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Date: Mon, 11 May 2020 19:40:55 +0530
From: Pratik Rajesh Sampat <psampat@...ux.ibm.com>
To: linux-kernel@...r.kernel.org, linux-pm@...r.kernel.org,
rafael.j.wysocki@...el.com, peterz@...radead.org,
dsmythies@...us.net, daniel.lezcano@...aro.org,
ego@...ux.vnet.ibm.com, svaidy@...ux.ibm.com,
psampat@...ux.ibm.com, pratik.sampat@...ibm.com,
pratik.r.sampat@...il.com
Subject: [RFC 1/1] Weighted approach to gather and use history in TEO governor
Complementing the current self correcting window algorithm, an alternate
approach is devised to leverage history based on probability of
occurrence of states
Each CPU maintains a matrix wherein each idle state maintains a
probability distribution for itself and the other corresponding states.
The probability distribution is nothing but a n*n matrix, where
n = drv->state_count.
Each entry in the array signifies a weight for that row.
The weights can vary from the range [0-10000].
For example:
state_mat[2][1] = 3000 means that when state 2 is entered idle with, the
probability that the interval will last long enough to satisfy state 1's
residency is 30%.
The trailing zeros correspond to having more resolution while increasing
or reducing the weights for correction.
Initially the weights are distributed in a way such that the index of
that state in question has a higher probability of choosing itself, as
we have no reason to believe otherwise yet. Initial bias to itself is
60% and the rest 40% is equally distributed to the rest of the states.
Selection of an idle state:
When the TEO governor chooses an idle state, the probability
distribution for that state is looked at. A weighted random number
generator is used using the weights as bias to choose the next idle
state. The algorithm leans to choose that or a shallower state than that
for its next prediction
Correction of the probability distribution:
On wakeup, the weights are updated. The state which it should have woken
up with (could be the hit / miss / early hit state) is increased in
weight by the "LEARNING_RATE" % and the rest of the states for that
index are reduced by the same factor.
The LEARNING RATE is experimentally chosen to be 10 %
Signed-off-by: Pratik Rajesh Sampat <psampat@...ux.ibm.com>
---
drivers/cpuidle/governors/teo.c | 96 +++++++++++++++++++++++++++++++--
1 file changed, 91 insertions(+), 5 deletions(-)
diff --git a/drivers/cpuidle/governors/teo.c b/drivers/cpuidle/governors/teo.c
index de7e706efd46..84058d797b43 100644
--- a/drivers/cpuidle/governors/teo.c
+++ b/drivers/cpuidle/governors/teo.c
@@ -50,6 +50,7 @@
#include <linux/kernel.h>
#include <linux/sched/clock.h>
#include <linux/tick.h>
+#include <linux/random.h>
/*
* The PULSE value is added to metrics when they grow and the DECAY_SHIFT value
@@ -64,6 +65,12 @@
*/
#define INTERVALS 8
+/*
+ * Percentage of the amount of weight to be shifted in the idle state weight
+ * distribution for correction
+ */
+#define LEARNING_RATE 10
+
/**
* struct teo_idle_state - Idle state data used by the TEO cpuidle governor.
* @early_hits: "Early" CPU wakeups "matching" this state.
@@ -98,6 +105,8 @@ struct teo_idle_state {
* @states: Idle states data corresponding to this CPU.
* @interval_idx: Index of the most recent saved idle interval.
* @intervals: Saved idle duration values.
