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Библиотека JIT-компиляции Loops для оптимизации нейросетей, СУБД и не только

Loop fusion is a compiler transformation in which two adjacent loops are merged into a single loop over the same index range. This transformation is typically applied to reduce loop overhead and improve run-time performance.

— Intel compiler guide

Привет, Хабр! Меня зовут Пётр Чекмарёв, я старший инженер компании YADRO, занимаюсь компьютерным зрением на мобильных устройствах и низкоуровневой оптимизацией плотных вычислительных функций.

Оптимизация кода — вечная тема, особенно актуальная в дни триумфального шествия искусственного интеллекта. Оптимально написанные, но изолированные ядра сетей составляются в разные последовательности в зависимости от архитектуры модели. Однако, если дать им информацию друг о друге во время компиляции, сеть удастся заметно ускорить. Выгружать программу для перекомпиляции, будь она движком инференса или СУБД — бессмысленно, поэтому компилировать надо во время работы, Just-In-Time. В предыдущей статье AI-дивизиона YADRO Илья Знаменский рассказывал про JIT на базе Xbyak. В продолжении темы, я расскажу про пет-проект векторной JIT-кодогенерации, который я веду, и покажу, как она может помогать в оптимизации.

Оптимизация, векторизация и JIT

Когда возникает задача оптимизации кода, а все мы хотим чтобы наше приложение или фреймворк не тормозили, есть несколько классических подходов. Если речь идет о вычислительно емких алгоритмах обработки значительных массивов данных, векторизация, то есть раскручивание циклов и задействование векторных инструкций SIMD , обрабатывающих пачку данных за раз, — это один из самых очевидных и мощных способов ускорения кода, работающего с однородными данными. Например, с данными компьютерного зрения, вычислительной физики, нейронных сетей, и даже, как ни странно, БД. Причем на мобильных устройствах альтернатива в виде портирования кода на GPU оказывается довольно дорогой, а иногда может даже проигрывать в скорости и энергоэффективности.

285572b62504c7d338147d6acf92c6ac.png

Почему это важно для «железа» заказчиков: в B2B-планшетах цена миллисекунды — это не только скорость интерфейса, но и время работы от батареи, нагрев, стабильность в длительных сессиях и предсказуемость на разных ревизиях устройств. Поэтому задачи оптимизации у нас приходят от полевых сценариев клиентов: распознавание/классификация на устройстве, пост-обработка, локальная аналитика – там, где облако недоступно или запрещено политиками безопасности.

Современные компиляторы могут автоматически векторизовать ваш код, но вы об этом не узнаете, пока не полезете в ассемблер, однако, несмотря на впечатляющее развитие возможностей автовекторизации, практика показывает, что иногда даже с несложными задачами они справиться не могут. Сложные же ядра автовекторизатор может либо не обработать, либо сделать это так, что из получившегося кода ручная оптимизация может выжать кратное ускорение. Например, программист может хитро изменить схему доступа к данным и порядок их обхода, заточив не только алгоритм, но и структуры данных специально под вектора. Поэтому работа с ассемблером и интринсиками более чем востребована.

Интринсики — это специализированные функции, которые почти в неизменном виде пролетают через пайплайн компилятора. Они плюс-минус соответствуют одной инструкции ассемблера. По сравнению с ассемблером интринсики добавляют очень ощутимый синтаксический сахар:

  • вы работаете не с ограниченным числом регистров, а с бесконечным числом переменных;

  • не нужно создавать ассемблерные вставки: код можно отлаживать вместе с остальным.

Конечно, это не избавляет оптимизатора от необходимости переосмыслить алгоритм в терминах векторных операций и даже периодически почитывать генерируемый ассемблерный код, но работа значительно упрощается. Интринсики — это главный инструмент человека, который занимается ручной векторизацией. Он органично и удобно описывает машинерию векторных расширений. В качестве простого примера посмотрим на упрощенный код функции сглаживания 3x3 на NEON, составляющей для каждого пикселя среднее из пикселей окрестности:

Код
cv::Mat src, dst;
// Специальный тип регистра с четырьмя float'ми
float32x4_t denominator = vdupq_n_f32(1./9.);
constexpr int NEON_32BIT_LANES = 4;
for(int y = 0; y < hd; y++)
{
    float* srcPrevLine = src.ptr<float>(y-1);
    float* srcCurLine  = src.ptr<float>(y);
    float* srcNextLine = src.ptr<float>(y+1);
    float* dstCurLine  = dst.ptr<float>(y);
    // Будем обрабатывать пиксели не по одному, а по четыре.
    for(int x = 0; x <= wd - NEON_32BIT_LANES; x+=NEON_32BIT_LANES)
    {
        float32x4_t sum = vld1q_f32(srcPrevLine + x - 1);
        // Эти функции со специфическими названиями и есть
        // интринсики, add - суммирование, ld - загрузка из
        // памяти, mul - умножение, st - выгрузка в память.
        // Немного практики - и все становится довольно просто.
        sum   = vaddq_f32(sum, vld1q_f32(srcPrevLine + x    ));
        sum   = vaddq_f32(sum, vld1q_f32(srcPrevLine + x + 1));
        sum   = vaddq_f32(sum, vld1q_f32(srcCurLine  + x - 1));
        sum   = vaddq_f32(sum, vld1q_f32(srcCurLine  + x    ));
        sum   = vaddq_f32(sum, vld1q_f32(srcCurLine  + x + 1));
        sum   = vaddq_f32(sum, vld1q_f32(srcNextLine + x - 1));
        sum   = vaddq_f32(sum, vld1q_f32(srcNextLine + x    ));
        sum   = vaddq_f32(sum, vld1q_f32(srcNextLine + x + 1));
        vst1q_f32(dstCurLine + x, vmulq_f32(sum, denominator));
    }
}

Давайте теперь поговорим про JIT и как JIT связан с оптимизацией. Классический подход к оптимизации — это использование обычного AOT-компилятора C++ с поддержкой интринсиков. Термин «JIT» наверняка хорошо знаком Java-программистам — он отсылает к Hotspot, стандартному JIT-компилятору, на ходу преобразующему вызываемые куски байт-кода в нативный код, который по ходу профилируется и, при необходимости, дооптимизируется, то есть компилируется с более агрессивными настройками. Поэтому часто люди, которые слышат «JIT-компилятор», представляют себе недоступный для управления программистом процесс автоматической компиляции байткода в машинный код.

