starpu-max/doc/doxygen/chapters/380_offline_performance_tools.doxy

/* StarPU --- Runtime system for heterogeneous multicore architectures.
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/*! \page OfflinePerformanceTools Offline Performance Tools

To get an idea of what is happening, a lot of performance feedback is available,
detailed in this chapter. The various informations should be checked for.

<ul>
<li>
What does the Gantt diagram look like? (see \ref CreatingAGanttDiagram)
<ul>
  <li> If it's mostly green (tasks running in the initial context) or context specific
  color prevailing, then the machine is properly
  utilized, and perhaps the codelets are just slow. Check their performance, see
  \ref PerformanceOfCodelets.
  </li>
  <li> If it's mostly purple (FetchingInput), tasks keep waiting for data
  transfers, do you perhaps have far more communication than computation? Did
  you properly use CUDA streams to make sure communication can be
  overlapped? Did you use data-locality aware schedulers to avoid transfers as
  much as possible?
  </li>
  <li> If it's mostly red (Blocked), tasks keep waiting for dependencies,
  do you have enough parallelism? It might be a good idea to check what the DAG
  looks like (see \ref CreatingADAGWithGraphviz).
  </li>
  <li> If only some workers are completely red (Blocked), for some reason the
  scheduler didn't assign tasks to them. Perhaps the performance model is bogus,
  check it (see \ref PerformanceOfCodelets). Do all your codelets have a
  performance model?  When some of them don't, the schedulers switches to a
  greedy algorithm which thus performs badly.
  </li>
</ul>
</li>
</ul>

You can also use the Temanejo task debugger (see \ref UsingTheTemanejoTaskDebugger) to
visualize the task graph more easily.
\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 get a tarball from http://download.savannah.gnu.org/releases/fkt/

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.

The additional \c configure option \ref enable-fxt-lock "--enable-fxt-lock" can
be used to generate trace events which describes the locks behaviour during
the execution. It is however very heavy and should not be used unless debugging
StarPU's internal locking.

The environment variable \ref STARPU_FXT_TRACE can be set to 0 to disable the
generation of the <c>prof_file_XXX_YYY</c> file.

When the FxT trace file <c>prof_file_something</c> has been generated,
it is possible to generate different trace formats by calling:

\verbatim
$ starpu_fxt_tool -i /tmp/prof_file_something
\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.

One can also set the environment variable \ref
STARPU_GENERATE_TRACE_OPTIONS to specify options, see
<c>starpu_fxt_tool --help</c>, for example:

\verbatim
$ export STARPU_GENERATE_TRACE=1
$ export STARPU_GENERATE_TRACE_OPTIONS="-no-acquire"
\endverbatim

When running a MPI application, \ref STARPU_GENERATE_TRACE will not
work as expected (each node will try to generate trace files, thus
mixing outputs...), you have to 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 /tmp/prof_file_something*
\endverbatim

By default, the generated trace contains all informations. To reduce
the trace size, various <c>-no-foo</c> options can be passed to
<c>starpu_fxt_tool</c>, see <c>starpu_fxt_tool --help</c> .

\subsubsection CreatingAGanttDiagram Creating a Gantt Diagram

One of the generated files is a trace in the Paje format. The file,
located in the current directory, is named <c>paje.trace</c>. It can
be viewed with ViTE (http://vite.gforge.inria.fr/) a trace
visualizing open-source tool.  To open the file <c>paje.trace</c> with
ViTE, use the following command:

\verbatim
$ vite paje.trace
\endverbatim

Tasks can be assigned a name (instead of the default \c unknown) by
filling the optional starpu_codelet::name, or assigning them a
performance model. The name can also be set with the field
starpu_task::name or by using \ref STARPU_NAME when calling
starpu_task_insert().

Tasks are assigned default colors based on the worker which executed
them (green for CPUs, yellow/orange/red for CUDAs, blue for OpenCLs,
red for MICs, ...). To use a different color for every type of task,
one can specify the option <c>-c</c> to <c>starpu_fxt_tool</c> or in
\ref STARPU_GENERATE_TRACE_OPTIONS. Tasks can also be given a specific
color by setting the field starpu_codelet::color or the
starpu_task::color. Colors are expressed with the following format
\c 0xRRGGBB (e.g \c 0xFF0000 for red). See
<c>basic_examples/task_insert_color</c> for examples on how to assign
colors.

