|
| 1 | +/* |
| 2 | + * Portions (c) Meta Platforms, Inc. and affiliates. |
| 3 | + * All rights reserved. |
| 4 | + * |
| 5 | + * This source code is licensed under the BSD-style license found in the |
| 6 | + * LICENSE file in the root directory of this source tree. |
| 7 | + */ |
| 8 | + |
| 9 | +/* |
| 10 | + * Code sourced from |
| 11 | + * https://github.com/microsoft/ArchProbe/blob/main/include/stats.hpp with the |
| 12 | + * following MIT license |
| 13 | + * |
| 14 | + * MIT License |
| 15 | + * |
| 16 | + * Copyright (c) Microsoft Corporation. |
| 17 | + * |
| 18 | + * Permission is hereby granted, free of charge, to any person obtaining a copy |
| 19 | + * of this software and associated documentation files (the "Software"), to |
| 20 | + * deal in the Software without restriction, including without limitation the |
| 21 | + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or |
| 22 | + * sell copies of the Software, and to permit persons to whom the Software is |
| 23 | + * furnished to do so, subject to the following conditions: |
| 24 | + * |
| 25 | + * The above copyright notice and this permission notice shall be included in |
| 26 | + * all copies or substantial portions of the Software. |
| 27 | + * |
| 28 | + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| 29 | + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| 30 | + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
| 31 | + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| 32 | + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING |
| 33 | + * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS |
| 34 | + * IN THE SOFTWARE |
| 35 | + */ |
| 36 | + |
| 37 | +#pragma once |
| 38 | +#include <array> |
| 39 | +#include <cstdint> |
| 40 | + |
| 41 | +template <typename T> |
| 42 | +class AvgStats { |
| 43 | + T sum_ = 0; |
| 44 | + uint64_t n_ = 0; |
| 45 | + |
| 46 | + public: |
| 47 | + typedef T value_t; |
| 48 | + |
| 49 | + void push(T value) { |
| 50 | + sum_ += value; |
| 51 | + n_ += 1; |
| 52 | + } |
| 53 | + inline bool has_value() const { |
| 54 | + return n_ != 0; |
| 55 | + } |
| 56 | + operator T() const { |
| 57 | + return sum_ / n_; |
| 58 | + } |
| 59 | +}; |
| 60 | + |
| 61 | +template <typename T, size_t NTap> |
| 62 | +class NTapAvgStats { |
| 63 | + std::array<double, NTap> hist_; |
| 64 | + size_t cur_idx_; |
| 65 | + bool ready_; |
| 66 | + |
| 67 | + public: |
| 68 | + typedef T value_t; |
| 69 | + |
| 70 | + void push(T value) { |
| 71 | + hist_[cur_idx_++] = value; |
| 72 | + if (cur_idx_ >= NTap) { |
| 73 | + cur_idx_ = 0; |
| 74 | + ready_ = true; |
| 75 | + } |
| 76 | + } |
| 77 | + inline bool has_value() const { |
| 78 | + return ready_; |
| 79 | + } |
| 80 | + operator T() const { |
| 81 | + double out = 0.0; |
| 82 | + for (double x : hist_) { |
| 83 | + out += x; |
| 84 | + } |
| 85 | + out /= NTap; |
| 86 | + return out; |
| 87 | + } |
| 88 | +}; |
| 89 | + |
| 90 | +template <uint32_t NTap> |
| 91 | +struct DtJumpFinder { |
| 92 | + private: |
| 93 | + NTapAvgStats<double, NTap> time_avg_; |
| 94 | + AvgStats<double> dtime_avg_; |
| 95 | + double compensation_; |
| 96 | + double threshold_; |
| 97 | + |
| 98 | + public: |
| 99 | + // Compensation is a tiny additive to give on delta time so that the algorithm |
| 100 | + // works smoothly when a sequence of identical timing is ingested, which is |
| 101 | + // pretty common in our tests. Threshold is simply how many times the new |
| 102 | + // delta has to be to be recognized as a deviation. |
| 103 | + DtJumpFinder(double compensation = 0.01, double threshold = 10) |
| 104 | + : time_avg_(), |
| 105 | + dtime_avg_(), |
| 106 | + compensation_(compensation), |
| 107 | + threshold_(threshold) {} |
| 108 | + |
| 109 | + // Returns true if the delta time regarding to the last data point seems |
| 110 | + // normal; returns false if it seems the new data point is too much away from |
| 111 | + // the historical records. |
| 112 | + bool push(double time) { |
| 113 | + if (time_avg_.has_value()) { |
| 114 | + double dtime = std::abs(time - time_avg_) + (compensation_ * time_avg_); |
| 115 | + if (dtime_avg_.has_value()) { |
| 116 | + double ddtime = std::abs(dtime - dtime_avg_); |
| 117 | + if (ddtime > threshold_ * dtime_avg_) { |
| 118 | + return true; |
| 119 | + } |
| 120 | + } |
| 121 | + dtime_avg_.push(dtime); |
| 122 | + } |
| 123 | + time_avg_.push(time); |
| 124 | + return false; |
| 125 | + } |
| 126 | + |
| 127 | + double dtime_avg() const { |
| 128 | + return dtime_avg_; |
| 129 | + } |
| 130 | + double compensate_time() const { |
| 131 | + return compensation_ * time_avg_; |
| 132 | + } |
| 133 | +}; |
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