Чтобы избежать путаницы, я буду пользоваться термином «JIT-кодогенерация», которая также осуществляется на лету, но находится под управлением программиста. Это совершенно другая практика, заключающаяся в написании программного кода в процессе работы программы с последующим запуском. В той же Java есть библиотека JIT-кодогенерации Janino, использующаяся, например, в Spark, для написания оптимального кода для фильтров конкретных запросов (насколько вообще может быть оптимальным Java-код).

В YADRO мы занимаемся оптимизацией под конкретные сценарии заказчиков. Часть таких сценариев живет на KVADRA_T – корпоративных планшетах: офлайн-инференс на устройстве, ограничения по энергии и термопакету, требования по безопасности и длительной поддержке. Поэтому JIT-кодогенерация и векторные оптимизации для нас — не абстракция, а способ выжать стабильную производительность в реальных B2B-нагрузках без зависимости от облака.

JIT-кодогенерация полезна в оптимизации — она позволяет на лету специализировать обобщенный код под текущий случай. Посмотрим на несколько примеров.

Пример 1. Удаление условных переходов

Самый простой пример — это удаление условных переходов, когда вы точно знаете, что какое-то условие не будет реализовано. AOT-компилятор с этим не справится, потому что ему неоткуда взять информацию о принципиальной нереализуемости ветвления.

Пример 2. Параметризованные ядра нейронных сетей

Пример, точнее передающий идею метапрограммирования, заложенную в JIT — это параметризованные ядра нейронных сетей, таких как свертки разных размеров, ресайзы или пулинги. Возьмем MaxPool, у которого несколько параметров: размер окна, stride и dilation. По классике сначала пишется общая реализация — медленная, но работающая при любых комбинациях параметров. Затем выбираются несколько наиболее часто употребляемых комбинаций ({3x3, stride 1x1}, {3x3, stride 2x2}, {5x5, stride 1x1},...) и переписываются оптимально. Но таких комбинаций много не напишешь, а их число очень быстро растет, увеличивая размер исходника и трудозатраты на написание и поддержку.

Наличие JIT же позволяет реализовать «метаоперацию MaxPool», которая создаст оптимальный код MaxPool для конкретных параметров, конкретного типа данных (float, uint8, …) и под конкретное железо (например, ARM v8.2).

В качестве PoC библиотеки Loops мы ускоряли ядра из экспериментального движка ficusNN, в частности MaxPool. MaxPool — сепарабельное ядро, то есть вместо O(N2) прохода по пикселям окна, его можно разбить на два O(N) прохода, сначала по вертикалям, потом по горизонталям. Однако, результат первого прохода нужно где-то хранить, обычно для этого используют память, а нам, благодаря JIT, удалось использовать под хранение промежуточного результата регистры. В виде обобщенной AOT-реализации сделать это в принципе невозможно: регистры хардкодятся в бинарное представление программы, перебирать их в цикле по номерам нельзя. Поэтому, либо вручную для отдельных комбинаций ядер, либо обобщенно с помощью JIT. В результате на типичных ядрах мы проигрывали ядрам ручной оптимизации небольшие проценты почти в пределах погрешности, а на экзотических, вроде 13 на 13, которые встречаются, например, в YOLO, получали ускорение в десятки раз, что позволяло ощутимо ускорять всю нейросеть.

Пример 3. Loop fusion

Лучший пример — это loop fusion, вынесенный в заголовок и давший название проекту. Два идущих подряд цикла с одним диапазоном и порядком, с независимыми данными или данными, зависимость которых можно уложить в итерацию. Циклы можно объединить в один, что позволит уменьшить накладные расходы на организацию циклов, количество операций чтения и записи, а если данные достаточно большие, то избежать лишней прокачки кэша.

Например, когда мы загружаем в память нейронную сеть и строим граф вычислений, мы можем проанализировать граф и понять, что какие-то операции можно и нужно объединить. В качестве примера идеально подойдет применение функций активации после какой-нибудь сложной по данным операции. Применение функции активации это поэлементная операция, поэтому может быть без труда подклеена к предыдущему слою, например к свертке или к полносвязному слою (матричному умножению).

Схема работы AOT- и JIT-компиляторов
Схема работы AOT- и JIT-компиляторов

Применимость JIT

В общем нам, как оптимизаторам, понятно, что JIT – это хорошо, векторный JIT — очень хорошо. А есть ли недостатки? В теории — нет. А вот чтобы решить, нужен ли вам на практике в вашем проекте JIT, нужно для себя ответить на несколько вопросов:

  • Скорость компиляции. Все-таки мы занимаемся оптимизацией, поэтому будет странно, если мы полминуты будем собирать код, чтобы ускорить его исполнение на долю секунды.

  • Добавление зависимостей. Компиляторы — это обычно большие проекты, интегрировать их в свой проект — значит усложнить сборку, число трудозатрат на поддержку этой сборки и заметно увеличить объем приложений.

  • Простота использования JIT. В идеале нам хотелось бы писать код как обычно мы пишем код с интринсиками, при этом каким-то образом указывая, какие переменные станут в собранном коде константным значением параметра, а какие будут именно переменными.

  • Кросс-платформенность. Векторизация — это низкоуровневая практика, сильно завязанная на архитектуру. Можно ли как-то добиться независимости наших кодогенераторов от типа машины?

Приглашаем в ИИ-команду YADRO, у нас есть задачи практически на любой вкус и для любого уровня:

→ AI Tech Lead (LLM)
→ Senior AQA Engineer (AI)
→ Product Owner AI Solutions

Мы, в составе небольшой команды из Вадима Писаревского и меня, постарались дать хорошие ответы на эти вопросы в нашем open source-проекте.

Мы задаем эти вопросы не в вакууме: в корпоративных планшетах и других персональных устройствах заказчиков любые «дорогие» решения быстро проявляются в эксплуатации — в автономности, температуре, SLA и стоимости владения. Поэтому R&D-задачи по JIT/векторизации у нас формулируются от требований клиентов: ускорить конкретный пайплайн, уложиться в энергобюджет, убрать зависимость от сети/облака, обеспечить воспроизводимость производительности на парке устройств.

Истоки проекта

Одним из источников вдохновения для нас стали universal intrinsics – технология из OpenCV. Фактически это abstraction layer. Такой подход позволяет писать код на векторных интринсиках независимо от платформы. Среди поддерживаемых платформ, например, Intel (AVX2/AVX512), ARM (Neon) и RISC-V (RVV). Благодаря технологии universal intrinsic большая часть векторизованных алгоритмов OpenCV написана разом под все поддерживаемые CPU-архитектуры. Это AOT-технология, поэтому ожидать способности избегать комбинаторного взрыва или ускорения кода с помощью слияния циклов тут не нужно.

