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@@ -166,20 +166,34 @@ be obtained from the machine power supplier.
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The energy actually consumed by the total execution can be displayed by setting
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<c>export STARPU_PROFILING=1 STARPU_WORKER_STATS=1</c> .
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-On-line task consumption measurement is currently only supported through the
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+For OpenCL devices, on-line task consumption measurement is currently supported through the
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<c>CL_PROFILING_POWER_CONSUMED</c> OpenCL extension, implemented in the MoviSim
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-simulator. Applications can however provide explicit measurements by
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-using the function starpu_perfmodel_update_history() (examplified in \ref PerformanceModelExample
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-with the <c>energy_model</c> performance model). Fine-grain
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-measurement is often not feasible with the feedback provided by the hardware, so
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-the user can for instance run a given task a thousand times, measure the global
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+simulator.
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+
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+For CUDA devices, on-line task consumption measurement is supported on V100
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+cards and beyond. This however only works for quite long tasks, since the
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+measurement granularity is about 10ms.
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+
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+Applications can however provide explicit measurements by using the function
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+starpu_perfmodel_update_history() (examplified in \ref PerformanceModelExample
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+with the <c>energy_model</c> performance model). Fine-grain measurement
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+is often not feasible with the feedback provided by the hardware, so the
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+user can for instance run a given task a thousand times, measure the global
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consumption for that series of tasks, divide it by a thousand, repeat for
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-varying kinds of tasks and task sizes, and eventually feed StarPU
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-with these manual measurements through starpu_perfmodel_update_history().
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-For instance, for CUDA devices, <c>nvidia-smi -q -d POWER</c> can be used to get
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-the current consumption in Watt. Multiplying this value by the average duration
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-of a single task gives the consumption of the task in Joules, which can be given
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-to starpu_perfmodel_update_history().
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+varying kinds of tasks and task sizes, and eventually feed StarPU with these
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+manual measurements through starpu_perfmodel_update_history(). For instance,
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+for CUDA devices, <c>nvidia-smi -q -d POWER</c> can be used to get the current
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+consumption in Watt. Multiplying this value by the average duration of a
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+single task gives the consumption of the task in Joules, which can be given to
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+starpu_perfmodel_update_history().
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+
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+Another way to provide the energy performance is to define a
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+perfmodel with starpu_perfmodel::type ::STARPU_PER_ARCH, and set the
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+starpu_perfmodel::arch_cost_function field to a function which shall return the
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+estimated consumption of the task in Joules. Such a function can for instance
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+use starpu_task_expected_length() on the task (in µs), multiplied by the
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+typical power consumption of the device, e.g. in W, and divided by 1000000. to
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+get Joules.
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\section ExistingModularizedSchedulers Modularized Schedulers
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