linux / linux / kernel / git / torvalds / linux / eaea2947063ac694cddff1787d43e7807490dbc7 / . / lib / win_minmax.c

// SPDX-License-Identifier: GPL-2.0 | |

/** | |

* lib/minmax.c: windowed min/max tracker | |

* | |

* Kathleen Nichols' algorithm for tracking the minimum (or maximum) | |

* value of a data stream over some fixed time interval. (E.g., | |

* the minimum RTT over the past five minutes.) It uses constant | |

* space and constant time per update yet almost always delivers | |

* the same minimum as an implementation that has to keep all the | |

* data in the window. | |

* | |

* The algorithm keeps track of the best, 2nd best & 3rd best min | |

* values, maintaining an invariant that the measurement time of | |

* the n'th best >= n-1'th best. It also makes sure that the three | |

* values are widely separated in the time window since that bounds | |

* the worse case error when that data is monotonically increasing | |

* over the window. | |

* | |

* Upon getting a new min, we can forget everything earlier because | |

* it has no value - the new min is <= everything else in the window | |

* by definition and it's the most recent. So we restart fresh on | |

* every new min and overwrites 2nd & 3rd choices. The same property | |

* holds for 2nd & 3rd best. | |

*/ | |

#include <linux/module.h> | |

#include <linux/win_minmax.h> | |

/* As time advances, update the 1st, 2nd, and 3rd choices. */ | |

static u32 minmax_subwin_update(struct minmax *m, u32 win, | |

const struct minmax_sample *val) | |

{ | |

u32 dt = val->t - m->s[0].t; | |

if (unlikely(dt > win)) { | |

/* | |

* Passed entire window without a new val so make 2nd | |

* choice the new val & 3rd choice the new 2nd choice. | |

* we may have to iterate this since our 2nd choice | |

* may also be outside the window (we checked on entry | |

* that the third choice was in the window). | |

*/ | |

m->s[0] = m->s[1]; | |

m->s[1] = m->s[2]; | |

m->s[2] = *val; | |

if (unlikely(val->t - m->s[0].t > win)) { | |

m->s[0] = m->s[1]; | |

m->s[1] = m->s[2]; | |

m->s[2] = *val; | |

} | |

} else if (unlikely(m->s[1].t == m->s[0].t) && dt > win/4) { | |

/* | |

* We've passed a quarter of the window without a new val | |

* so take a 2nd choice from the 2nd quarter of the window. | |

*/ | |

m->s[2] = m->s[1] = *val; | |

} else if (unlikely(m->s[2].t == m->s[1].t) && dt > win/2) { | |

/* | |

* We've passed half the window without finding a new val | |

* so take a 3rd choice from the last half of the window | |

*/ | |

m->s[2] = *val; | |

} | |

return m->s[0].v; | |

} | |

/* Check if new measurement updates the 1st, 2nd or 3rd choice max. */ | |

u32 minmax_running_max(struct minmax *m, u32 win, u32 t, u32 meas) | |

{ | |

struct minmax_sample val = { .t = t, .v = meas }; | |

if (unlikely(val.v >= m->s[0].v) || /* found new max? */ | |

unlikely(val.t - m->s[2].t > win)) /* nothing left in window? */ | |

return minmax_reset(m, t, meas); /* forget earlier samples */ | |

if (unlikely(val.v >= m->s[1].v)) | |

m->s[2] = m->s[1] = val; | |

else if (unlikely(val.v >= m->s[2].v)) | |

m->s[2] = val; | |

return minmax_subwin_update(m, win, &val); | |

} | |

EXPORT_SYMBOL(minmax_running_max); | |

/* Check if new measurement updates the 1st, 2nd or 3rd choice min. */ | |

u32 minmax_running_min(struct minmax *m, u32 win, u32 t, u32 meas) | |

{ | |

struct minmax_sample val = { .t = t, .v = meas }; | |

if (unlikely(val.v <= m->s[0].v) || /* found new min? */ | |

unlikely(val.t - m->s[2].t > win)) /* nothing left in window? */ | |

return minmax_reset(m, t, meas); /* forget earlier samples */ | |

if (unlikely(val.v <= m->s[1].v)) | |

m->s[2] = m->s[1] = val; | |

else if (unlikely(val.v <= m->s[2].v)) | |

m->s[2] = val; | |

return minmax_subwin_update(m, win, &val); | |

} |