Интересно, что установить полное соответствие между разными архитектурами невозможно: ряда инструкций нет в NEON, а других — в AVX2. Поэтому abstraction layer иногда приходится разворачивать отдельные его инструкции в небольшие последовательности нативных интринсиков конкретной платформы. Рассмотрим тот же пример с blur 3x3 на универсальных интринсиках:

Код
cv::Mat src, dst;
// Специальный тип регистра с четырьмя float'ми
float32x4_t denominator = vdupq_n_f32(1./9.);
constexpr int NEON_32BIT_LANES = 4;
for(int y = 0; y < hd; y++)
{
    float* srcPrevLine = src.ptr<float>(y-1);
    float* srcCurLine  = src.ptr<float>(y);
    float* srcNextLine = src.ptr<float>(y+1);
    float* dstCurLine  = dst.ptr<float>(y);
    // Будем обрабатывать пиксели не по одному, а по четыре.
    for(int x = 0; x <= wd - NEON_32BIT_LANES; x+=NEON_32BIT_LANES)
    {
        float32x4_t sum = vld1q_f32(srcPrevLine + x - 1);
        // Эти функции со специфическими названиями и есть
        // интринсики, add - суммирование, ld - загрузка из
        // памяти, mul - умножение, st - выгрузка в память.
        // Немного практики - и все становится довольно просто.
        sum   = vaddq_f32(sum, vld1q_f32(srcPrevLine + x    ));
        sum   = vaddq_f32(sum, vld1q_f32(srcPrevLine + x + 1));
        sum   = vaddq_f32(sum, vld1q_f32(srcCurLine  + x - 1));
        sum   = vaddq_f32(sum, vld1q_f32(srcCurLine  + x    ));
        sum   = vaddq_f32(sum, vld1q_f32(srcCurLine  + x + 1));
        sum   = vaddq_f32(sum, vld1q_f32(srcNextLine + x - 1));
        sum   = vaddq_f32(sum, vld1q_f32(srcNextLine + x    ));
        sum   = vaddq_f32(sum, vld1q_f32(srcNextLine + x + 1));
        vst1q_f32(dstCurLine + x, vmulq_f32(sum, denominator));
    }
}

Второй и главный исток проекта, его прародитель — это Xbyak, библиотека JIT-кодогенерации от Сигэо Мицунари (Shigeo Mitsunari). Она используется в Intel OpenVINO для ускорения инференса нейронных сетей за счет компиляции параметризованных ядер (слоев нейронных сетей), специализации обобщенного кода и слияния циклов. Есть несколько версий Xbyak для:

Все версии Xbyak либо предоставляют возможность закодировать весь набор ассемблерных инструкций конкретной архитектуры, либо стремятся к этому. И это можно только приветствовать! Но именно поэтому нет особого смысла объединять эти проекты в один: точное следование архитектуре процессора ведет к необходимости писать под разные архитектуры разный пользовательский код.

Работа c Xbyak похожа на работу с ассемблером. Вы условно объявляете функцию (на самом деле создаете буфер в памяти) и начинаете вызывать «инструкции», то есть функции, имена которых повторяют названия ассемблерных инструкций. При вызове этих функций вместо исполнения инструкций происходит их добавление в конец буфера. Когда функция завершена, под нее специальным образом выделяется память, на нее можно получить указатель и запустить. Из-за полного соответствия набора функций ассемблерным командам трансляция происходит мгновенно. Это преимущество. Поскольку это JIT-кодогенерация, открываются все возможности ускорения с помощью специализации и слияния циклов.

Главная головная боль программиста на Xbyak – это распределение регистров. Любой реальный процессор имеет ограниченное количество регистров, которые с некоторой натяжкой можно спроецировать на понятие переменной. Например, у x86_64 — 16 регистров общего назначения и 16 векторных в AVX2/SSE или, если есть AVX512 — 32 векторных. Если отрезок кода использует больше переменных, их надо сохранять на стеке до момента использования (то есть имеет место так называемый register spilling).

В Xbyak процессами распределения регистров, загрузки, выгрузки переменных, отслеживания текущей локации — как в стеке, так и на регистре — управляет программист. Это тяжелая рутинная работа, знакомая всем, кто пишет на ассемблере. При использовании C/C++ или любого другого языка высокого уровня эти вопросы возьмет на себя компилятор, разгрузив голову человека. Но в Xbyak, несмотря на то что мета-функция пишется на C++, мы снова спускаемся на уровень ассемблера. В Loops мы тоже решаем этот вопрос. Это, пожалуй, его главное преимущество перед Xbyak.

Схожее преимущество — это работа с вызовами функций. Даже у одного процессора на разных операционных системах может использоваться разный calling convention – договоренность о том, как функции передаются аргументы. Часть из них размещается в регистрах, часть определенным образом укладывается в стек. Для правильного извлечения аргументов и подготовки фрейма стека у функций есть пролог и эпилог, которые в рамках работы с xbyak также должен писать программист. Поэтому одну и ту же Xbyak-функцию нужно писать по-разному для Windows и Linux. В Loops мы автоматизировали эту работу.

Пример с blur 3x3 выглядит громоздко, но зато мы в рантайме можем менять этот код в зависимости от наших потребностей:

Код
// Этот отрезок кода отличается от кода на NEON-интринсиках
// ровно одним: он запустится на Intel и Risc-V так же,
// как на Arm.
cv::Mat src, dst;
v_float32x4 denominator = v_setall_f32(1./9.);
constexpr int UI128_32BIT_LANES = 4;
for(int y = 0; y < hd; y++)
{
    float* srcPrevLine = src.ptr<float>(y-1);
    float* srcCurLine  = src.ptr<float>(y);
    float* srcNextLine = src.ptr<float>(y+1);
    float* dstCurLine  = dst.ptr<float>(y);
    for(int x = 0; x <= wd - UI128_32BIT_LANES; x+=UI128_32BIT_LANES)
    {
        v_float32x4 sum = v_load(srcPrevLine + x - 1);
        sum   = v_add(sum, v_load(srcPrevLine + x    ));
        sum   = v_add(sum, v_load(srcPrevLine + x + 1));
        sum   = v_add(sum, v_load(srcCurLine  + x - 1));
        sum   = v_add(sum, v_load(srcCurLine  + x    ));
        sum   = v_add(sum, v_load(srcCurLine  + x + 1));
        sum   = v_add(sum, v_load(srcNextLine + x - 1));
        sum   = v_add(sum, v_load(srcNextLine + x    ));
        sum   = v_add(sum, v_load(srcNextLine + x + 1));
        v_store(dstCurLine + x, v_mul(sum, denominator));
    }
}

Loops. Определение

Loops – это минималистичная скоростная кросс-платформенная C++-библиотека JIT-кодогенерации с распределением регистров, которая предназначена для написания векторного кода. То есть Loops — это JIT-компилятор, оформленный как библиотека, для удобной интеграции его в пользовательские проекты. Основные особенности — ниже.

