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Merge branch 'irq-core-for-linus' of git://git.kernel.org/pub/scm/linux/kernel/git/tip/tip 

Pull irq updates from Ingo Molnar:
 "The changes in this cycle were:

   - Remove the irq timings/variance statistics code that tried to
     predict when the next interrupt would occur, which didn't work out
     as hoped and is replaced by another mechanism.

   - This new mechanism is the 'array suffix computation' estimate,
     which is superior to the previous one as it can detect not just a
     single periodic pattern, but independent periodic patterns along a
     log-2 scale of bucketing and exponential moving average. The
     comments are longer than the code - and it works better at
     predicting various complex interrupt patterns from real-world
     devices than the previous estimate.

   - avoid IRQ-work self-IPIs on the local CPU

   - fix work-list corruption in irq_set_affinity_notifier()"

* 'irq-core-for-linus' of git://git.kernel.org/pub/scm/linux/kernel/git/tip/tip:
  irq_work: Do not raise an IPI when queueing work on the local CPU
  genirq/devres: Use struct_size() in devm_kzalloc()
  genirq/timings: Add array suffix computation code
  genirq/timings: Remove variance computation code
  genirq: Prevent use-after-free and work list corruption
This commit is contained in:
Linus Torvalds 2019-05-06 13:45:04 -07:00
parent d90dcc1f14 471ba0e686
commit 2f1835dffa
4 changed files with 414 additions and 200 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

3
kernel/irq/devres.c
View File

@ -220,9 +220,8 @@ devm_irq_alloc_generic_chip(struct device *dev, const char *name, int num_ct,
irq_flow_handler_t handler)
{
struct irq_chip_generic *gc;
unsigned long sz = sizeof(*gc) + num_ct * sizeof(struct irq_chip_type);
gc = devm_kzalloc(dev, sz, GFP_KERNEL);
gc = devm_kzalloc(dev, struct_size(gc, chip_types, num_ct), GFP_KERNEL);
if (gc)
irq_init_generic_chip(gc, name, num_ct,
irq_base, reg_base, handler);

4
kernel/irq/manage.c
View File

@ -357,8 +357,10 @@ irq_set_affinity_notifier(unsigned int irq, struct irq_affinity_notify *notify)
desc->affinity_notify = notify;
raw_spin_unlock_irqrestore(&desc->lock, flags);
if (old_notify)
if (old_notify) {
cancel_work_sync(&old_notify->work);
kref_put(&old_notify->kref, old_notify->release);
}
return 0;
}

