| 1 | |
| 2 | |
| 3 | * lib/minmax.c: windowed min/max tracker
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| 4 | *
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| 5 | * Kathleen Nichols' algorithm for tracking the minimum (or maximum)
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| 6 | * value of a data stream over some fixed time interval. (E.g.,
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| 7 | * the minimum RTT over the past five minutes.) It uses constant
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| 8 | * space and constant time per update yet almost always delivers
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| 9 | * the same minimum as an implementation that has to keep all the
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| 10 | * data in the window.
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| 11 | *
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| 12 | * The algorithm keeps track of the best, 2nd best & 3rd best min
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| 13 | * values, maintaining an invariant that the measurement time of
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| 14 | * the n'th best >= n-1'th best. It also makes sure that the three
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| 15 | * values are widely separated in the time window since that bounds
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| 16 | * the worse case error when that data is monotonically increasing
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| 17 | * over the window.
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| 18 | *
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| 19 | * Upon getting a new min, we can forget everything earlier because
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| 20 | * it has no value - the new min is <= everything else in the window
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| 21 | * by definition and it's the most recent. So we restart fresh on
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| 22 | * every new min and overwrites 2nd & 3rd choices. The same property
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| 23 | * holds for 2nd & 3rd best.
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| 24 |
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| 25 | #include <linux/module.h>
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| 26 | #include <linux/win_minmax.h>
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| 27 |
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| 28 |
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| 29 | static u32 minmax_subwin_update(struct minmax *m, u32 win,
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| 30 | const struct minmax_sample *val)
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| 31 | {
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| 32 | u32 dt = val->t - m->s[0].t;
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| 33 |
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| 34 | if (unlikely(dt > win)) {
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| 35 | |
| 36 | * Passed entire window without a new val so make 2nd
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| 37 | * choice the new val & 3rd choice the new 2nd choice.
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| 38 | * we may have to iterate this since our 2nd choice
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| 39 | * may also be outside the window (we checked on entry
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| 40 | * that the third choice was in the window).
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| 41 |
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| 42 | m->s[0] = m->s[1];
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| 43 | m->s[1] = m->s[2];
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| 44 | m->s[2] = *val;
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| 45 | if (unlikely(val->t - m->s[0].t > win)) {
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| 46 | m->s[0] = m->s[1];
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| 47 | m->s[1] = m->s[2];
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| 48 | m->s[2] = *val;
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| 49 | }
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| 50 | } else if (unlikely(m->s[1].t == m->s[0].t) && dt > win/4) {
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| 51 | |
| 52 | * We've passed a quarter of the window without a new val
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| 53 | * so take a 2nd choice from the 2nd quarter of the window.
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| 54 |
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| 55 | m->s[2] = m->s[1] = *val;
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| 56 | } else if (unlikely(m->s[2].t == m->s[1].t) && dt > win/2) {
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| 57 | |
| 58 | * We've passed half the window without finding a new val
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| 59 | * so take a 3rd choice from the last half of the window
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| 60 |
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| 61 | m->s[2] = *val;
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| 62 | }
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| 63 | return m->s[0].v;
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| 64 | }
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| 65 |
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| 66 |
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| 67 | u32 minmax_running_max(struct minmax *m, u32 win, u32 t, u32 meas)
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| 68 | {
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| 69 | struct minmax_sample val = { .t = t, .v = meas };
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| 70 |
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| 71 | if (unlikely(val.v >= m->s[0].v) ||
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| 72 | unlikely(val.t - m->s[2].t > win))
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| 73 | return minmax_reset(m, t, meas);
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| 74 |
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| 75 | if (unlikely(val.v >= m->s[1].v))
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| 76 | m->s[2] = m->s[1] = val;
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| 77 | else if (unlikely(val.v >= m->s[2].v))
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| 78 | m->s[2] = val;
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| 79 |
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| 80 | return minmax_subwin_update(m, win, &val);
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| 81 | }
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| 82 | EXPORT_SYMBOL(minmax_running_max);
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| 83 |
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| 84 |
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| 85 | u32 minmax_running_min(struct minmax *m, u32 win, u32 t, u32 meas)
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| 86 | {
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| 87 | struct minmax_sample val = { .t = t, .v = meas };
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| 88 |
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| 89 | if (unlikely(val.v <= m->s[0].v) ||
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| 90 | unlikely(val.t - m->s[2].t > win))
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| 91 | return minmax_reset(m, t, meas);
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| 92 |
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| 93 | if (unlikely(val.v <= m->s[1].v))
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| 94 | m->s[2] = m->s[1] = val;
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| 95 | else if (unlikely(val.v <= m->s[2].v))
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| 96 | m->s[2] = val;
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| 97 |
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| 98 | return minmax_subwin_update(m, win, &val);
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| 99 | }
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| 100 | EXPORT_SYMBOL(minmax_running_min);
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