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linux/fs/bcachefs/mean_and_variance.c
Darrick J. Wong 4b4f0876ab bcachefs: mean_and_variance: put struct mean_and_variance_weighted on a diet
The only caller of this code (time_stats) always knows the weights and
whether or not any information has been collected.  Pass this
information into the mean and variance code so that it doesn't have to
store that information.  This reduces the structure size from 24 to 16
bytes, which shrinks each time_stats counter to 192 bytes from 208.

Signed-off-by: Darrick J. Wong <djwong@kernel.org>
Signed-off-by: Kent Overstreet <kent.overstreet@linux.dev>
2024-03-13 21:37:58 -04:00

174 lines
5.2 KiB
C

// SPDX-License-Identifier: GPL-2.0
/*
* Functions for incremental mean and variance.
*
* This program is free software; you can redistribute it and/or modify it
* under the terms of the GNU General Public License version 2 as published by
* the Free Software Foundation.
*
* This program is distributed in the hope that it will be useful, but WITHOUT
* ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
* FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for
* more details.
*
* Copyright © 2022 Daniel B. Hill
*
* Author: Daniel B. Hill <daniel@gluo.nz>
*
* Description:
*
* This is includes some incremental algorithms for mean and variance calculation
*
* Derived from the paper: https://fanf2.user.srcf.net/hermes/doc/antiforgery/stats.pdf
*
* Create a struct and if it's the weighted variant set the w field (weight = 2^k).
*
* Use mean_and_variance[_weighted]_update() on the struct to update it's state.
*
* Use the mean_and_variance[_weighted]_get_* functions to calculate the mean and variance, some computation
* is deferred to these functions for performance reasons.
*
* see lib/math/mean_and_variance_test.c for examples of usage.
*
* DO NOT access the mean and variance fields of the weighted variants directly.
* DO NOT change the weight after calling update.
*/
#include <linux/bug.h>
#include <linux/compiler.h>
#include <linux/export.h>
#include <linux/limits.h>
#include <linux/math.h>
#include <linux/math64.h>
#include <linux/module.h>
#include "mean_and_variance.h"
u128_u u128_div(u128_u n, u64 d)
{
u128_u r;
u64 rem;
u64 hi = u128_hi(n);
u64 lo = u128_lo(n);
u64 h = hi & ((u64) U32_MAX << 32);
u64 l = (hi & (u64) U32_MAX) << 32;
r = u128_shl(u64_to_u128(div64_u64_rem(h, d, &rem)), 64);
r = u128_add(r, u128_shl(u64_to_u128(div64_u64_rem(l + (rem << 32), d, &rem)), 32));
r = u128_add(r, u64_to_u128(div64_u64_rem(lo + (rem << 32), d, &rem)));
return r;
}
EXPORT_SYMBOL_GPL(u128_div);
/**
* mean_and_variance_get_mean() - get mean from @s
* @s: mean and variance number of samples and their sums
*/
s64 mean_and_variance_get_mean(struct mean_and_variance s)
{
return s.n ? div64_u64(s.sum, s.n) : 0;
}
EXPORT_SYMBOL_GPL(mean_and_variance_get_mean);
/**
* mean_and_variance_get_variance() - get variance from @s1
* @s1: mean and variance number of samples and sums
*
* see linked pdf equation 12.
*/
u64 mean_and_variance_get_variance(struct mean_and_variance s1)
{
if (s1.n) {
u128_u s2 = u128_div(s1.sum_squares, s1.n);
u64 s3 = abs(mean_and_variance_get_mean(s1));
return u128_lo(u128_sub(s2, u128_square(s3)));
} else {
return 0;
}
}
EXPORT_SYMBOL_GPL(mean_and_variance_get_variance);
/**
* mean_and_variance_get_stddev() - get standard deviation from @s
* @s: mean and variance number of samples and their sums
*/
u32 mean_and_variance_get_stddev(struct mean_and_variance s)
{
return int_sqrt64(mean_and_variance_get_variance(s));
}
EXPORT_SYMBOL_GPL(mean_and_variance_get_stddev);
/**
* mean_and_variance_weighted_update() - exponentially weighted variant of mean_and_variance_update()
* @s: mean and variance number of samples and their sums
* @x: new value to include in the &mean_and_variance_weighted
* @initted: caller must track whether this is the first use or not
* @weight: ewma weight
*
* see linked pdf: function derived from equations 140-143 where alpha = 2^w.
* values are stored bitshifted for performance and added precision.
*/
void mean_and_variance_weighted_update(struct mean_and_variance_weighted *s,
s64 x, bool initted, u8 weight)
{
// previous weighted variance.
u8 w = weight;
u64 var_w0 = s->variance;
// new value weighted.
s64 x_w = x << w;
s64 diff_w = x_w - s->mean;
s64 diff = fast_divpow2(diff_w, w);
// new mean weighted.
s64 u_w1 = s->mean + diff;
if (!initted) {
s->mean = x_w;
s->variance = 0;
} else {
s->mean = u_w1;
s->variance = ((var_w0 << w) - var_w0 + ((diff_w * (x_w - u_w1)) >> w)) >> w;
}
}
EXPORT_SYMBOL_GPL(mean_and_variance_weighted_update);
/**
* mean_and_variance_weighted_get_mean() - get mean from @s
* @s: mean and variance number of samples and their sums
* @weight: ewma weight
*/
s64 mean_and_variance_weighted_get_mean(struct mean_and_variance_weighted s,
u8 weight)
{
return fast_divpow2(s.mean, weight);
}
EXPORT_SYMBOL_GPL(mean_and_variance_weighted_get_mean);
/**
* mean_and_variance_weighted_get_variance() -- get variance from @s
* @s: mean and variance number of samples and their sums
* @weight: ewma weight
*/
u64 mean_and_variance_weighted_get_variance(struct mean_and_variance_weighted s,
u8 weight)
{
// always positive don't need fast divpow2
return s.variance >> weight;
}
EXPORT_SYMBOL_GPL(mean_and_variance_weighted_get_variance);
/**
* mean_and_variance_weighted_get_stddev() - get standard deviation from @s
* @s: mean and variance number of samples and their sums
* @weight: ewma weight
*/
u32 mean_and_variance_weighted_get_stddev(struct mean_and_variance_weighted s,
u8 weight)
{
return int_sqrt64(mean_and_variance_weighted_get_variance(s, weight));
}
EXPORT_SYMBOL_GPL(mean_and_variance_weighted_get_stddev);
MODULE_AUTHOR("Daniel B. Hill");
MODULE_LICENSE("GPL");