To get statistics on the time spend in runtime overhead, one can use the
statistics plugin of ViTE. In Preferences, select Plugins. In "States Type",
select "Worker State". Then click on "Reload" to update the histogram. The red
"Idle" percentages are due to lack of parallelism, while the brown "Overhead"
and "Scheduling" percentages are due to the overhead of the runtime and of the
scheduler.

To identify tasks precisely, the application can also set the field
starpu_task::tag_id or setting \ref STARPU_TAG_ONLY when calling
starpu_task_insert(). The value of the tag will then show up in the
trace.

One can also introduce user-defined events in the diagram thanks to the
starpu_fxt_trace_user_event_string() function.

One can also set the iteration number, by just calling starpu_iteration_push()
at the beginning of submission loops and starpu_iteration_pop() at the end of
submission loops. These iteration numbers will show up in traces for all tasks
submitted from there.

Coordinates can also be given to data with the starpu_data_set_coordinates() or
starpu_data_set_coordinates_array() function. In the trace, tasks will then be
assigned the coordinates of the first data they write to.

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

\verbatim
$ fxt_print -o -f /tmp/prof_file_something
\endverbatim

Timings are in nanoseconds (while timings as seen in ViTE are in milliseconds).

\subsubsection CreatingADAGWithGraphviz Creating a DAG With Graphviz

Another generated trace file is a task graph described using the DOT
language. The file, created in the current directory, is named
<c>dag.dot</c> file in the current directory.
It is possible to get a graphical output of the graph by using the
<c>graphviz</c> library:


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

\subsubsection TraceTaskDetails Getting Task Details

Another generated trace file gives details on the executed tasks. The
file, created in the current directory, is named <c>tasks.rec</c>. This file
is in the recutils format, i.e. <c>Field: value</c> lines, and empty lines are used to
separate each task.  This can be used as a convenient input for various ad-hoc
analysis tools. By default it only contains information about the actual
execution. Performance models can be obtained by running
<c>starpu_tasks_rec_complete</c> on it:

\verbatim
$ starpu_tasks_rec_complete tasks.rec tasks2.rec
\endverbatim

which will add <c>EstimatedTime</c> lines which contain the performance
model-estimated time (in µs) for each worker starting from 0. Since it needs
the performance models, it needs to be run the same way as the application
execution, or at least with <c>STARPU_HOSTNAME</c> set to the hostname of the
machine used for execution, to get the performance models of that machine.

Another possibility is to obtain the performance models as an auxiliary <c>perfmodel.rec</c> file, by using the <c>starpu_perfmodel_recdump</c> utility:

\verbatim
$ starpu_perfmodel_recdump tasks.rec -o perfmodel.rec
\endverbatim

\subsubsection TraceSchedTaskDetails Getting Scheduling Task Details

The file, <c>sched_tasks.rec</c>, created in the current directory,
and in the recutils format, gives information about the tasks
scheduling, and lists the push and pop actions of the scheduler. For
each action, it gives the timestamp, the job priority and the job id.
Each action is separated from the next one by empty lines.

\subsubsection MonitoringActivity Monitoring Activity

Another generated trace file is an activity trace. The file, created
in the current directory, is named <c>activity.data</c>. 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.

\subsubsection Animation Getting Modular Schedular Animation

When using modular schedulers (i.e. schedulers which use a modular architecture,
and whose name start with "modular-"), the call to
<c>starpu_fxt_tool</c> will also produce a <c>trace.html</c> file
which can be viewed in a javascript-enabled web browser. It shows the
flow of tasks between the components of the modular scheduler.

\subsubsection TimeBetweenSendRecvDataUse Analyzing Time Between MPI Data Transfer and Use by Tasks

<c>starpu_fxt_tool</c> produces a file called <c>comms.rec</c> which describes all 
MPI communications. The script <c>starpu_send_recv_data_use.py</c> uses this file 
and <c>tasks.rec</c> in order to produce two graphs: the first one shows durations 
between the reception of data and their usage by a task and the second one plots the 
same graph but with elapsed time between send and usage of a data by the sender.

