task_info indicates the date when the
task was submitted (starpu_profiling_task_info::submit_time), started
(starpu_profiling_task_info::start_time), and terminated
(starpu_profiling_task_info::end_time), relative to the initialization
of StarPU with starpu_init(). It also specifies the identifier of the worker
that has executed the task (starpu_profiling_task_info::workerid).
These date are stored as <c>timespec</c> structures which the user may convert
into micro-seconds using the helper function
starpu_timing_timespec_to_us().

It it worth noting that the application may directly access this structure from
the callback executed at the end of the task. The structure starpu_task
associated to the callback currently being executed is indeed accessible with
the function starpu_task_get_current().

\subsection Per-codeletFeedback Per-codelet Feedback

The field starpu_codelet::per_worker_stats is
an array of counters. The i-th entry of the array is incremented every time a
task implementing the codelet is executed on the i-th worker.
This array is not reinitialized when profiling is enabled or disabled.

\subsection Per-workerFeedback Per-worker Feedback

The second argument returned by the function
starpu_profiling_worker_get_info() is a structure
starpu_profiling_worker_info that gives statistics about the specified
worker. This structure specifies when StarPU started collecting
profiling information for that worker
(starpu_profiling_worker_info::start_time), the
duration of the profiling measurement interval
(starpu_profiling_worker_info::total_time), the time spent executing
kernels (starpu_profiling_worker_info::executing_time), the time
spent sleeping because there is no task to execute at all
(starpu_profiling_worker_info::sleeping_time), and the number of tasks that were executed
while profiling was enabled. These values give an estimation of the
proportion of time spent do real work, and the time spent either
sleeping because there are not enough executable tasks or simply
wasted in pure StarPU overhead.

Calling starpu_profiling_worker_get_info() resets the profiling
information associated to a worker.

When an FxT trace is generated (see \ref GeneratingTracesWithFxT), it is also
possible to use the tool <c>starpu_workers_activity</c> (see \ref
MonitoringActivity) to generate a graphic showing the evolution of
these values during the time, for the different workers.

\subsection Bus-relatedFeedback Bus-related Feedback

TODO: ajouter \ref STARPU_BUS_STATS

\internal
how to enable/disable performance monitoring
what kind of information do we get ?
\endinternal

The bus speed measured by StarPU can be displayed by using the tool
<c>starpu_machine_display</c>, for instance:

\verbatim
StarPU has found:
        3 CUDA devices
                CUDA 0 (Tesla C2050 02:00.0)
                CUDA 1 (Tesla C2050 03:00.0)
                CUDA 2 (Tesla C2050 84:00.0)
from    to RAM          to CUDA 0       to CUDA 1       to CUDA 2
RAM     0.000000        5176.530428     5176.492994     5191.710722
CUDA 0  4523.732446     0.000000        2414.074751     2417.379201
CUDA 1  4523.718152     2414.078822     0.000000        2417.375119
CUDA 2  4534.229519     2417.069025     2417.060863     0.000000
\endverbatim

\subsection StarPU-TopInterface StarPU-Top Interface

StarPU-Top is an interface which remotely displays the on-line state of a StarPU
application and permits the user to change parameters on the fly.

Variables to be monitored can be registered by calling the functions
starpu_top_add_data_boolean(), starpu_top_add_data_integer(),
starpu_top_add_data_float(), e.g.:

\code{.c}
starpu_top_data *data = starpu_top_add_data_integer("mynum", 0, 100, 1);
\endcode

The application should then call starpu_top_init_and_wait() to give its name
and wait for StarPU-Top to get a start request from the user. The name is used
by StarPU-Top to quickly reload a previously-saved layout of parameter display.

