starpu-max/doc/doxygen/chapters/api/performance_model.doxy

/*
 * This file is part of the StarPU Handbook.
 * Copyright (C) 2009--2011  Universit@'e de Bordeaux
 * Copyright (C) 2010, 2011, 2012, 2013, 2014, 2015, 2016  CNRS
 * Copyright (C) 2011, 2012, 2016 INRIA
 * See the file version.doxy for copying conditions.
 */

/*! \defgroup API_Performance_Model Performance Model

\enum starpu_perfmodel_type
\ingroup API_Performance_Model
TODO
\var starpu_perfmodel_type::STARPU_PER_ARCH
\ingroup API_Performance_Model
Application-provided per-arch cost model function
\var starpu_perfmodel_type::STARPU_COMMON
\ingroup API_Performance_Model
Application-provided common cost model function, with per-arch factor
\var starpu_perfmodel_type::STARPU_HISTORY_BASED
\ingroup API_Performance_Model
Automatic history-based cost model
\var starpu_perfmodel_type::STARPU_REGRESSION_BASED
\ingroup API_Performance_Model
Automatic linear regression-based cost model  (alpha * size ^ beta)
\var starpu_perfmodel_type::STARPU_NL_REGRESSION_BASED
\ingroup API_Performance_Model
Automatic non-linear regression-based cost model (a * size ^ b + c)
\var starpu_perfmodel_type::STARPU_MULTIPLE_REGRESSION_BASED
\ingroup API_Performance_Model
Automatic multiple linear regression-based cost model. Application provides parameters, their combinations and exponents

\struct starpu_perfmodel_device
todo
\ingroup API_Performance_Model
\var enum starpu_worker_archtype starpu_perfmodel_device::type
is the type of the device
\var int starpu_perfmodel_device::devid
is the identifier of the precise device
\var int starpu_perfmodel_device::ncore
is the number of execution in parallel, minus 1

\struct starpu_perfmodel_arch
todo
\ingroup API_Performance_Model
\var int starpu_perfmodel_arch::ndevices
is the number of the devices for the given arch
\var struct starpu_perfmodel_device *starpu_perfmodel_arch::devices
is the list of the devices for the given arch

\struct starpu_perfmodel
Contains all information about a performance model. At least the
type and symbol fields have to be filled when defining a performance
model for a codelet. For compatibility, make sure to initialize the
whole structure to zero, either by using explicit memset, or by
letting the compiler implicitly do it in e.g. static storage case. If
not provided, other fields have to be zero.
\ingroup API_Performance_Model
\var enum starpu_perfmodel_type starpu_perfmodel::type
is the type of performance model
<ul>
<li>::STARPU_HISTORY_BASED, ::STARPU_REGRESSION_BASED,
::STARPU_NL_REGRESSION_BASED: No other fields needs to be provided,
this is purely history-based.
</li>
<li> ::STARPU_MULTIPLE_REGRESSION_BASED: Need to provide fields starpu_perfmodel::nparameters (number of different parameters),  starpu_perfmodel::ncombinations (number of parameters combinations-tuples) and table starpu_perfmodel::combinations which defines exponents of the equation. Function cl_perf_func also needs to define how to extract parameters from the task.
</li>
<li> ::STARPU_PER_ARCH: either field starpu_perfmodel::arch_cost_function has to be
filled with a function that returns the cost in micro-seconds on the arch given
as parameter, or field starpu_perfmodel::per_arch has to be
filled with functions which return the cost in micro-seconds.
</li>
<li> ::STARPU_COMMON: field starpu_perfmodel::cost_function has to be
filled with a function that returns the cost in micro-seconds on a
CPU, timing on other archs will be determined by multiplying by an
arch-specific factor.
</li>
</ul>
\var const char *starpu_perfmodel::symbol
is the symbol name for the performance model, which will be used as
file name to store the model. It must be set otherwise the model will
be ignored.
\var double (*starpu_perfmodel::cost_function)(struct starpu_task *, unsigned nimpl)
Used by ::STARPU_COMMON: takes a task and implementation number, and
must return a task duration estimation in micro-seconds.
\var double (*starpu_perfmodel::arch_cost_function)(struct starpu_task *, struct starpu_perfmodel_arch* arch, unsigned nimpl)
Used by ::STARPU_COMMON: takes a task, an arch and implementation number, and
must return a task duration estimation in micro-seconds on that arch.
\var size_t (*starpu_perfmodel::size_base)(struct starpu_task *, unsigned nimpl)
Used by ::STARPU_HISTORY_BASED, ::STARPU_REGRESSION_BASED and
::STARPU_NL_REGRESSION_BASED. If not NULL, takes a task and
implementation number, and returns the size to be used as index to distinguish
histories and as a base for regressions.
\var uint32_t (*starpu_perfmodel::footprint)(struct starpu_task *)
Used by ::STARPU_HISTORY_BASED. If not NULL, takes a task and returns the
footprint to be used as index to distinguish histories. The default is to use
the starpu_task_data_footprint function.
\var unsigned starpu_perfmodel::is_loaded
\private
Whether the performance model is already loaded from the disk.
\var unsigned starpu_perfmodel::benchmarking
\private
\var unsigned starpu_perfmodel::is_init
todo
\var starpu_perfmodel_state_t starpu_perfmodel::state
\private
\var const char ** starpu_perfmodel::parameters_names
\private
Names of parameters used for multiple linear regression models (M, N, K)
\var unsigned starpu_perfmodel::nparameters
\private
Number of parameters used for multiple linear regression models
\var unsigned ** starpu_perfmodel::combinations
\private
Table of combinations of parameters (and the exponents) used for multiple linear regression models
\var unsigned starpu_perfmodel::ncombinations
\private
Number of combination of parameters used for multiple linear regression models


