Using the Temanejo task debugger
================================

StarPU can connect to Temanejo (see http://www.hlrs.de/temanejo), to permit nice visual task debugging. To do so, build Temanejo's libayudame.so, install Ayudame.h to e.g. /usr/local/include, apply the tools/patch-ayudame to it to fix C build, re-./configure, 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 libayudame.so.

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 4000000000000000000ULL to be usable for Temanejo (so as to distinguish them from tasks).

On-line performance feedback
============================

Enabling on-line performance monitoring
---------------------------------------

In order to enable online performance monitoring, the application can call starpu_profiling_status_set(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 STARPU_PROFILING environment variable can also be set to 1 to achieve the same effect.

Likewise, performance monitoring is stopped by calling starpu_profiling_status_set(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 section Profiling API.

Per-task feedback
-----------------

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

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

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

Per-codelet feedback
--------------------

The per_worker_stats field of the struct starpu_codelet structure 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.

Per-worker feedback
-------------------

The second argument returned by the starpu_worker_get_profiling_info function is a starpu_worker_profiling_info structure that gives statistics about the specified worker. This structure specifies when StarPU started collecting profiling information for that worker (start_time), the duration of the profiling measurement interval (total_time), the time spent executing kernels (executing_time), the time spent sleeping because there is no task to execute at all (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_worker_get_profiling_info resets the profiling information associated to a worker.

When an FxT trace is generated (see Generating traces), it is also possible to use the starpu_workers_activity script (described in starpu-workers-activity) to generate a graphic showing the evolution of these values during the time, for the different workers.

Bus-related feedback
--------------------

TODO: ajouter STARPU_BUS_STATS

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

     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

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 starpu_top_add_data_boolean, starpu_top_add_data_integer, starpu_top_add_data_float functions, e.g.:

     starpu_top_data *data = starpu_top_add_data_integer("mynum", 0, 100, 1);

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.

     starpu_top_init_and_wait("the application");

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.:

     starpu_top_update_data_integer(data, mynum);

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

     float alpha;
     starpu_top_register_parameter_float("alpha", &alpha, 0, 10, modif_hook);

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

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

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:

     starpu_top_task_prevision(task, workerid, begin, end);

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

  * 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.

  * 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.:

          $ ssh myserver STARPU_SCHED=dmda ./application

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

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

     and "localhost" should be used as IP Address to connect to.

Off-line performance feedback
=============================

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:

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

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

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

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

     $ ./configure --prefix=$FXTDIR
     $ make
     $ make install

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

     $ ./configure --with-fxt=$FXTDIR

Or you can simply point the PKG_CONFIG_PATH to $FXTDIR/lib/pkgconfig and pass --with-fxt to ./configure

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 prof_file_XXX_YYY where XXX is the user name, and YYY is the pid of the process that used StarPU. This file is saved in the /tmp/ directory by default, or by the directory specified by the STARPU_FXT_PREFIX environment variable.

Creating a Gantt Diagram
------------------------

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

     $ starpu_fxt_tool -i filename

Or alternatively, setting the STARPU_GENERATE_TRACE environment variable to 1 before application execution will make StarPU do it automatically at application shutdown.

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

     $ vite paje.trace

To get names of tasks instead of "unknown", fill the optional name field of the codelets, 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 starpu_fxt_tool command, for instance:

     $ starpu_fxt_tool -i filename1 -i filename2

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

Creating a DAG with graphviz
----------------------------

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

     $ starpu_fxt_tool -i filename

This will create a dag.dot 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:

     $ dot -Tpdf dag.dot -o output.pdf

Monitoring activity
-------------------

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

     $ starpu_fxt_tool -i filename

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

     $ starpu_workers_activity activity.data

This will create a file named activity.eps 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 activity.eps picture 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.

Performance of codelets
=======================

The performance model of codelets (described in Performance model example) can be examined by using the starpu_perfmodel_display tool:

     $ 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>

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

     $ 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

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:

     $ 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
     ...

The same can also be achieved by using StarPU's library API, see Performance Model API and notably the starpu_perfmodel_load_symbol function. The source code of the starpu_perfmodel_display tool can be a useful example.

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

When the flops field of tasks is set, starpu_perfmodel_plot can directly draw a GFlops curve, by simply adding the -f option:

     $ starpu_perfmodel_display -f -s chol_model_11

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

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

     $ starpu_fxt_tool -i filename
     $ starpu_codelet_profile distrib.data codelet_name

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

This is also available in the starpu_perfmodel_plot tool, by passing it the fxt trace:

     $ starpu_perfmodel_plot -s non_linear_memset_regression_based -i /tmp/prof_file_foo_0

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

If you have the R statistical tool installed, you can additionally use

     $ starpu_codelet_histo_profile distrib.data

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

Theoretical lower bound on execution time
=========================================

See Theoretical lower bound on execution time for an example on how to use this API. It permits to 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.

 -- Function: void starpu_bound_start (int DEPS, int PRIO)
     Start recording tasks (resets stats).  DEPS tells whether dependencies should be recorded too (this is quite expensive)

 -- Function: void starpu_bound_stop (void)
     Stop recording tasks

 -- Function: void starpu_bound_print_dot (FILE *OUTPUT)
     Print the DAG that was recorded

 -- Function: void starpu_bound_compute (double *RES, double *INTEGER_RES, int INTEGER)
     Get theoretical upper bound (in ms) (needs glpk support detected by configure script). It returns 0 if some performance models are not calibrated.

 -- Function: void starpu_bound_print_lp (FILE *OUTPUT)
     Emit the Linear Programming system on OUTPUT for the recorded tasks, in the lp format

 -- Function: void starpu_bound_print_mps (FILE *OUTPUT)
     Emit the Linear Programming system on OUTPUT for the recorded tasks, in the mps format

 -- Function: void starpu_bound_print (FILE *OUTPUT, int INTEGER)
     Emit statistics of actual execution vs theoretical upper bound. INTEGER permits to choose between integer solving (which takes a long time but is correct), and relaxed solving (which provides an approximate solution).

Memory feedback
===============

It is possible to enable memory statistics. To do so, you need to pass the option --enable-memory-stats when running configure. It is then possible to call the function starpu_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 STARPU_MEMORY_STATS to 0.

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

     $ 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

     $ 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

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 --enable-stats when calling configure. 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 STARPU_STATS to 0.

     $ ./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 %)
     ...

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