\code{.c}
starpu_top_init_and_wait("the application");
\endcode

The new values can then be provided thanks to
starpu_top_update_data_boolean(), starpu_top_update_data_integer(),
starpu_top_update_data_float(), e.g.:

\code{.c}
starpu_top_update_data_integer(data, mynum);
\endcode

Updateable parameters can be registered thanks to starpu_top_register_parameter_boolean(), starpu_top_register_parameter_integer(), starpu_top_register_parameter_float(), e.g.:

\code{.c}
float alpha;
starpu_top_register_parameter_float("alpha", &alpha, 0, 10, modif_hook);
\endcode

<c>modif_hook</c> is a function which will be called when the parameter is being modified, it can for instance print the new value:

\code{.c}
void modif_hook(struct starpu_top_param *d) {
    fprintf(stderr,"%s has been modified: %f\n", d->name, alpha);
}
\endcode

Task schedulers should notify StarPU-Top when it has decided when a task will be
scheduled, so that it can show it in its Gantt chart, for instance:

\code{.c}
starpu_top_task_prevision(task, workerid, begin, end);
\endcode

Starting StarPU-Top (StarPU-Top is started via the binary
<c>starpu_top</c>.) and the application can be done two ways:

<ul>
<li> The application is started by hand on some machine (and thus already
waiting for the start event). In the Preference dialog of StarPU-Top, the SSH
checkbox should be unchecked, and the hostname and port (default is 2011) on
which the application is already running should be specified. Clicking on the
connection button will thus connect to the already-running application.
</li>
<li> StarPU-Top is started first, and clicking on the connection button will
start the application itself (possibly on a remote machine). The SSH checkbox
should be checked, and a command line provided, e.g.:

\verbatim
$ ssh myserver STARPU_SCHED=dmda ./application
\endverbatim

If port 2011 of the remote machine can not be accessed directly, an ssh port bridge should be added:

\verbatim
$ ssh -L 2011:localhost:2011 myserver STARPU_SCHED=dmda ./application
\endverbatim

and "localhost" should be used as IP Address to connect to.
</li>
</ul>

\section Off-linePerformanceFeedback Off-line Performance Feedback

\subsection GeneratingTracesWithFxT Generating Traces With FxT

StarPU can use the FxT library (see
https://savannah.nongnu.org/projects/fkt/) to generate traces
with a limited runtime overhead.

You can either get a tarball:

\verbatim
$ wget http://download.savannah.gnu.org/releases/fkt/fxt-0.2.11.tar.gz
\endverbatim

or use the FxT library from CVS (autotools are required):

\verbatim
$ cvs -d :pserver:anonymous\@cvs.sv.gnu.org:/sources/fkt co FxT
$ ./bootstrap
\endverbatim

Compiling and installing the FxT library in the <c>$FXTDIR</c> path is
done following the standard procedure:

\verbatim
$ ./configure --prefix=$FXTDIR
$ make
$ make install
\endverbatim

In order to have StarPU to generate traces, StarPU should be configured with
the option \ref with-fxt "--with-fxt" :

\verbatim
$ ./configure --with-fxt=$FXTDIR
\endverbatim

Or you can simply point the <c>PKG_CONFIG_PATH</c> to
<c>$FXTDIR/lib/pkgconfig</c> and pass
\ref with-fxt "--with-fxt" to <c>./configure</c>

When FxT is enabled, a trace is generated when StarPU is terminated by calling
starpu_shutdown(). The trace is a binary file whose name has the form
<c>prof_file_XXX_YYY</c> where <c>XXX</c> is the user name, and
<c>YYY</c> is the pid of the process that used StarPU. This file is saved in the
<c>/tmp/</c> directory by default, or by the directory specified by
the environment variable \ref STARPU_FXT_PREFIX.

\subsection CreatingAGanttDiagram Creating a Gantt Diagram

When the FxT trace file <c>filename</c> has been generated, it is possible to
generate a trace in the Paje format by calling:

\verbatim
$ starpu_fxt_tool -i filename
\endverbatim

Or alternatively, setting the environment variable \ref STARPU_GENERATE_TRACE
to <c>1</c> before application execution will make StarPU do it automatically at
application shutdown.

This will create a file <c>paje.trace</c> in the current directory that
can be inspected with the <a href="http://vite.gforge.inria.fr/">ViTE trace
visualizing open-source tool</a>.  It is possible to open the
file <c>paje.trace</c> with ViTE by using the following command:

\verbatim
$ vite paje.trace
\endverbatim

To get names of tasks instead of "unknown", fill the optional
starpu_codelet::name, or use a performance model for them.

