StarPU Handbook
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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 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
.
It permits to visualize the task graph, add breakpoints, continue execution task-by-task, and run gdb on a given task, etc.
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).
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 STARPU_PROFILING can also be set to 1
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 STARPU_PROFILING is set to 1
.
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 Profiling.
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 timespec
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().
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.
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 Generating Traces With FxT), it is also possible to use the tool starpu_workers_activity
(see Monitoring Activity) to generate a graphic showing the evolution of these values during the time, for the different workers.
TODO: ajouter STARPU_BUS_STATS
The bus speed measured by StarPU can be displayed by using the tool starpu_machine_display
, 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 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.:
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.
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.:
Updateable parameters can be registered thanks to starpu_top_register_parameter_boolean(), starpu_top_register_parameter_integer(), starpu_top_register_parameter_float(), e.g.:
modif_hook
is a function which will be called when the parameter is being modified, it can for instance print the new value:
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:
Starting StarPU-Top (StarPU-Top is started via the binary starpu_top
.) and the application can be done two ways:
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.
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 option --with-fxt :
$ ./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 environment variable STARPU_FXT_PREFIX.
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 environment variable STARPU_GENERATE_TRACE to 1
before application execution will make StarPU do it automatically at application shutdown.
This will create a file paje.trace
in the current directory that can be inspected with the ViTE trace visualizing open-source tool. It is possible to open the file paje.trace
with ViTE by using the following command:
$ vite paje.trace
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 starpu_fxt_tool
, 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
.
Traces can also be inspected by hand by using the tool fxt_print
, for instance:
$ fxt_print -o -f filename
Timings are in nanoseconds (while timings as seen in vite
are in milliseconds).
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
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 a file activity.data
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 picture activity.eps
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.
The performance model of codelets (see PerformanceModelExample) can be examined by using the tool starpu_perfmodel_display
:
$ 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 example lu
are available. We can examine the performance of the kernel 22
(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 and notably the function starpu_perfmodel_load_symbol(). The source code of the tool starpu_perfmodel_display
can be a useful example.
The tool starpu_perfmodel_plot
can be used to draw performance models. It writes a .gp
file in the current directory, to be run with the tool gnuplot
, which shows the corresponding curve.
When the field starpu_task::flops is set, starpu_perfmodel_plot
can directly draw a GFlops curve, by simply adding the -f
option:
$ starpu_perfmodel_plot -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 tool starpu_perfmodel_plot
, 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 statistical tool R
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.
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 R
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:
$ R > install.packages("plyr") > install.packages("ggplot2") > install.packages("data.table") > install.packages("knitr")
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:
$ 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
And one can plot histograms of execution times, of several states for instance:
$ starpu_paje_draw_histogram -n chol_model_11,chol_model_21,chol_model_22 native.trace simgrid.trace
and see the resulting pdf file:
A quick statistical report can be generated by using:
$ starpu_paje_summary native.trace simgrid.trace
it includes gantt charts, execution summaries, as well as state duration charts and time distribution histograms.
Other external Pajé 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):
$ starpu_paje_sort paje.trace
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.
Theoretical Lower Bound On Execution Time Example provides an example on how to use this.
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_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 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
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