520
kernel/irq/timings.c
View File

@ -9,6 +9,7 @@
#include <linux/idr.h>
#include <linux/irq.h>
#include <linux/math64.h>
#include <linux/log2.h>
#include <trace/events/irq.h>
@ -18,16 +19,6 @@ DEFINE_STATIC_KEY_FALSE(irq_timing_enabled);
DEFINE_PER_CPU(struct irq_timings, irq_timings);
struct irqt_stat {
u64 next_evt;
u64 last_ts;
u64 variance;
u32 avg;
u32 nr_samples;
int anomalies;
int valid;
};
static DEFINE_IDR(irqt_stats);
void irq_timings_enable(void)
@ -40,75 +31,360 @@ void irq_timings_disable(void)
static_branch_disable(&irq_timing_enabled);
}
/**
* irqs_update - update the irq timing statistics with a new timestamp
/*
* The main goal of this algorithm is to predict the next interrupt
* occurrence on the current CPU.
*
* @irqs: an irqt_stat struct pointer
* @ts: the new timestamp
* Currently, the interrupt timings are stored in a circular array
* buffer every time there is an interrupt, as a tuple: the interrupt
* number and the associated timestamp when the event occurred <irq,
* timestamp>.
*
* The statistics are computed online, in other words, the code is
* designed to compute the statistics on a stream of values rather
* than doing multiple passes on the values to compute the average,
* then the variance. The integer division introduces a loss of
* precision but with an acceptable error margin regarding the results
* we would have with the double floating precision: we are dealing
* with nanosec, so big numbers, consequently the mantisse is
* negligeable, especially when converting the time in usec
* afterwards.
* For every interrupt occurring in a short period of time, we can
* measure the elapsed time between the occurrences for the same
* interrupt and we end up with a suite of intervals. The experience
* showed the interrupts are often coming following a periodic
* pattern.
*
* The computation happens at idle time. When the CPU is not idle, the
* interrupts' timestamps are stored in the circular buffer, when the
* CPU goes idle and this routine is called, all the buffer's values
* are injected in the statistical model continuying to extend the
* statistics from the previous busy-idle cycle.
* The objective of the algorithm is to find out this periodic pattern
* in a fastest way and use its period to predict the next irq event.
*
* The observations showed a device will trigger a burst of periodic
* interrupts followed by one or two peaks of longer time, for
* instance when a SD card device flushes its cache, then the periodic
* intervals occur again. A one second inactivity period resets the
* stats, that gives us the certitude the statistical values won't
* exceed 1x10^9, thus the computation won't overflow.
* When the next interrupt event is requested, we are in the situation
* where the interrupts are disabled and the circular buffer
* containing the timings is filled with the events which happened
* after the previous next-interrupt-event request.
*
* Basically, the purpose of the algorithm is to watch the periodic
* interrupts and eliminate the peaks.
* At this point, we read the circular buffer and we fill the irq
* related statistics structure. After this step, the circular array
* containing the timings is empty because all the values are
* dispatched in their corresponding buffers.
*
* An interrupt is considered periodically stable if the interval of
* its occurences follow the normal distribution, thus the values
* comply with:
* Now for each interrupt, we can predict the next event by using the
* suffix array, log interval and exponential moving average
*
* avg - 3 x stddev < value < avg + 3 x stddev
* 1. Suffix array
*
* Which can be simplified to:
* Suffix array is an array of all the suffixes of a string. It is
* widely used as a data structure for compression, text search, ...
* For instance for the word 'banana', the suffixes will be: 'banana'
* 'anana' 'nana' 'ana' 'na' 'a'
*
* -3 x stddev < value - avg < 3 x stddev
* Usually, the suffix array is sorted but for our purpose it is
* not necessary and won't provide any improvement in the context of
* the solved problem where we clearly define the boundaries of the
* search by a max period and min period.
*
* abs(value - avg) < 3 x stddev
* The suffix array will build a suite of intervals of different
* length and will look for the repetition of each suite. If the suite
* is repeating then we have the period because it is the length of