Минимализм — не пытаясь повторить набор инструкций современных процессоров во всей их полноте, библиотека предоставляет все необходимые целочисленные скалярные инструкции для организации логики программы: базовых вычислений, циклов, условий, а также векторные инструкции, где в полной мере поддерживаются целочисленные операции и операции с плавающей запятой для всех основных типов данных, от 8-битных целых (int8_t/uint8_t) до 64-битных вещественных (double).

Кросс-платформенность — целочисленные скалярные инструкции реализованы для Intel/AMD x64, ARM aarch64 и RISC-V (тоже 64-бит). Векторные инструкции пока реализованы только для x64 (AVX2) и aarch64 (NEON), поддержка расширения RVV для RISC-V в среднесрочных планах. В отличие от Xbyak, ключевая кросс-платформенная особенность Loops — использование одного и того же имени для семантически одинаковых операций, независимо от платформы. Например, вместо mm256max_ps (интринсик для AVX2) или vmaxq_f32 (аналогичный интринсик для NEON) мы просто пишем loops::max, а тип операции (f32) будет автоматически выведен из типов аргументов.

Поддержка операционных систем:

  • Windows (x86_64),

  • Linux (x86_64, aarch64, riscv64),

  • MacOS (aarch64).

Из кроссплатформенности следует необходимость в платформо-независимом промежуточном представлении, о нем подробнее будет рассказано чуть ниже.

Скорость — Loops является очень быстрым многопроходным компилятором. Каждая функция компилируется независимо, и каждый проход имеет теоретическую сложность не больше чем O(N2) от числа команд N в функции, да и те в-среднем работают за O(N) или O(N*log(N)). На данный момент проект еще не профилировался, но предполагается, что можно дополнительно ускорить компиляцию за счет правильного подбора std-контейнеров, мелкой алгоритмической оптимизации и так далее. Loops сложно назвать оптимизирующим компилятором, предполагается, что он будет использоваться людьми с опытом векторизации с помощью интринсиков. Это очевидный и главный недостаток при сравнении его с большими компиляторными проектами вроде LLVM.

Распределение регистров — из-за желания добиться максимальной скорости компиляции вместо тяжеловесных алгоритмов раскраски графа используется усовершенствованный вариант простого алгоритма linear scan. Опыт показывает, что для наших задач этого вполне достаточно для получения качественного кода, особенно на современных платформах вроде ARM или RISC-V с большим количеством регистров: 32. Качество определяется количеством промежуточных сохранений регистров на стек с последующим восстановлением (так называемых «спиллов» / register spill). Чем их меньше, тем лучше. Важнее всего минимизировать количество «спиллов» во внутренних циклах, чего наша реализация linear scan и пытается добиться.

Простота сборки — хотя Loops, в отличие от Xbyak, не является суперлегковесным проектом, состоящим всего из нескольких заголовочных файлов, но довольно к этому близок — он состоит из нескольких заголовочных и исходных файлов на С++. Собирается с помощью CMake и не имеет внешних зависимостей.

Как упростить процесс отладки в CMake с помощью встроенного отладчика и профилировщика — читайте в статье «Как победить CMake: отладка CMake-скриптов».

Внутренняя машинерия

Разберемся с архитектурными решениями проекта.

Кросс-платформенность неизбежно требует введения IR. Исторически, первой архитектурой, которую мы поддержали в Loops, была ARM aarch64 с NEON, поэтому во многом IR повторяет их систему команд, в целом, довольно полную и удобную. Loops позволяет для каждой сгенерированной функции распечатать IR. Также можно распечатать в виде ассемблера получившийся из IR машинный код.

Исходя из минимализма, мы решили сделать структуры данных максимально простыми. IR-инструкции укладываются в простую планарную последовательность, без деревьев вроде AST. Это помешало бы написанию сложных оптимизирующих проходов, но для наших задач в самый раз — просто и быстро. Впрочем, на самом раннем этапе, при сборе кода в буфер, деревья все-таки используются, но это касается в основном интерфейса, а основная работа происходит над планарным представлением.

Идеальной простоты Xbyak, где псевдоинструкция сразу транслирует код и добавляет его в буфер, нам достигнуть не удастся, да и не хочется. Тут есть проходы по функциям как массивам IR-инструкций, уложенные в пайплайн, выполняющий компиляторную работу. Ниже мы разберем проходы по отдельности.

Схема пайплайна Loops
Схема пайплайна Loops

Интерфейс Loops имитирует привычные действия с кодом, как это делает Xbyak, но при этом содержит гораздо больше синтаксического сахара. Здесь вы не встретите команду mov — вместо нее нужно присваивать переменные и делать арифметические операции, что достигается благодаря агрессивной перегрузке операторов. Есть конструкции для цикла while и ветвлений. Одна из странностей — отсутствие оператора goto, что компенсируется другой странностью — операторами break(x) и continue(x), которые позволяют выходить не из самого вложенного цикла, а из нескольких вложений.

В течение работы проходы анализируют инструкции, опрашивая объект бэкэнда для выявления различных свойств той или иной инструкции. Например, самый первый проход после сбора буфера функции — это CP_IMMEDIATE_IMPLANTATION. Он определяет, можно ли вписать константный аргумент прямо в тело инструкции, или придется добавить отдельный mov. На Arm и RISC-V с их фиксированным размером инструкций такие вопросы возникают особенно часто. Для этого достаточно посмотреть, сколько места в бинарном представлении инструкции отведено под операнд, то есть рассмотреть энкодинг, который формируется в самом конце пайплайна и сильно зависит от целевой платформы. Проход работает с платформо-независимым IR, но при этом вынужден опираться на свойства инструкций нативной архитектуры, как бы подсматривать, что будет с этой IR-инструкцией в конце пайплайна. Такие опросы о свойствах инструкции распространены во всем пайплайне и обобщенно называются концепцией peeking. Она делает проходы универсальными, а всю необходимую информацию об инструкциях компактно собирает в одном месте: в энкодинге.

Список компиляторных проходов

Рассмотрим проходы по порядку. Префикс CP_ означает compiler pass.