\struct starpu_perfmodel_regression_model
...
\ingroup API_Performance_Model
\var double starpu_perfmodel_regression_model::sumlny
sum of ln(measured)
\var double starpu_perfmodel_regression_model::sumlnx
sum of ln(size)
\var double starpu_perfmodel_regression_model::sumlnx2
sum of ln(size)^2
\var unsigned long starpu_perfmodel_regression_model::minx
minimum size
\var unsigned long starpu_perfmodel_regression_model::maxx
maximum size
\var double starpu_perfmodel_regression_model::sumlnxlny
sum of ln(size)*ln(measured)
\var double starpu_perfmodel_regression_model::alpha
estimated = alpha * size ^ beta
\var double starpu_perfmodel_regression_model::beta
estimated = alpha * size ^ beta
\var unsigned starpu_perfmodel_regression_model::valid
whether the linear regression model is valid (i.e. enough measures)
\var double starpu_perfmodel_regression_model::a
estimated = a size ^b + c
\var double starpu_perfmodel_regression_model::b
estimated = a size ^b + c
\var double starpu_perfmodel_regression_model::c
estimated = a size ^b + c
\var unsigned starpu_perfmodel_regression_model::nl_valid
whether the non-linear regression model is valid (i.e. enough measures)
\var unsigned starpu_perfmodel_regression_model::nsample
number of sample values for non-linear regression
\var double starpu_perfmodel_regression_model::coeff[]
list of computed coefficients for multiple linear regression model
\var double starpu_perfmodel_regression_model::ncoeff
number of coefficients for multiple linear regression model
\var double starpu_perfmodel_regression_model::multi_valid
whether the multiple linear regression model is valid

\struct starpu_perfmodel_per_arch
contains information about the performance model of a given
arch.
\ingroup API_Performance_Model
\var starpu_perfmodel_per_arch_cost_function starpu_perfmodel_per_arch::cost_function
Used by ::STARPU_PER_ARCH, must point to functions which take a task,
the target arch and implementation number (as mere conveniency, since
the array is already indexed by these), and must return a task
duration estimation in micro-seconds.
\var starpu_perfmodel_per_arch_size_base starpu_perfmodel_per_arch::size_base
Same as in structure starpu_perfmodel, but per-arch, in case it
depends on the architecture-specific implementation.
\var struct starpu_perfmodel_history_table *starpu_perfmodel_per_arch::history
\private
The history of performance measurements.
\var struct starpu_perfmodel_history_list *starpu_perfmodel_per_arch::list
\private
Used by ::STARPU_HISTORY_BASED, ::STARPU_NL_REGRESSION_BASED and ::STARPU_MULTIPLE_REGRESSION_BASED,
records all execution history measures.
\var struct starpu_perfmodel_regression_model starpu_perfmodel_per_arch::regression
\private
Used by ::STARPU_REGRESSION_BASED, 
::STARPU_NL_REGRESSION_BASED and ::STARPU_MULTIPLE_REGRESSION_BASED, contains the estimated factors of the
regression.

\struct starpu_perfmodel_history_list
todo
\ingroup API_Performance_Model
\var struct starpu_perfmodel_history_list *starpu_perfmodel_history_list::next
todo
\var struct starpu_perfmodel_history_entry *starpu_perfmodel_history_list::entry
todo

\struct starpu_perfmodel_history_entry
todo
\ingroup API_Performance_Model
\var double starpu_perfmodel_history_entry::mean
mean_n = 1/n sum
\var double starpu_perfmodel_history_entry::deviation
n dev_n = sum2 - 1/n (sum)^2
\var double starpu_perfmodel_history_entry::sum
sum of samples (in µs)
\var double starpu_perfmodel_history_entry::sum2
sum of samples^2
\var unsigned starpu_perfmodel_history_entry::nsample
number of samples
\var uint32_t starpu_perfmodel_history_entry::footprint
data footprint
\var size_t starpu_perfmodel_history_entry::size
in bytes
\var double starpu_perfmodel_history_entry::flops
Provided by the application

\fn void starpu_perfmodel_init(struct starpu_perfmodel *model)
\ingroup API_Performance_Model
todo

\fn void starpu_perfmodel_free_sampling_directories(void)
\ingroup API_Performance_Model
this function frees internal memory used for sampling directory
management. It should only be called by an application which is not
calling starpu_shutdown as this function already calls it. See for
example <c>tools/starpu_perfmodel_display.c</c>.