* the suite whatever its position in the buffer.
*
* In order to save a costly square root computation, we use the
* variance. For the record, stddev = sqrt(variance). The equation
* above becomes:
* 2. Log interval
*
* abs(value - avg) < 3 x sqrt(variance)
* We saw the irq timings allow to compute the interval of the
* occurrences for a specific interrupt. We can reasonibly assume the
* longer is the interval, the higher is the error for the next event
* and we can consider storing those interval values into an array
* where each slot in the array correspond to an interval at the power
* of 2 of the index. For example, index 12 will contain values
* between 2^11 and 2^12.
*
* And finally we square it:
* At the end we have an array of values where at each index defines a
* [2^index - 1, 2 ^ index] interval values allowing to store a large
* number of values inside a small array.
*
* (value - avg) ^ 2 < (3 x sqrt(variance)) ^ 2
* For example, if we have the value 1123, then we store it at
* ilog2(1123) = 10 index value.
*
* (value - avg) x (value - avg) < 9 x variance
* Storing those value at the specific index is done by computing an
* exponential moving average for this specific slot. For instance,
* for values 1800, 1123, 1453, ... fall under the same slot (10) and
* the exponential moving average is computed every time a new value
* is stored at this slot.
*
* Statistically speaking, any values out of this interval is
* considered as an anomaly and is discarded. However, a normal
* distribution appears when the number of samples is 30 (it is the
* rule of thumb in statistics, cf. "30 samples" on Internet). When
* there are three consecutive anomalies, the statistics are resetted.
* 3. Exponential Moving Average
*
* The EMA is largely used to track a signal for stocks or as a low
* pass filter. The magic of the formula, is it is very simple and the
* reactivity of the average can be tuned with the factors called
* alpha.
*
* The higher the alphas are, the faster the average respond to the
* signal change. In our case, if a slot in the array is a big
* interval, we can have numbers with a big difference between
* them. The impact of those differences in the average computation
* can be tuned by changing the alpha value.
*
*
* -- The algorithm --
*
* We saw the different processing above, now let's see how they are
* used together.
*
* For each interrupt:
* For each interval:
* Compute the index = ilog2(interval)
* Compute a new_ema(buffer[index], interval)
* Store the index in a circular buffer
*
* Compute the suffix array of the indexes
*
* For each suffix:
* If the suffix is reverse-found 3 times
* Return suffix
*
* Return Not found
*
* However we can not have endless suffix array to be build, it won't
* make sense and it will add an extra overhead, so we can restrict
* this to a maximum suffix length of 5 and a minimum suffix length of
* 2. The experience showed 5 is the majority of the maximum pattern
* period found for different devices.
*
* The result is a pattern finding less than 1us for an interrupt.
*
* Example based on real values:
*
* Example 1 : MMC write/read interrupt interval:
*
* 223947, 1240, 1384, 1386, 1386,
* 217416, 1236, 1384, 1386, 1387,
* 214719, 1241, 1386, 1387, 1384,
* 213696, 1234, 1384, 1386, 1388,
* 219904, 1240, 1385, 1389, 1385,
* 212240, 1240, 1386, 1386, 1386,
* 214415, 1236, 1384, 1386, 1387,
* 214276, 1234, 1384, 1388, ?
*
* For each element, apply ilog2(value)
*
* 15, 8, 8, 8, 8,
* 15, 8, 8, 8, 8,
* 15, 8, 8, 8, 8,
* 15, 8, 8, 8, 8,
* 15, 8, 8, 8, 8,
* 15, 8, 8, 8, 8,
* 15, 8, 8, 8, 8,
* 15, 8, 8, 8, ?
*
* Max period of 5, we take the last (max_period * 3) 15 elements as
* we can be confident if the pattern repeats itself three times it is
* a repeating pattern.
*
* 8,
* 15, 8, 8, 8, 8,
* 15, 8, 8, 8, 8,
* 15, 8, 8, 8, ?
*
* Suffixes are:
*
* 1) 8, 15, 8, 8, 8 <- max period
* 2) 8, 15, 8, 8
* 3) 8, 15, 8
* 4) 8, 15 <- min period
*
* From there we search the repeating pattern for each suffix.
*
* buffer: 8, 15, 8, 8, 8, 8, 15, 8, 8, 8, 8, 15, 8, 8, 8
* | | | | | | | | | | | | | | |
* 8, 15, 8, 8, 8 | | | | | | | | | |
* 8, 15, 8, 8, 8 | | | | |
* 8, 15, 8, 8, 8
*
* When moving the suffix, we found exactly 3 matches.
*
* The first suffix with period 5 is repeating.
*
* The next event is (3 * max_period) % suffix_period
*