In the MPI execution case, collect the trace files from the MPI nodes, and
specify them all on the command <c>starpu_fxt_tool</c>, for instance:

\verbatim
$ starpu_fxt_tool -i filename1 -i filename2
\endverbatim

By default, all tasks are displayed using a green color. To display tasks with
varying colors, pass option <c>-c</c> to <c>starpu_fxt_tool</c>.

Traces can also be inspected by hand by using the tool <c>fxt_print</c>, for instance:

\verbatim
$ fxt_print -o -f filename
\endverbatim

Timings are in nanoseconds (while timings as seen in <c>vite</c> are in milliseconds).

\subsection CreatingADAGWithGraphviz Creating a DAG With Graphviz

When the FxT trace file <c>filename</c> has been generated, it is possible to
generate a task graph in the DOT format by calling:

\verbatim
$ starpu_fxt_tool -i filename
\endverbatim

This will create a <c>dag.dot</c> file in the current directory. This file is a
task graph described using the DOT language. It is possible to get a
graphical output of the graph by using the graphviz library:

\verbatim
$ dot -Tpdf dag.dot -o output.pdf
\endverbatim

\subsection MonitoringActivity Monitoring Activity

When the FxT trace file <c>filename</c> has been generated, it is possible to
generate an activity trace by calling:

\verbatim
$ starpu_fxt_tool -i filename
\endverbatim

This will create a file <c>activity.data</c> in the current
directory. A profile of the application showing the activity of StarPU
during the execution of the program can be generated:

\verbatim
$ starpu_workers_activity activity.data
\endverbatim

This will create a file named <c>activity.eps</c> in the current directory.
This picture is composed of two parts.
The first part shows the activity of the different workers. The green sections
indicate which proportion of the time was spent executed kernels on the
processing unit. The red sections indicate the proportion of time spent in
StartPU: an important overhead may indicate that the granularity may be too
low, and that bigger tasks may be appropriate to use the processing unit more
efficiently. The black sections indicate that the processing unit was blocked
because there was no task to process: this may indicate a lack of parallelism
which may be alleviated by creating more tasks when it is possible.

The second part of the picture <c>activity.eps</c> is a graph showing the
evolution of the number of tasks available in the system during the execution.
Ready tasks are shown in black, and tasks that are submitted but not
schedulable yet are shown in grey.

\section PerformanceOfCodelets Performance Of Codelets

The performance model of codelets (see \ref PerformanceModelExample)
can be examined by using the tool <c>starpu_perfmodel_display</c>:

\verbatim
$ starpu_perfmodel_display -l
file: <malloc_pinned.hannibal>
file: <starpu_slu_lu_model_21.hannibal>
file: <starpu_slu_lu_model_11.hannibal>
file: <starpu_slu_lu_model_22.hannibal>
file: <starpu_slu_lu_model_12.hannibal>
\endverbatim

Here, the codelets of the example <c>lu</c> are available. We can examine the
performance of the kernel <c>22</c> (in micro-seconds), which is history-based:

\verbatim
$ starpu_perfmodel_display -s starpu_slu_lu_model_22
performance model for cpu
# hash      size       mean          dev           n
57618ab0    19660800   2.851069e+05  1.829369e+04  109
performance model for cuda_0
# hash      size       mean          dev           n
57618ab0    19660800   1.164144e+04  1.556094e+01  315
performance model for cuda_1
# hash      size       mean          dev           n
57618ab0    19660800   1.164271e+04  1.330628e+01  360
performance model for cuda_2
# hash      size       mean          dev           n
57618ab0    19660800   1.166730e+04  3.390395e+02  456
\endverbatim

We can see that for the given size, over a sample of a few hundreds of
execution, the GPUs are about 20 times faster than the CPUs (numbers are in
us). The standard deviation is extremely low for the GPUs, and less than 10% for
CPUs.