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

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

\subsection LimitingScopeTrace Limiting The Scope Of The Trace

For computing statistics, it is useful to limit the trace to a given portion of
the time of the whole execution. This can be achieved by calling

\code{.c}
starpu_fxt_autostart_profiling(0)
\endcode

before calling starpu_init(), to
prevent tracing from starting immediately. Then

\code{.c}
starpu_fxt_start_profiling();
\endcode

and

\code{.c}
starpu_fxt_stop_profiling();
\endcode

can be used around the portion of code to be traced. This will show up as marks
in the trace, and states of workers will only show up for that portion.

\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.

An XML output can also be printed by using the <c>-x</c> option:
\verbatim
tools/starpu_perfmodel_display -x -s non_linear_memset_regression_based 
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE StarPUPerfmodel SYSTEM "starpu-perfmodel.dtd">
<!-- symbol non_linear_memset_regression_based -->
<!-- All times in us -->
<perfmodel version="45">
  <combination>
    <device type="CPU" id="0" ncores="1"/>
    <implementation id="0">
      <!-- cpu0_impl0 (Comb0) -->
      <!-- time = a size ^b + c -->
      <nl_regression a="5.429195e-04" b="8.654899e-01" c="9.009313e-01"/>
      <entry footprint="a3d3725e" size="4096" flops="0.000000e+00" mean="4.763200e+00" deviation="7.650928e-01" nsample="100"/>
      <entry footprint="870a30aa" size="8192" flops="0.000000e+00" mean="1.827970e+00" deviation="2.037181e-01" nsample="100"/>
      <entry footprint="48e988e9" size="16384" flops="0.000000e+00" mean="2.652800e+00" deviation="1.876459e-01" nsample="100"/>
      <entry footprint="961e65d2" size="32768" flops="0.000000e+00" mean="4.255530e+00" deviation="3.518025e-01" nsample="100"/>
    </implementation>
  </combination>
</perfmodel>
\endverbatim

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 (or \ref STARPU_FLOPS is passed to
starpu_task_insert()), <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>prof_file_something</c> has been generated, it is possible to
get a profiling of each codelet by calling:

\verbatim
$ starpu_fxt_tool -i /tmp/prof_file_something
$ starpu_codelet_profile distrib.data codelet_name
\endverbatim

This will create profiling data files, and a <c>distrib.data.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 DataTrace Data trace and tasks length

It is possible to get statistics about tasks length and data size by using :
\verbatim
$ starpu_fxt_data_trace filename [codelet1 codelet2 ... codeletn]
\endverbatim
Where filename is the FxT trace file and codeletX the names of the codelets you
want to profile (if no names are specified, <c>starpu_fxt_data_trace</c> will profile them all).
This will create a file, <c>data_trace.gp</c> which
can be executed to get a <c>.eps</c> image of these results. On the image, each point represents a
task, and each color corresponds to a codelet.

\image html data_trace.png
\image latex data_trace.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 <c>plyr</c>,
<c>ggplot2</c> and <c>data.table</c> packages. If your system
distribution does not have packages for these, one can fetch them from
<c>CRAN</c>:

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

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

One can then get textual or <c>.csv</c> 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

An other way to get statistics of StarPU states (without installing <c>R</c> and
<c>pj_dump</c>) is to use the <c>starpu_trace_state_stats.py</c> script which parses the
generated <c>trace.rec</c> file instead of the <c>paje.trace</c> file. The output is similar
to the previous script but it doesn't need any dependencies.

The different prefixes used in <c>trace.rec</c> are:

\verbatim
E: Event type
N: Event name
C: Event category
W: Worker ID
T: Thread ID
S: Start time
\endverbatim

Here's an example on how to use it:

\verbatim
$ python starpu_trace_state_stats.py trace.rec | column -t -s ","
"Name"		"Count" "Type"	"Duration"
"Callback"       220	Runtime	0.075978
"chol_model_11"  10	Task	565.176
"chol_model_21"  45	Task	9184.828
"chol_model_22"  165	Task	64712.07
\endverbatim

<c>starpu_trace_state_stats.py</c> can also be used to compute the different
efficiencies. Refer to the usage description to show some examples.

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

A quick statistical report can be generated by using:

\verbatim
$ starpu_paje_summary native.trace simgrid.trace
\endverbatim

it includes gantt charts, execution summaries, as well as state duration charts
and time distribution histograms.