* In this example, the result 0, so the next event is suffix[0] => 8
*
* However, 8 is the index in the array of exponential moving average
* which was calculated on the fly when storing the values, so the
* interval is ema[8] = 1366
*
*
* Example 2:
*
* 4, 3, 5, 100,
* 3, 3, 5, 117,
* 4, 4, 5, 112,
* 4, 3, 4, 110,
* 3, 5, 3, 117,
* 4, 4, 5, 112,
* 4, 3, 4, 110,
* 3, 4, 5, 112,
* 4, 3, 4, 110
*
* ilog2
*
* 0, 0, 0, 4,
* 0, 0, 0, 4,
* 0, 0, 0, 4,
* 0, 0, 0, 4,
* 0, 0, 0, 4,
* 0, 0, 0, 4,
* 0, 0, 0, 4,
* 0, 0, 0, 4,
* 0, 0, 0, 4
*
* Max period 5:
* 0, 0, 4,
* 0, 0, 0, 4,
* 0, 0, 0, 4,
* 0, 0, 0, 4
*
* Suffixes:
*
* 1) 0, 0, 4, 0, 0
* 2) 0, 0, 4, 0
* 3) 0, 0, 4
* 4) 0, 0
*
* buffer: 0, 0, 4, 0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0, 4
* | | | | | | X
* 0, 0, 4, 0, 0, | X
* 0, 0
*
* buffer: 0, 0, 4, 0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0, 4
* | | | | | | | | | | | | | | |
* 0, 0, 4, 0, | | | | | | | | | | |
* 0, 0, 4, 0, | | | | | | |
* 0, 0, 4, 0, | | |
* 0 0 4
*
* Pattern is found 3 times, the remaining is 1 which results from
* (max_period * 3) % suffix_period. This value is the index in the
* suffix arrays. The suffix array for a period 4 has the value 4
* at index 1.
*/
static void irqs_update(struct irqt_stat *irqs, u64 ts)
#define EMA_ALPHA_VAL 64
#define EMA_ALPHA_SHIFT 7
#define PREDICTION_PERIOD_MIN 2
#define PREDICTION_PERIOD_MAX 5
#define PREDICTION_FACTOR 4
#define PREDICTION_MAX 10 /* 2 ^ PREDICTION_MAX useconds */
#define PREDICTION_BUFFER_SIZE 16 /* slots for EMAs, hardly more than 16 */
struct irqt_stat {
u64 last_ts;
u64 ema_time[PREDICTION_BUFFER_SIZE];
int timings[IRQ_TIMINGS_SIZE];
int circ_timings[IRQ_TIMINGS_SIZE];
int count;
};
/*
* Exponential moving average computation
*/
static u64 irq_timings_ema_new(u64 value, u64 ema_old)
{
s64 diff;
if (unlikely(!ema_old))
return value;
diff = (value - ema_old) * EMA_ALPHA_VAL;
/*
* We can use a s64 type variable to be added with the u64
* ema_old variable as this one will never have its topmost
* bit set, it will be always smaller than 2^63 nanosec
* interrupt interval (292 years).
*/
return ema_old + (diff >> EMA_ALPHA_SHIFT);
}
static int irq_timings_next_event_index(int *buffer, size_t len, int period_max)
{
int i;
/*
* The buffer contains the suite of intervals, in a ilog2
* basis, we are looking for a repetition. We point the
* beginning of the search three times the length of the
* period beginning at the end of the buffer. We do that for
* each suffix.
*/
for (i = period_max; i >= PREDICTION_PERIOD_MIN ; i--) {
int *begin = &buffer[len - (i * 3)];
int *ptr = begin;
/*
* We look if the suite with period 'i' repeat
* itself. If it is truncated at the end, as it
* repeats we can use the period to find out the next
* element.
*/
while (!memcmp(ptr, begin, i * sizeof(*ptr))) {
ptr += i;
if (ptr >= &buffer[len])
return begin[((i * 3) % i)];
}
}
return -1;
}
static u64 __irq_timings_next_event(struct irqt_stat *irqs, int irq, u64 now)
{
int index, i, period_max, count, start, min = INT_MAX;
if ((now - irqs->last_ts) >= NSEC_PER_SEC) {
irqs->count = irqs->last_ts = 0;
return U64_MAX;
}
/*
* As we want to find three times the repetition, we need a
* number of intervals greater or equal to three times the
* maximum period, otherwise we truncate the max period.
*/
period_max = irqs->count > (3 * PREDICTION_PERIOD_MAX) ?
PREDICTION_PERIOD_MAX : irqs->count / 3;
/*
* If we don't have enough irq timings for this prediction,
* just bail out.
*/
if (period_max <= PREDICTION_PERIOD_MIN)
return U64_MAX;
/*
* 'count' will depends if the circular buffer wrapped or not
*/
count = irqs->count < IRQ_TIMINGS_SIZE ?
irqs->count : IRQ_TIMINGS_SIZE;
start = irqs->count < IRQ_TIMINGS_SIZE ?
0 : (irqs->count & IRQ_TIMINGS_MASK);
/*
* Copy the content of the circular buffer into another buffer
* in order to linearize the buffer instead of dealing with
* wrapping indexes and shifted array which will be prone to
* error and extremelly difficult to debug.
*/
for (i = 0; i < count; i++) {
int index = (start + i) & IRQ_TIMINGS_MASK;
irqs->timings[i] = irqs->circ_timings[index];
min = min_t(int, irqs->timings[i], min);
}
index = irq_timings_next_event_index(irqs->timings, count, period_max);
if (index < 0)
return irqs->last_ts + irqs->ema_time[min];
return irqs->last_ts + irqs->ema_time[index];
}
static inline void irq_timings_store(int irq, struct irqt_stat *irqs, u64 ts)