  1. CP_COLLECTING — это метапроход, обозначающий состояние до начала основной обработки, в котором происходит сбор инструкций для функции, которую мы будем компилировать. Интерфейс при этом все же выполняет некоторую работу: например, разворачивает деревья выражений для присваиваний и использования в условиях циклов и ветвлений. На этом этапе циклы и ветвления помечаются специальными аннотационными инструкциями, которые позже исчезнут из кода. Например, для циклов есть три такие аннотации: annotation:whilecstart, annotation:whilecend и annotation:endwhile. Это компенсирует отсутствие древовидной структуры кода и фактически позволяет воссоздавать ее по этим аннотациям в тех проходах, где она нужна: анализе времени жизни и аллокации регистров.

  2. CP_IMMEDIATE_IMPLANTATION — уже упомянутый проход, распределяющий константные значения в телах инструкций, делая peeking энкодинга для определения ширины поля в битах.

  3. CP_ELIF_ELIMINATION — удаляет elif-инструкции, превращая их в систему вложенных if-else-endif.

  4. CP_<ARCHNAME>_BRA_SNIPPETS. BRA_SNIPPETS расшифровывается как Before Register Allocation Snippets и решает задачу, близкую к Universal Intrinsics в OpenCV: разворачивает инструкцию в последовательность инструкций, если на текущей архитектуре нет ее прямого аналога. <ARCHNAME> выделено потому, что на всех машинах имя отличается. В Loops есть система платформо-зависимых проходов, которые добавляются в двух точках: до аллокации регистров и после. Это задел под систему пользовательских проходов.

  5. CP_LIVENESS_ANALYSIS — один из двух наиболее алгоритмически сложных проходов. Он выполняет подготовительную работу для прохода распределения регистров и тесно с ним связан. На выходе у него не только и не столько преобразованный код, сколько карта времен жизни переменных. Кроме того, проход разбивает время жизни переменных на минимально возможные отрезки, что улучшает качество работы аллокатора регистров. Интересно, что в общем случае эта задача решается за O(N^4), однако отказ от произвольных goto позволяет снизить сложность до O(N^2), а в большинстве случаев — до O(M*N), где N — число инструкций, а M — число базовых блоков. Именно поэтому в Loops нет произвольных goto, но есть break(x) и continue(x). На этом этапе выполняется peeking информации о том, какие аргументы инструкции являются входными, а какие — выходными.

  6. CP_REGISTER_ALLOCATION — сердце Loops, собирающее все ниточки в одном месте. Здесь происходит переход от бесконечного числа скалярных и векторных переменных к конечному числу регистров, доступных на целевой машине. Если регистра под переменную не хватает, место под нее выделяется на стеке и добавляются инструкции spill и unspill. Вместо тяжеловесных алгоритмов раскраски графа используется простой, быстрый и, на практике, вполне эффективный алгоритм линейного сканирования. [Poletto, M. Linear scan register allocation / M. Poletto, V. Sarkar // ACM Transactions on Programming Languages and Systems (TOPLAS). —1999. — Т. 21, № 5. — С. 895—913.] Удивительно, но и этот, и предыдущий проход удалось сделать платформо-независимыми, ограничившись peeking энкодинга. Здесь же добавляются пролог и эпилог функции — они тесно связаны с набором использованных регистров.

  7. CP_CONTROLFLOW_TO_JUMPS — простой проход, который удаляет annotation-инструкции и заменяет их обычными командами условного и безусловного перехода.

  8. CP_<ARCHNAME>_ARA_SNIPPETS — это технические ARA-сниппеты (After Register Allocation), которые зависят не только от платформы, но и от ее регистров, поэтому выполняются после распределения регистров. Например, операция скалярного целочисленного деления на Intel требует, чтобы делимое и результат деления находились в регистре rax. Поэтому rax сохраняется на стеке, используется для деления, а затем восстанавливается. Одна из самых важных задач, которые решают ARA-сниппеты — вызов функций из сгенерированного кода.

  9. CP_IR_TO_ASSEMBLY — преобразование общего IR в ассемблерное представление. К этому этапу почти все инструкции переводятся простым отображением «команда в команду», «аргумент в аргумент». Иногда нужно поменять порядок аргументов или добавить дополнительные, но это несложная работа. Кроме того, на этом этапе появляется информация о размере кода, а значит о сдвигах, поэтому здесь переходы на метки превращаются в переходы на адреса, для вычисления этих сдвигов peeking запрашивает размеры команд.

  10. CP_ASSEMBLY_TO_HEX — перевод ассемблерного представления в бинарный вид и формирование тела функции в специально выделенном буфере. В основном это просто склеивание бинарных полей, но иногда бывают сложные случаи: например, с большими константами на Arm, где в ряде инструкций нужно обрабатывать как маленькие immediate, так и большие, из-за чего способ кодирования весьма нетривиальный. Энкодинг на Intel с его бесконечными слоями legacy самый громоздкий и запутанный. В то же время у минималистичного RISC-V есть всего четыре-пять заранее определенных форматов команд, работать с ним одно удовольствие.

Примеры использования Loops

Пример 1. Сложение

Для начала создадим какой-нибудь совсем простой пример и посмотрим, как он скомпилируется на разных машинах. Hello world на ассемблере писать не так-то просто, поэтому начнем с функции, которая складывает два входных числа:

#include <iostream>
#include <loops/loops.hpp>


int main()
{
    loops::Context CTX;          // Создаем контекст 
    USE_CONTEXT_(CTX);           // Обеспечиваем корректную работу макросов
    loops::IReg a, b;            // Создаем переменные-аргументы функции
    STARTFUNC_("sum", &a, &b)    // Объявляем новую функцию с именем и аргументами
    {                         
        loops::IReg c = a + b;   // Объявили переменную и присвоили ей результат сложения
        RETURN_(c);              // Макрос для возврата
    } 
    loops::Func sfunc = CTX.getFunc("sum"); // Достали по имени функцию из контекста
    std::cout << "=========IR LISTING=========\n"; 
    sfunc.printIR(std::cout);               // Печать IR(самый поздний проход)
    std::cout << "\n========ASM LISTING=========\n";
    sfunc.printAssembly(std::cout);         // Печать ассемблерного кода
    typedef int64_t (*sum_t)(int64_t a, int64_t b);
    sum_t func = (sum_t)sfunc.ptr();        // Компилируем и достаем указатель на функцию
    std::cout << "\n\n5 + 4 = " << func(5, 4) << "\n"; // Вызываем наш первый JIT!
    return 0;
}

Итак, по пунктам:

  1. Сначала нужно создать контекст (loops::Context). Это контейнер функций, с помощью него они создаются и удаляются, доступ к функциям по именам.