\fn int starpu_perfmodel_load_file(const char *filename, struct starpu_perfmodel *model)
\ingroup API_Performance_Model
loads the performance model found in the given file. The model structure has to be
completely zero, and will be filled with the information stored in the given file.

\fn int starpu_perfmodel_load_symbol(const char *symbol, struct starpu_perfmodel *model)
\ingroup API_Performance_Model
loads a given performance model. The model structure has to be
completely zero, and will be filled with the information saved in
<c>$STARPU_HOME/.starpu</c>. The function is intended to be used by
external tools that should read the performance model files.

\fn int starpu_perfmodel_unload_model(struct starpu_perfmodel *model)
\ingroup API_Performance_Model
unloads the given model which has been previously loaded
through the function starpu_perfmodel_load_symbol()

\fn void starpu_perfmodel_debugfilepath(struct starpu_perfmodel *model, struct starpu_perfmodel_arch *arch, char *path, size_t maxlen, unsigned nimpl)
\ingroup API_Performance_Model
returns the path to the debugging information for the performance model.

\fn char* starpu_perfmodel_get_archtype_name(enum starpu_worker_archtype archtype)
\ingroup API_Performance_Model
todo

\fn void starpu_perfmodel_get_arch_name(struct starpu_perfmodel_arch *arch, char *archname, size_t maxlen, unsigned nimpl)
\ingroup API_Performance_Model
returns the architecture name for \p arch

\fn struct starpu_perfmodel_arch *starpu_worker_get_perf_archtype(int workerid, unsigned sched_ctx_id)
\ingroup API_Performance_Model
returns the architecture type of a given worker.

\fn int starpu_perfmodel_list(FILE *output)
\ingroup API_Performance_Model
prints a list of all performance models on \p output

\fn void starpu_perfmodel_directory(FILE *output)
\ingroup API_Performance_Model
prints the directory name storing performance models on \p output

\fn void starpu_perfmodel_print(struct starpu_perfmodel *model, struct starpu_perfmodel_arch *arch, unsigned nimpl, char *parameter, uint32_t *footprint, FILE *output)
\ingroup API_Performance_Model
todo

\fn int starpu_perfmodel_print_all(struct starpu_perfmodel *model, char *arch, char *parameter, uint32_t *footprint, FILE *output)
\ingroup API_Performance_Model
todo

\fn int starpu_perfmodel_print_estimations(struct starpu_perfmodel *model, uint32_t footprint, FILE *output)
\ingroup API_Performance_Model
todo

\fn void starpu_bus_print_bandwidth(FILE *f)
\ingroup API_Performance_Model
prints a matrix of bus bandwidths on \p f.

\fn void starpu_bus_print_affinity(FILE *f)
\ingroup API_Performance_Model
prints the affinity devices on \p f.

\fn void starpu_bus_print_filenames(FILE *f)
\ingroup API_Performance_Model
prints on \p f the name of the files containing the matrix of bus bandwidths, the affinity devices and the latency.

\fn void starpu_perfmodel_update_history(struct starpu_perfmodel *model, struct starpu_task *task, struct starpu_perfmodel_arch *arch, unsigned cpuid, unsigned nimpl, double measured);
\ingroup API_Performance_Model
This feeds the performance model model with an explicit
measurement measured (in µs), in addition to measurements done by StarPU
itself. This can be useful when the application already has an
existing set of measurements done in good conditions, that StarPU
could benefit from instead of doing on-line measurements. And example
of use can be seen in \ref PerformanceModelExample.

\fn double starpu_transfer_bandwidth(unsigned src_node, unsigned dst_node)
\ingroup API_Performance_Model
Return the bandwidth of data transfer between two memory nodes

\fn double starpu_transfer_latency(unsigned src_node, unsigned dst_node)
\ingroup API_Performance_Model
Return the latency of data transfer between two memory nodes

\fn double starpu_transfer_predict(unsigned src_node, unsigned dst_node, size_t size)
\ingroup API_Performance_Model
Return the estimated time to transfer a given size between two memory nodes.

\fn double starpu_perfmodel_history_based_expected_perf(struct starpu_perfmodel *model, struct starpu_perfmodel_arch* arch, uint32_t footprint)
\ingroup API_Performance_Model
Return the estimated time of a task whose model is named \p and whose footprint is \p footprint

*/