{
u64 old_ts = irqs->last_ts;
u64 variance = 0;
u64 interval;
s64 diff;
int index;
/*
* The timestamps are absolute time values, we need to compute
@ -135,87 +411,28 @@ static void irqs_update(struct irqt_stat *irqs, u64 ts)
* want as we need another timestamp to compute an interval.
*/
if (interval >= NSEC_PER_SEC) {
memset(irqs, 0, sizeof(*irqs));
irqs->last_ts = ts;
irqs->count = 0;
return;
}
/*
* Pre-compute the delta with the average as the result is
* used several times in this function.
* Get the index in the ema table for this interrupt. The
* PREDICTION_FACTOR increase the interval size for the array
* of exponential average.
*/
diff = interval - irqs->avg;
index = likely(interval) ?
ilog2((interval >> 10) / PREDICTION_FACTOR) : 0;
/*
* Increment the number of samples.
* Store the index as an element of the pattern in another
* circular array.
*/
irqs->nr_samples++;
irqs->circ_timings[irqs->count & IRQ_TIMINGS_MASK] = index;
/*
* Online variance divided by the number of elements if there
* is more than one sample. Normally the formula is division
* by nr_samples - 1 but we assume the number of element will be
* more than 32 and dividing by 32 instead of 31 is enough
* precise.
*/
if (likely(irqs->nr_samples > 1))
variance = irqs->variance >> IRQ_TIMINGS_SHIFT;
irqs->ema_time[index] = irq_timings_ema_new(interval,
irqs->ema_time[index]);
/*
* The rule of thumb in statistics for the normal distribution
* is having at least 30 samples in order to have the model to
* apply. Values outside the interval are considered as an
* anomaly.
*/
if ((irqs->nr_samples >= 30) && ((diff * diff) > (9 * variance))) {
/*
* After three consecutive anomalies, we reset the
* stats as it is no longer stable enough.
*/
if (irqs->anomalies++ >= 3) {
memset(irqs, 0, sizeof(*irqs));
irqs->last_ts = ts;
return;
}
} else {
/*
* The anomalies must be consecutives, so at this
* point, we reset the anomalies counter.
*/
irqs->anomalies = 0;
}
/*
* The interrupt is considered stable enough to try to predict
* the next event on it.
*/
irqs->valid = 1;
/*
* Online average algorithm:
*
* new_average = average + ((value - average) / count)
*
* The variance computation depends on the new average
* to be computed here first.
*
*/
irqs->avg = irqs->avg + (diff >> IRQ_TIMINGS_SHIFT);
/*
* Online variance algorithm:
*
* new_variance = variance + (value - average) x (value - new_average)
*
* Warning: irqs->avg is updated with the line above, hence
* 'interval - irqs->avg' is no longer equal to 'diff'
*/
irqs->variance = irqs->variance + (diff * (interval - irqs->avg));
/*
* Update the next event
*/
irqs->next_evt = ts + irqs->avg;
irqs->count++;
}
/**
@ -259,6 +476,9 @@ u64 irq_timings_next_event(u64 now)
*/
lockdep_assert_irqs_disabled();
if (!irqts->count)
return next_evt;
/*
* Number of elements in the circular buffer: If it happens it
* was flushed before, then the number of elements could be
@ -269,21 +489,19 @@ u64 irq_timings_next_event(u64 now)
* type but with the cost of extra computation in the
* interrupt handler hot path. We choose efficiency.
*
* Inject measured irq/timestamp to the statistical model
* while decrementing the counter because we consume the data
* from our circular buffer.
* Inject measured irq/timestamp to the pattern prediction
* model while decrementing the counter because we consume the
* data from our circular buffer.
*/
for (i = irqts->count & IRQ_TIMINGS_MASK,
i = (irqts->count & IRQ_TIMINGS_MASK) - 1;
irqts->count = min(IRQ_TIMINGS_SIZE, irqts->count);
irqts->count > 0; irqts->count--, i = (i + 1) & IRQ_TIMINGS_MASK) {
for (; irqts->count > 0; irqts->count--, i = (i + 1) & IRQ_TIMINGS_MASK) {
irq = irq_timing_decode(irqts->values[i], &ts);
s = idr_find(&irqt_stats, irq);
if (s) {
irqs = this_cpu_ptr(s);
irqs_update(irqs, ts);
}
if (s)
irq_timings_store(irq, this_cpu_ptr(s), ts);
}
/*
@ -294,26 +512,12 @@ u64 irq_timings_next_event(u64 now)
irqs = this_cpu_ptr(s);
if (!irqs->valid)
continue;
ts = __irq_timings_next_event(irqs, i, now);
if (ts <= now)
return now;
if (irqs->next_evt <= now) {
irq = i;
next_evt = now;
/*
* This interrupt mustn't use in the future
* until new events occur and update the
* statistics.
*/
irqs->valid = 0;
break;
}
if (irqs->next_evt < next_evt) {
irq = i;
next_evt = irqs->next_evt;
}
if (ts < next_evt)
next_evt = ts;
}
return next_evt;