This tool can also be used for regression-based performance models. It will then
display the regression formula, and in the case of non-linear regression, the
same performance log as for history-based performance models:

\verbatim
$ starpu_perfmodel_display -s non_linear_memset_regression_based
performance model for cpu_impl_0
	Regression : #sample = 1400
	Linear: y = alpha size ^ beta
		alpha = 1.335973e-03
		beta = 8.024020e-01
	Non-Linear: y = a size ^b + c
		a = 5.429195e-04
		b = 8.654899e-01
		c = 9.009313e-01
# hash		size		mean		stddev		n
a3d3725e	4096           	4.763200e+00   	7.650928e-01   	100
870a30aa	8192           	1.827970e+00   	2.037181e-01   	100
48e988e9	16384          	2.652800e+00   	1.876459e-01   	100
961e65d2	32768          	4.255530e+00   	3.518025e-01   	100
...
\endverbatim

The same can also be achieved by using StarPU's library API, see
\ref API_Performance_Model and notably the function
starpu_perfmodel_load_symbol(). The source code of the tool
<c>starpu_perfmodel_display</c> can be a useful example.

The tool <c>starpu_perfmodel_plot</c> can be used to draw performance
models. It writes a <c>.gp</c> file in the current directory, to be
run with the tool <c>gnuplot</c>, which shows the corresponding curve.

\image html starpu_non_linear_memset_regression_based.png
\image latex starpu_non_linear_memset_regression_based.eps "" width=\textwidth

When the field starpu_task::flops is set, <c>starpu_perfmodel_plot</c> can
directly draw a GFlops curve, by simply adding the <c>-f</c> option:

\verbatim
$ starpu_perfmodel_plot -f -s chol_model_11
\endverbatim

This will however disable displaying the regression model, for which we can not
compute GFlops.

\image html starpu_chol_model_11_type.png
\image latex starpu_chol_model_11_type.eps "" width=\textwidth

When the FxT trace file <c>filename</c> has been generated, it is possible to
get a profiling of each codelet by calling:

\verbatim
$ starpu_fxt_tool -i filename
$ starpu_codelet_profile distrib.data codelet_name
\endverbatim

This will create profiling data files, and a <c>.gp</c> file in the current
directory, which draws the distribution of codelet time over the application
execution, according to data input size.

\image html distrib_data.png
\image latex distrib_data.eps "" width=\textwidth

This is also available in the tool <c>starpu_perfmodel_plot</c>, by passing it
the fxt trace:

\verbatim
$ starpu_perfmodel_plot -s non_linear_memset_regression_based -i /tmp/prof_file_foo_0
\endverbatim

It will produce a <c>.gp</c> file which contains both the performance model
curves, and the profiling measurements.

\image html starpu_non_linear_memset_regression_based_2.png
\image latex starpu_non_linear_memset_regression_based_2.eps "" width=\textwidth

If you have the statistical tool <c>R</c> installed, you can additionally use

\verbatim
$ starpu_codelet_histo_profile distrib.data
\endverbatim

Which will create one <c>.pdf</c> file per codelet and per input size, showing a
histogram of the codelet execution time distribution.

\image html distrib_data_histo.png
\image latex distrib_data_histo.eps "" width=\textwidth

\section TraceStatistics Trace statistics

More than just codelet performance, it is interesting to get statistics over all
kinds of StarPU states (allocations, data transfers, etc.). This is particularly
useful to check what may have gone wrong in the accurracy of the simgrid
simulation.

This requires the <c>R</c> statistical tool, with the plyr, ggplot2 and
data.table packages. If your system distribution does not have packages for
these, one can fetch them from CRAN:

\verbatim
$ R
> install.packages("plyr")
> install.packages("ggplot2")
> install.packages("data.table")
\endverbatim

The pj_dump tool from pajeng is also needed (see
https://github.com/schnorr/pajeng)

One can then get textual or .csv statistics over the trace states:

\verbatim
$ starpu_paje_state_stats -v native.trace simgrid.trace
"Value"         "Events_native.csv" "Duration_native.csv" "Events_simgrid.csv" "Duration_simgrid.csv"
"Callback"      220                 0.075978              220                  0
"chol_model_11" 10                  565.176               10                   572.8695
"chol_model_21" 45                  9184.828              45                   9170.719
"chol_model_22" 165                 64712.07              165                  64299.203
$ starpu_paje_state_stats native.trace simgrid.trace
\endverbatim