  2. Если вы собираетесь что-то писать в буфер текущей функции, то нужно инициализировать макросы с помощью USE_CONTEXT_. Если только брать уже написанные функции, печатать их код или запускать, USE_CONTEXT_ не нужен.

  3. Благодаря USE_CONTEXT_ хорошо сработает следующий макрос — STARTFUNC_, который «объявляет» функцию. В фигурных скобках внутри можно писать весь нужный вам код. Само собой, описываемые вами действия — не выполняются, вы только добавляете в буфер их последовательность, прямо во время выполнения программы решая, какую последовательность действий туда записать.

  4. Loops::IReg — абстракция для регистра общего назначения, в более широком смысле — это переменная. Это всегда 64-битное знаковое целое, но вы можете загружать туда и выгружать оттуда типы меньших размеров — поддержаны соответствующие инструкции.

  5. Loops::IReg должен объявляться только внутри блока объявленной функции, иначе будут ошибки. Единственное исключение — это объявление аргументов функций. Они должны объявляться по умолчанию и подаваться в макрос STARTFUNC_ для обозначения аргументов генерируемой нами функции.

  6. Внутри функции вы можете пользоваться loops::IReg, как обычной переменной: складывать, присваивать и передавать в функции. Она будет вести себя примерно так же, как и обычная переменная. Однако, на первых порах не рекомендую объявлять переменные по умолчанию, потому что им неоткуда будет взять информацию о контексте.

  7. Loops::Func описывает функцию, из нее можно получить указатель void* на скомпилированный код. Также позволяет распечатать функцию, причем на разных стадиях обработки: можно указать имя прохода, и printIR выведет код после применения этого прохода.

  8. Перед использованием этот указатель надо преобразовать в соответствии с сигнатурой, и можно выполнять свежескомпилированный код.

Что выведет эта программа на Windows+x86_64:

=========IR LISTING=========
sum(i0, i1)
     0 : add i1, i1, i2
     1 : mov i0, i1
     2 : ret

========ASM LISTING=========
sum(i0, i1)
     0 : add rcx, rdx ; 48 01 d1
     1 : mov rax, rcx ; 48 89 c8
     2 : ret          ; c3


5 + 4 = 9

Отмечу, что ASM listing будет выглядеть иначе, даже если просто перейти с одной операционной системы на другую, в нашем случае на Linux. Это связано с тем, что в Windows и Linux используются разные соглашения о том, через какие регистры передаются аргументы:

========ASM LISTING=========
sum(i0, i1)
     0 : add rdi, rsi ; 48 01 f7  
     1 : mov rax, rdi ; 48 89 f8  
     2 : ret          ; c3

Тот же листинг на Arm:

========ASM LISTING=========
sum(i0, i1)
     0 : add x0, x0, x1 ; 00 00 01 8b  
     1 : ret x30        ; c0 03 5f d6

И на RISC-V:

========ASM LISTING=========
sum(i0, i1)
     0 : add a0, a0, a1 ; 33 05 b5 00  
     1 : ret            ; 67 80 00 00

Пример 2. Сортировка

Первый пример был довольно простым, поэтому напишем программу посложнее, чтобы посмотреть, как организуются циклы и ветвления.

Далее я привожу примеры на Arm, чтобы не тратить время на объяснения архитектурных различий.

Рассмотрим простую сортировку целых 32-битных знаковых чисел:

#include <iostream>
#include <loops/loops.hpp>


int main()
{
    loops::Context CTX;
    USE_CONTEXT_(CTX);
    loops::IReg ptr32, size;
    STARTFUNC_("sort", &ptr32, &size)
    {
        loops::IReg curnum = CONST_(0);   // Начальное значение переменной задается с помощью макроса CONST_
        WHILE_(curnum < size - 1)         // Цикл, как цикл. Но генерирует код, а не гоняет итерации.
        {
            loops::IReg minnum = curnum;  
            loops::IReg searchnum = curnum + 1;
            WHILE_(searchnum < size - 1)
            {
                // Обращение к элементам массива - задача чуть более сложная, чем в Си. И обратите
                // внимание, что offset домножается на размер int32_t. Без типизации 
                // адресная арифметика становится сложнее.
                loops::IReg minval    = loops::load_<int32_t>(ptr32, minnum * sizeof(int32_t));
                loops::IReg searchval = loops::load_<int32_t>(ptr32, searchnum * sizeof(int32_t));
                IF_(searchval < minval)
                    minnum = searchnum;
                searchnum = searchnum + 1;
            }
            loops::IReg curval    = loops::load_<int32_t>(ptr32, curnum * sizeof(int32_t));
            loops::IReg minval    = loops::load_<int32_t>(ptr32, minnum * sizeof(int32_t));
            loops::store_<int32_t>(ptr32, curnum * sizeof(int32_t), minval);
            loops::store_<int32_t>(ptr32, minnum * sizeof(int32_t), curval);
            curnum = curnum + 1;
        }
    } 
    loops::Func sfunc = CTX.getFunc("sort");
    std::cout << "=========IR LISTING=========\n"; 
    sfunc.printIR(std::cout);
    std::cout << "\n========ASM LISTING=========\n";
    sfunc.printAssembly(std::cout);
    typedef void (*sort_t)(int32_t* ptr32, int64_t size);
    sort_t func = (sort_t)sfunc.ptr();
    std::vector<int32_t> toSort = {5, 2, 15, -4, 10};
    func(toSort.data(), (int64_t)toSort.size());
    std::cout << "\n========SORTED VALUES=========\n";
    for(int i = 0; i < (int)toSort.size(); i++)
        std::cout << toSort[i] << std::endl;
    return 0;
}
Вывод
=========IR LISTING=========
sort(i0, i1)
     0 : mov              i2, 0
     1 : __loops_label_0:
     2 : sub              i3, i1, 1
     3 : cmp              i2, i3
     4 : jmp_ge           __loops_label_2
     5 : mov              i3, i2
     6 : add              i4, i2, 1
     7 : __loops_label_3:
     8 : sub              i5, i1, 1
     9 : cmp              i4, i5
    10 : jmp_ge           __loops_label_5
    11 : mov              i5, 4
    12 : mul              i5, i3, i5
    13 : load.i32         i5, i0, i5
    14 : mov              i6, 4
    15 : mul              i6, i4, i6
    16 : load.i32         i6, i0, i6
    17 : cmp              i6, i5
    18 : jmp_ge           __loops_label_7
    19 : mov              i3, i4
    20 : __loops_label_7:
    21 : add              i4, i4, 1
    22 : jmp              3
    23 : __loops_label_5:
    24 : mov              i4, 4
    25 : mul              i4, i2, i4
    26 : load.i32         i4, i0, i4
    27 : mov              i5, 4
    28 : mul              i5, i3, i5
    29 : load.i32         i5, i0, i5
    30 : mov              i6, 4
    31 : mul              i6, i2, i6
    32 : store.i32        i0, i6, i5
    33 : mov              i5, 4
    34 : mul              i3, i3, i5
    35 : store.i32        i0, i3, i4
    36 : add              i2, i2, 1
    37 : jmp              0
    38 : __loops_label_2:
    39 : ret