85
kernel/irq_work.c
View File

@ -56,34 +56,18 @@ void __weak arch_irq_work_raise(void)
*/
}
/*
* Enqueue the irq_work @work on @cpu unless it's already pending
* somewhere.
*
* Can be re-enqueued while the callback is still in progress.
*/
bool irq_work_queue_on(struct irq_work *work, int cpu)
/* Enqueue on current CPU, work must already be claimed and preempt disabled */
static void __irq_work_queue_local(struct irq_work *work)
{
/* All work should have been flushed before going offline */
WARN_ON_ONCE(cpu_is_offline(cpu));
#ifdef CONFIG_SMP
/* Arch remote IPI send/receive backend aren't NMI safe */
WARN_ON_ONCE(in_nmi());
/* Only queue if not already pending */
if (!irq_work_claim(work))
return false;
if (llist_add(&work->llnode, &per_cpu(raised_list, cpu)))
arch_send_call_function_single_ipi(cpu);
#else /* #ifdef CONFIG_SMP */
irq_work_queue(work);
#endif /* #else #ifdef CONFIG_SMP */
return true;
/* If the work is "lazy", handle it from next tick if any */
if (work->flags & IRQ_WORK_LAZY) {
if (llist_add(&work->llnode, this_cpu_ptr(&lazy_list)) &&
tick_nohz_tick_stopped())
arch_irq_work_raise();
} else {
if (llist_add(&work->llnode, this_cpu_ptr(&raised_list)))
arch_irq_work_raise();
}
}
/* Enqueue the irq work @work on the current CPU */
@ -95,23 +79,48 @@ bool irq_work_queue(struct irq_work *work)
/* Queue the entry and raise the IPI if needed. */
preempt_disable();
/* If the work is "lazy", handle it from next tick if any */
if (work->flags & IRQ_WORK_LAZY) {
if (llist_add(&work->llnode, this_cpu_ptr(&lazy_list)) &&
tick_nohz_tick_stopped())
arch_irq_work_raise();
} else {
if (llist_add(&work->llnode, this_cpu_ptr(&raised_list)))
arch_irq_work_raise();
}
__irq_work_queue_local(work);
preempt_enable();
return true;
}
EXPORT_SYMBOL_GPL(irq_work_queue);
/*
* Enqueue the irq_work @work on @cpu unless it's already pending
* somewhere.
*
* Can be re-enqueued while the callback is still in progress.
*/
bool irq_work_queue_on(struct irq_work *work, int cpu)
{
#ifndef CONFIG_SMP
return irq_work_queue(work);
#else /* CONFIG_SMP: */
/* All work should have been flushed before going offline */
WARN_ON_ONCE(cpu_is_offline(cpu));
/* Only queue if not already pending */
if (!irq_work_claim(work))
return false;
preempt_disable();
if (cpu != smp_processor_id()) {
/* Arch remote IPI send/receive backend aren't NMI safe */
WARN_ON_ONCE(in_nmi());
if (llist_add(&work->llnode, &per_cpu(raised_list, cpu)))
arch_send_call_function_single_ipi(cpu);
} else {
__irq_work_queue_local(work);
}
preempt_enable();
return true;
#endif /* CONFIG_SMP */
}
bool irq_work_needs_cpu(void)
{
struct llist_head *raised, *lazy;