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2 CPU cooling APIs How To
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5 Written by Amit Daniel Kachhap <amit.kachhap@linaro.org>
9 Copyright (c) 2012 Samsung Electronics Co., Ltd(http://www.samsung.com)
14 The generic cpu cooling(freq clipping) provides registration/unregistration APIs
15 to the caller. The binding of the cooling devices to the trip point is left for
16 the user. The registration APIs returns the cooling device pointer.
21 1.1 cpufreq registration/unregistration APIs
22 --------------------------------------------
26 struct thermal_cooling_device
27 *cpufreq_cooling_register(struct cpumask *clip_cpus)
29 This interface function registers the cpufreq cooling device with the name
30 "thermal-cpufreq-%x". This api can support multiple instances of cpufreq
34 cpumask of cpus where the frequency constraints will happen.
38 struct thermal_cooling_device
39 *of_cpufreq_cooling_register(struct cpufreq_policy *policy)
41 This interface function registers the cpufreq cooling device with
42 the name "thermal-cpufreq-%x" linking it with a device tree node, in
43 order to bind it via the thermal DT code. This api can support multiple
44 instances of cpufreq cooling devices.
52 void cpufreq_cooling_unregister(struct thermal_cooling_device *cdev)
54 This interface function unregisters the "thermal-cpufreq-%x" cooling device.
56 cdev: Cooling device pointer which has to be unregistered.
61 The power API registration functions provide a simple power model for
62 CPUs. The current power is calculated as dynamic power (static power isn't
63 supported currently). This power model requires that the operating-points of
64 the CPUs are registered using the kernel's opp library and the
65 `cpufreq_frequency_table` is assigned to the `struct device` of the
66 cpu. If you are using CONFIG_CPUFREQ_DT then the
67 `cpufreq_frequency_table` should already be assigned to the cpu
70 The dynamic power consumption of a processor depends on many factors.
71 For a given processor implementation the primary factors are:
73 - The time the processor spends running, consuming dynamic power, as
74 compared to the time in idle states where dynamic consumption is
75 negligible. Herein we refer to this as 'utilisation'.
76 - The voltage and frequency levels as a result of DVFS. The DVFS
77 level is a dominant factor governing power consumption.
78 - In running time the 'execution' behaviour (instruction types, memory
79 access patterns and so forth) causes, in most cases, a second order
80 variation. In pathological cases this variation can be significant,
81 but typically it is of a much lesser impact than the factors above.
83 A high level dynamic power consumption model may then be represented as::
85 Pdyn = f(run) * Voltage^2 * Frequency * Utilisation
87 f(run) here represents the described execution behaviour and its
88 result has a units of Watts/Hz/Volt^2 (this often expressed in
91 The detailed behaviour for f(run) could be modelled on-line. However,
92 in practice, such an on-line model has dependencies on a number of
93 implementation specific processor support and characterisation
94 factors. Therefore, in initial implementation that contribution is
95 represented as a constant coefficient. This is a simplification
96 consistent with the relative contribution to overall power variation.
98 In this simplified representation our model becomes::
100 Pdyn = Capacitance * Voltage^2 * Frequency * Utilisation
102 Where `capacitance` is a constant that represents an indicative
103 running time dynamic power coefficient in fundamental units of
104 mW/MHz/uVolt^2. Typical values for mobile CPUs might lie in range
105 from 100 to 500. For reference, the approximate values for the SoC in
106 ARM's Juno Development Platform are 530 for the Cortex-A57 cluster and
107 140 for the Cortex-A53 cluster.