========ASM LISTING=========
sort(i0, i1)
     0 : eor   x2, x2, x2       ; 42 00 02 ca
     1 :       __loops_label_0: ;
     2 : sub   x3, x1, #0x01    ; 23 04 00 d1
     3 : cmp   x2, x3           ; 5f 00 03 eb
     4 : b.ge  __loops_label_2  ; ea 03 00 54
     5 : mov   x3, x2           ; e3 03 02 aa
     6 : add   x4, x2, #0x01    ; 44 04 00 91
     7 :       __loops_label_3: ;
     8 : sub   x5, x1, #0x01    ; 25 04 00 d1
     9 : cmp   x4, x5           ; 9f 00 05 eb
    10 : b.ge  __loops_label_5  ; 8a 01 00 54
    11 : mov   x5, #0x04        ; 85 00 80 d2
    12 : mul   x5, x3, x5       ; 65 7c 05 9b
    13 : ldrsw x5, [x0, x5]     ; 05 68 a5 b8
    14 : mov   x6, #0x04        ; 86 00 80 d2
    15 : mul   x6, x4, x6       ; 86 7c 06 9b
    16 : ldrsw x6, [x0, x6]     ; 06 68 a6 b8
    17 : cmp   x6, x5           ; df 00 05 eb
    18 : b.ge  __loops_label_7  ; 4a 00 00 54
    19 : mov   x3, x4           ; e3 03 04 aa
    20 :       __loops_label_7: ;
    21 : add   x4, x4, #0x01    ; 84 04 00 91
    22 : b     __loops_label_3  ; f3 ff ff 17
    23 :       __loops_label_5: ;
    24 : mov   x4, #0x04        ; 84 00 80 d2
    25 : mul   x4, x2, x4       ; 44 7c 04 9b
    26 : ldrsw x4, [x0, x4]     ; 04 68 a4 b8
    27 : mov   x5, #0x04        ; 85 00 80 d2
    28 : mul   x5, x3, x5       ; 65 7c 05 9b
    29 : ldrsw x5, [x0, x5]     ; 05 68 a5 b8
    30 : mov   x6, #0x04        ; 86 00 80 d2
    31 : mul   x6, x2, x6       ; 46 7c 06 9b
    32 : str   w5, [x0, x6]     ; 05 68 26 b8
    33 : mov   x5, #0x04        ; 85 00 80 d2
    34 : mul   x3, x3, x5       ; 63 7c 05 9b
    35 : str   w4, [x0, x3]     ; 04 68 23 b8
    36 : add   x2, x2, #0x01    ; 42 04 00 91
    37 : b     __loops_label_0  ; e0 ff ff 17
    38 :       __loops_label_2: ;
    39 : ret   x30              ; c0 03 5f d6

========SORTED VALUES=========
-4
2
5
15
10

Посмотрим, на что в этом примере стоит обратить внимание.

В коде можно встретить макрос CONST_ при объявлении переменных. Дело в том, что Loops должен помещать операции в буфер программы, а для этого ему нужно знать в какой именно буфер. Эта информация обычно хранится в операндах — уже объявленных IReg. Это сделано ради синтаксического сахара и лаконичности выражений. Однако только конструирующемуся IReg взять эту информацию неоткуда. CONST_ порождает аргумент уже с ней, подключая IReg к буферу. Также можно объявить переменную без инициализации, с помощью макроса DEF_().

В Loops переменные описывают обобщенные регистры, что характерно для низкоуровневого кода. Память при этом рассматривается как внешняя среда, взаимодействие с которой происходит через специальные операции ввода/вывода (load/store). Также нужно обратить внимание на особенности адресной арифметики. Типизация указателей не используется, поэтому указатели и их смещения измеряются в байтах. Возможно, позже мы это улучшим.

Пример 3. Векторы и вызовы

Концептуально loops выстроен вокруг SIMD-регистров. Давайте поработаем с ними и напишем простой одномерный maxpool: в каждом элементе целевого массива — максимальное значение из трех подряд идущих элементов оригинала.

Код упрощенный, размеры входных массивов нужно проверять снаружи. Чтобы показать еще что-нибудь интересное, добавим отладочный вызов, который будет выводить некоторые значения:

// Аргументы вызываемых функций - только int64_t
// Возврат - либо void, либо int64_t
void debug_print(int64_t a)
{
    float* a_f32 = (float*)(void*)(&a);
    printf("Loops debug: %f\n", *a_f32);
}