Other external Paje analysis tools can be used on these traces, one just needs
to sort the traces by timestamp order (which not guaranteed to make recording
more efficient):

\verbatim
$ starpu_paje_sort paje.trace
\endverbatim

\section PapiCounters PAPI counters

Performance counter values can be obtained from the PAPI framework if
<c>./configure</c> detected the libpapi. One has to set the \ref STARPU_PROFILING
environment variable to 1 and then specify which counters to record with the
\ref STARPU_PROF_PAPI_EVENTS environment variable. For instance:

\verbatim
export STARPU_PROFILING=1 STARPU_PROF_PAPI_EVENTS="PAPI_TOT_INS PAPI_TOT_CYC"
\endverbatim

\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 TheoreticalLowerBoundOnExecutionTimeExample Theoretical Lower Bound On Execution Time Example

For kernels with history-based performance models (and provided that
they are completely calibrated), StarPU can very easily provide a
theoretical lower bound for the execution time of a whole set of
tasks. See for instance <c>examples/lu/lu_example.c</c>: before
submitting tasks, call the function starpu_bound_start(), and after
complete execution, call starpu_bound_stop().
starpu_bound_print_lp() or starpu_bound_print_mps() can then be used
to output a Linear Programming problem corresponding to the schedule
of your tasks. Run it through <c>lp_solve</c> or any other linear
programming solver, and that will give you a lower bound for the total
execution time of your tasks. If StarPU was compiled with the library
<c>glpk</c> installed, starpu_bound_compute() can be used to solve it
immediately and get the optimized minimum, in ms. Its parameter
<c>integer</c> allows to decide whether integer resolution should be
computed and returned

The <c>deps</c> parameter tells StarPU whether to take tasks, implicit
data, and tag dependencies into account. Tags released in a callback
or similar are not taken into account, only tags associated with a task are.
It must be understood that the linear programming
problem size is quadratic with the number of tasks and thus the time to solve it
will be very long, it could be minutes for just a few dozen tasks. You should
probably use <c>lp_solve -timeout 1 test.pl -wmps test.mps</c> to convert the
problem to MPS format and then use a better solver, <c>glpsol</c> might be
better than <c>lp_solve</c> for instance (the <c>--pcost</c> option may be
useful), but sometimes doesn't manage to converge. <c>cbc</c> might look
slower, but it is parallel. For <c>lp_solve</c>, be sure to try at least all the
<c>-B</c> options. For instance, we often just use <c>lp_solve -cc -B1 -Bb
-Bg -Bp -Bf -Br -BG -Bd -Bs -BB -Bo -Bc -Bi</c> , and the <c>-gr</c> option can
also be quite useful. The resulting schedule can be observed by using
the tool <c>starpu_lp2paje</c>, which converts it into the Paje
format.

Data transfer time can only be taken into account when <c>deps</c> is set. Only
data transfers inferred from implicit data dependencies between tasks are taken
into account. Other data transfers are assumed to be completely overlapped.

Setting <c>deps</c> to 0 will only take into account the actual computations
on processing units. It however still properly takes into account the varying
performances of kernels and processing units, which is quite more accurate than
just comparing StarPU performances with the fastest of the kernels being used.

The <c>prio</c> parameter tells StarPU whether to simulate taking into account
the priorities as the StarPU scheduler would, i.e. schedule prioritized
tasks before less prioritized tasks, to check to which extend this results
to a less optimal solution. This increases even more computation time.

\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, by adding a call to the function
starpu_data_display_memory_stats() in the fblock example before
unpartitioning the data, one will get something
similar to:

\verbatim
$ STARPU_MEMORY_STATS=1 ./examples/filters/fblock
...
#---------------------
Memory stats :
#-------
Data on Node #2
#-----
Data : 0x5562074e8670
Size : 144

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

Node #2
	Direct access : 0
	Loaded (Owner) : 1
	Loaded (Shared) : 0
	Invalidated (was Owner) : 0

#-------
Data on Node #3
#-----
Data : 0x5562074e9338
Size : 96

#--
Data access stats
/!\ Work Underway
Node #0
	Direct access : 0
	Loaded (Owner) : 0
	Loaded (Shared) : 0
	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 define the environment
variable \ref STARPU_ENABLE_STATS. 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

// TODO: data transfer stats are similar to the ones displayed when
// setting STARPU_BUS_STATS

*/