+ * @state_mat: Each idle state maintains a weights corresponding to that
+ * state, storing the probability distribution of occurrence for that state
*/
struct teo_cpu {
u64 time_span_ns;
@@ -105,6 +114,7 @@ struct teo_cpu {
struct teo_idle_state states[CPUIDLE_STATE_MAX];
int interval_idx;
u64 intervals[INTERVALS];
+ int state_mat[CPUIDLE_STATE_MAX][CPUIDLE_STATE_MAX];
};
static DEFINE_PER_CPU(struct teo_cpu, teo_cpus);
@@ -117,7 +127,8 @@ static DEFINE_PER_CPU(struct teo_cpu, teo_cpus);
static void teo_update(struct cpuidle_driver *drv, struct cpuidle_device *dev)
{
struct teo_cpu *cpu_data = per_cpu_ptr(&teo_cpus, dev->cpu);
- int i, idx_hit = -1, idx_timer = -1;
+ int i, idx_hit = -1, idx_timer = -1, idx = -1;
+ int last_idx = dev->last_state_idx;
u64 measured_ns;
if (cpu_data->time_span_ns >= cpu_data->sleep_length_ns) {
@@ -183,16 +194,50 @@ static void teo_update(struct cpuidle_driver *drv, struct cpuidle_device *dev)
if (idx_timer > idx_hit) {
misses += PULSE;
- if (idx_hit >= 0)
+ idx = idx_timer;
+ if (idx_hit >= 0) {
cpu_data->states[idx_hit].early_hits += PULSE;
+ idx = idx_hit;
+ }
} else {
hits += PULSE;
+ idx = last_idx;
}
cpu_data->states[idx_timer].misses = misses;
cpu_data->states[idx_timer].hits = hits;
}
+ /*
+ * Rearrange the weight distribution of the state, increase the weight
+ * by the LEARNING RATE % for the idle state that was supposed to be
+ * chosen and reduce by the same amount for rest of the states
+ *
+ * If the weights are greater than (100 - LEARNING_RATE) % or lesser
+ * than LEARNING_RATE %, do not increase or decrease the confidence
+ * respectively
+ */
+ for (i = 0; i < drv->state_count; i++) {
+ unsigned int delta;
+
+ if (idx == -1)
+ break;
+ if (i == idx) {
+ delta = (LEARNING_RATE * cpu_data->state_mat[last_idx][i]) / 100;
+ if (cpu_data->state_mat[last_idx][i] + delta >=
+ (100 - LEARNING_RATE) * 100)
+ continue;
+ cpu_data->state_mat[last_idx][i] += delta;
+ continue;
+ }
+ delta = (LEARNING_RATE * cpu_data->state_mat[last_idx][i]) /
+ ((drv->state_count - 1) * 100);
+ if (cpu_data->state_mat[last_idx][i] - delta <=
+ LEARNING_RATE * 100)
+ continue;
+ cpu_data->state_mat[last_idx][i] -= delta;
+ }
+
/*
* Save idle duration values corresponding to non-timer wakeups for
* pattern detection.
@@ -244,7 +289,7 @@ static int teo_select(struct cpuidle_driver *drv, struct cpuidle_device *dev,
s64 latency_req = cpuidle_governor_latency_req(dev->cpu);
u64 duration_ns;
unsigned int hits, misses, early_hits;
- int max_early_idx, prev_max_early_idx, constraint_idx, idx, i;
+ int max_early_idx, prev_max_early_idx, constraint_idx, idx, i, og_idx;
ktime_t delta_tick;
if (dev->last_state_idx >= 0) {
@@ -374,10 +419,14 @@ static int teo_select(struct cpuidle_driver *drv, struct cpuidle_device *dev,
if (constraint_idx < idx)
idx = constraint_idx;
+ og_idx = idx;
+
if (idx < 0) {
idx = 0; /* No states enabled. Must use 0. */
} else if (idx > 0) {
- unsigned int count = 0;
+ unsigned int weights_list[CPUIDLE_STATE_MAX];
+ unsigned int i, j = 0, rnd_wt, rnd_num = 0;
+ unsigned int count = 0, sum_weights = 0;
u64 sum = 0;
/*
@@ -412,6 +461,28 @@ static int teo_select(struct cpuidle_driver *drv, struct cpuidle_device *dev,
idx, avg_ns);
}
}
+ /*
+ * In case, the recent history yields a shallower state, then
+ * the probability distribution is looked at.
+ * The weighted random number generator uses the weights as a
+ * bias to choose the next idle state
+ */
+ if (og_idx != idx) {
+ for (i = 0; i <= idx; i++) {
+ if (dev->states_usage[i].disable)
+ continue;
+ sum_weights += cpu_data->state_mat[idx][i];
+ weights_list[j++] = sum_weights;
+ }
+ get_random_bytes(&rnd_num, sizeof(rnd_num));
+ rnd_num = rnd_num % 100;
+ rnd_wt = (rnd_num * sum_weights) / 100;
+ for (i = 0; i < j; i++) {
+ if (rnd_wt < weights_list[i])
+ break;
+ }
+ idx = i;
+ }
}
/*
@@ -468,13 +539,28 @@ static int teo_enable_device(struct cpuidle_driver *drv,
struct cpuidle_device *dev)
{
struct teo_cpu *cpu_data = per_cpu_ptr(&teo_cpus, dev->cpu);
- int i;
+ int i, j;
memset(cpu_data, 0, sizeof(*cpu_data));
for (i = 0; i < INTERVALS; i++)
cpu_data->intervals[i] = U64_MAX;
+ /*
+ * Populate initial weights for each state
+ * The stop state is initially more biased for itself.
+ *
+ * Currently the initial distribution of probabilities are 70%-30%.
+ * The trailing 0s are for increased resolution.
+ */
+ for (i = 0; i < drv->state_count; i++) {
+ for (j = 0; j < drv->state_count; j++) {
+ if (i == j)
+ cpu_data->state_mat[i][j] = 6000;
+ else
+ cpu_data->state_mat[i][j] = 4000 / (drv->state_count - 1);
+ }
+ }
return 0;
}
--
2.17.1
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