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@@ -1228,16 +1228,16 @@ Partitioning can be applied several times, see
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@section Performance model example
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@section Performance model example
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To achieve good scheduling, StarPU scheduling policies need to be able to
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To achieve good scheduling, StarPU scheduling policies need to be able to
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-estimate in advance the duration of a task. This is done by giving to codelets a
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-performance model. There are several kinds of performance models.
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+estimate in advance the duration of a task. This is done by giving to codelets
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+a performance model, by defining a @code{starpu_perfmodel_t} structure and
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+providing its address in the @code{model} field of the @code{starpu_codelet}
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+structure. The @code{symbol} and @code{type} fields of @code{starpu_perfmodel_t}
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+are mandatory, to give a name to the model, and the type of the model, since
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+there are several kinds of performance models.
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@itemize
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@itemize
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@item
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@item
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-Providing an estimation from the application itself (@code{STARPU_COMMON} model type and @code{cost_model} field),
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-see for instance
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-@code{examples/common/blas_model.h} and @code{examples/common/blas_model.c}. It can also be provided for each architecture (@code{STARPU_PER_ARCH} model type and @code{per_arch} field)
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-@item
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-Measured at runtime (STARPU_HISTORY_BASED model type). This assumes that for a
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+Measured at runtime (@code{STARPU_HISTORY_BASED} model type). This assumes that for a
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given set of data input/output sizes, the performance will always be about the
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given set of data input/output sizes, the performance will always be about the
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same. This is very true for regular kernels on GPUs for instance (<0.1% error),
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same. This is very true for regular kernels on GPUs for instance (<0.1% error),
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and just a bit less true on CPUs (~=1% error). This also assumes that there are
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and just a bit less true on CPUs (~=1% error). This also assumes that there are
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@@ -1277,7 +1277,7 @@ starpu_codelet cl = @{
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@end cartouche
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@end cartouche
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@item
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@item
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-Measured at runtime and refined by regression (STARPU_REGRESSION_*_BASED
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+Measured at runtime and refined by regression (@code{STARPU_REGRESSION_*_BASED}
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model type). This still assumes performance regularity, but can work
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model type). This still assumes performance regularity, but can work
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with various data input sizes, by applying regression over observed
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with various data input sizes, by applying regression over observed
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execution times. STARPU_REGRESSION_BASED uses an a*n^b regression
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execution times. STARPU_REGRESSION_BASED uses an a*n^b regression
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@@ -1287,7 +1287,12 @@ STARPU_REGRESSION_BASED, but costs a lot more to compute). For instance,
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model for the @code{memset} operation.
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model for the @code{memset} operation.
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@item
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@item
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-Provided explicitly by the application (STARPU_PER_ARCH model type): the
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+Provided as an estimation from the application itself (@code{STARPU_COMMON} model type and @code{cost_model} field),
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+see for instance
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+@code{examples/common/blas_model.h} and @code{examples/common/blas_model.c}.
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+
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+@item
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+Provided explicitly by the application (@code{STARPU_PER_ARCH} model type): the
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@code{.per_arch[i].cost_model} fields have to be filled with pointers to
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@code{.per_arch[i].cost_model} fields have to be filled with pointers to
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functions which return the expected duration of the task in micro-seconds, one
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functions which return the expected duration of the task in micro-seconds, one
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per architecture.
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per architecture.
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