And one can plot histograms of execution times, of several states for instance:
\verbatim
$ starpu_paje_draw_histogram -n chol_model_11,chol_model_21,chol_model_22 native.trace simgrid.trace
\endverbatim

and see the resulting pdf file:

\image html paje_draw_histogram.png
\image latex paje_draw_histogram.eps "" width=\textwidth

\section TheoreticalLowerBoundOnExecutionTime Theoretical Lower Bound On Execution Time

StarPU can record a trace of what tasks are needed to complete the
application, and then, by using a linear system, provide a theoretical lower
bound of the execution time (i.e. with an ideal scheduling).

The computed bound is not really correct when not taking into account
dependencies, but for an application which have enough parallelism, it is very
near to the bound computed with dependencies enabled (which takes a huge lot
more time to compute), and thus provides a good-enough estimation of the ideal
execution time.

\ref TheoreticalLowerBoundOnExecutionTimeExample provides an example on how to
use this.

\section MemoryFeedback Memory Feedback

It is possible to enable memory statistics. To do so, you need to pass
the option \ref enable-memory-stats "--enable-memory-stats" when running <c>configure</c>. It is then
possible to call the function starpu_data_display_memory_stats() to
display statistics about the current data handles registered within StarPU.

Moreover, statistics will be displayed at the end of the execution on
data handles which have not been cleared out. This can be disabled by
setting the environment variable \ref STARPU_MEMORY_STATS to <c>0</c>.

For example, if you do not unregister data at the end of the complex
example, you will get something similar to:

\verbatim
$ STARPU_MEMORY_STATS=0 ./examples/interface/complex
Complex[0] = 45.00 + 12.00 i
Complex[0] = 78.00 + 78.00 i
Complex[0] = 45.00 + 12.00 i
Complex[0] = 45.00 + 12.00 i
\endverbatim

\verbatim
$ STARPU_MEMORY_STATS=1 ./examples/interface/complex
Complex[0] = 45.00 + 12.00 i
Complex[0] = 78.00 + 78.00 i
Complex[0] = 45.00 + 12.00 i
Complex[0] = 45.00 + 12.00 i

#---------------------
Memory stats:
#-------
Data on Node #3
#-----
Data : 0x553ff40
Size : 16

#--
Data access stats
/!\ Work Underway
Node #0
	Direct access : 4
	Loaded (Owner) : 0
	Loaded (Shared) : 0
	Invalidated (was Owner) : 0

Node #3
	Direct access : 0
	Loaded (Owner) : 0
	Loaded (Shared) : 1
	Invalidated (was Owner) : 0

#-----
Data : 0x5544710
Size : 16

#--
Data access stats
/!\ Work Underway
Node #0
	Direct access : 2
	Loaded (Owner) : 0
	Loaded (Shared) : 1
	Invalidated (was Owner) : 1

Node #3
	Direct access : 0
	Loaded (Owner) : 1
	Loaded (Shared) : 0
	Invalidated (was Owner) : 0
\endverbatim

\section DataStatistics Data Statistics

Different data statistics can be displayed at the end of the execution
of the application. To enable them, you need to pass the option
\ref enable-stats "--enable-stats" when calling <c>configure</c>. When calling
starpu_shutdown() various statistics will be displayed,
execution, MSI cache statistics, allocation cache statistics, and data
transfer statistics. The display can be disabled by setting the
environment variable \ref STARPU_STATS to <c>0</c>.

\verbatim
$ ./examples/cholesky/cholesky_tag
Computation took (in ms)
518.16
Synthetic GFlops : 44.21
#---------------------
MSI cache stats :
TOTAL MSI stats	hit 1622 (66.23 %)	miss 827 (33.77 %)
...
\endverbatim

\verbatim
$ STARPU_STATS=0 ./examples/cholesky/cholesky_tag
Computation took (in ms)
518.16
Synthetic GFlops : 44.21
\endverbatim

\internal
TODO: data transfer stats are similar to the ones displayed when
setting STARPU_BUS_STATS
\endinternal

*/