//...
STARTFUNC_("maxpool1d_3", &ptrin_f32, &ptrout_f32, &sizeout)
{ 
    loops::IReg curpos = CONST_(0);
    WHILE_(curpos < sizeout)
    {
        // Векторные регистры типизированы, благодаря чему под арифметические и иные действия
        // автоматически подбираются корректные инструкции
        loops::VReg<float> in0 = loops::loadvec<float>(ptrin_f32, curpos       * sizeof(float));
        loops::VReg<float> in1 = loops::loadvec<float>(ptrin_f32, (curpos + 1) * sizeof(float));
        loops::VReg<float> in2 = loops::loadvec<float>(ptrin_f32, (curpos + 2) * sizeof(float));
        loops::VReg<float> out = loops::max(loops::max(in0, in1), in2);
        VOID_CALL_(debug_print, loops::getlane(out, 1)); // Вызов функции с одним аргументом
        loops::storevec(ptrout_f32, curpos * sizeof(float), out);
        curpos = curpos + CTX.vlanes<float>();// Количество lane'ов в векторе на разных машинах разное
    }
} 
Полученный код
=========IR LISTING=========
maxpool1d_3(i0, i1, i2)
     0 : sub              i31, i31, 416
     1 : arm_stp          i31, 400, i29, i30
     2 : mov              i29, i31
     3 : mov              i3, 0
     4 : __loops_label_0:
     5 : cmp              i3, i2
     6 : jmp_ge           __loops_label_2
     7 : mov              i4, 4
     8 : mul              i4, i3, i4
     9 : vld.fp32         v0, i0, i4
    10 : add              i4, i3, 1
    11 : mov              i5, 4
    12 : mul              i4, i4, i5
    13 : vld.fp32         v1, i0, i4
    14 : add              i4, i3, 2
    15 : mov              i5, 4
    16 : mul              i4, i4, i5
    17 : vld.fp32         v2, i0, i4
    18 : max.fp32         v0, v0, v1
    19 : max.fp32         v0, v0, v2
    20 : getlane.fp32     i4, v0, 1
    21 : mov              i5, 25416
    22 : arm_movk         i5, 47206, 16
    23 : arm_movk         i5, 43690, 32
    24 : arm_stp          i31, 0, i0, i1
    25 : arm_stp          i31, 16, i2, i3
    26 : arm_stp          i31, 32, i4, i5
    27 : arm_stp          i31, 48, i6, i7
    28 : arm_stp          i31, 64, i8, i9
    29 : arm_stp          i31, 80, i10, i11
    30 : arm_stp          i31, 96, i12, i13
    31 : arm_stp          i31, 112, i14, i15
    32 : arm_stp          i31, 128, i16, i17
    33 : mov              i0, i4
    34 : add              i10, i31, 144
    35 : vst_lane.u64     i10, v0
    36 : vst_lane.u64     i10, v4
    37 : vst_lane.u64     i10, v8
    38 : vst_lane.u64     i10, v12
    39 : call_noret       [i5]()
    40 : add              i10, i31, 144
    41 : vld_lane.u64     v0, i10
    42 : vld_lane.u64     v4, i10
    43 : vld_lane.u64     v8, i10
    44 : vld_lane.u64     v12, i10
    45 : arm_ldp          i0, i1, i31, 0
    46 : arm_ldp          i2, i3, i31, 16
    47 : arm_ldp          i4, i5, i31, 32
    48 : arm_ldp          i6, i7, i31, 48
    49 : arm_ldp          i8, i9, i31, 64
    50 : arm_ldp          i10, i11, i31, 80
    51 : arm_ldp          i12, i13, i31, 96
    52 : arm_ldp          i14, i15, i31, 112
    53 : arm_ldp          i16, i17, i31, 128
    54 : mov              i4, 4
    55 : mul              i4, i3, i4
    56 : vst.fp32         i1, i4, v0
    57 : add              i3, i3, 4
    58 : jmp              0
    59 : __loops_label_2:
    60 : arm_ldp          i29, i30, i31, 400
    61 : add              i31, i31, 416
    62 : ret

========ASM LISTING=========
maxpool1d_3(i0, i1, i2)
     0 : sub  sp, sp, #0x1a0                                 ; ff 83 06 d1
     1 : stp  x29, x30, [sp, #0x190]                         ; fd 7b 19 a9
     2 : mov  x29, sp                                        ; fd 03 1f aa
     3 : eor  x3, x3, x3                                     ; 63 00 03 ca
     4 :      __loops_label_0:                               ;
     5 : cmp  x3, x2                                         ; 7f 00 02 eb
     6 : b.ge __loops_label_2                                ; aa 06 00 54
     7 : mov  x4, #0x04                                      ; 84 00 80 d2
     8 : mul  x4, x3, x4                                     ; 64 7c 04 9b
     9 : ldr  q0, [x0, x4]                                   ; 00 68 e4 3c
    10 : add  x4, x3, #0x01                                  ; 64 04 00 91
    11 : mov  x5, #0x04                                      ; 85 00 80 d2
    12 : mul  x4, x4, x5                                     ; 84 7c 05 9b
    13 : ldr  q1, [x0, x4]                                   ; 01 68 e4 3c
    14 : add  x4, x3, #0x02                                  ; 64 08 00 91
    15 : mov  x5, #0x04                                      ; 85 00 80 d2
    16 : mul  x4, x4, x5                                     ; 84 7c 05 9b
    17 : ldr  q2, [x0, x4]                                   ; 02 68 e4 3c
    18 : fmax v0.4s, v0.4s, v1.4s                            ; 00 f4 21 4e
    19 : fmax v0.4s, v0.4s, v2.4s                            ; 00 f4 22 4e
    20 : umov w4, v0.s[1]                                    ; 04 3c 0c 0e
    21 : mov  x5, #0x6348                                    ; 05 69 8c d2
    22 : movk x5, #0xb866, lsl #16                           ; c5 0c b7 f2
    23 : movk x5, #0xaaaa, lsl #32                           ; 45 55 d5 f2
    24 : stp  x0, x1, [sp, #0]                               ; e0 07 00 a9
    25 : stp  x2, x3, [sp, #0x10]                            ; e2 0f 01 a9
    26 : stp  x4, x5, [sp, #0x20]                            ; e4 17 02 a9
    27 : stp  x6, x7, [sp, #0x30]                            ; e6 1f 03 a9
    28 : stp  x8, x9, [sp, #0x40]                            ; e8 27 04 a9
    29 : stp  x10, x11, [sp, #0x50]                          ; ea 2f 05 a9
    30 : stp  x12, x13, [sp, #0x60]                          ; ec 37 06 a9
    31 : stp  x14, x15, [sp, #0x70]                          ; ee 3f 07 a9
    32 : stp  x16, x17, [sp, #0x80]                          ; f0 47 08 a9
    33 : mov  x0, x4                                         ; e0 03 04 aa
    34 : add  x10, sp, #0x90                                 ; ea 43 02 91
    35 : st1  {v0.2d, v1.2d, v2.2d, v3.2d}, [x10], #0x40     ; 40 2d 9f 4c
    36 : st1  {v4.2d, v5.2d, v6.2d, v7.2d}, [x10], #0x40     ; 44 2d 9f 4c
    37 : st1  {v8.2d, v9.2d, v10.2d, v11.2d}, [x10], #0x40   ; 48 2d 9f 4c
    38 : st1  {v12.2d, v13.2d, v14.2d, v15.2d}, [x10], #0x40 ; 4c 2d 9f 4c
    39 : blr  x5                                             ; a0 00 3f d6
    40 : add  x10, sp, #0x90                                 ; ea 43 02 91
    41 : ld1  {v0.2d, v1.2d, v2.2d, v3.2d}, [x10], #0x40     ; 40 2d df 4c
    42 : ld1  {v4.2d, v5.2d, v6.2d, v7.2d}, [x10], #0x40     ; 44 2d df 4c
    43 : ld1  {v8.2d, v9.2d, v10.2d, v11.2d}, [x10], #0x40   ; 48 2d df 4c
    44 : ld1  {v12.2d, v13.2d, v14.2d, v15.2d}, [x10], #0x40 ; 4c 2d df 4c
    45 : ldp  x0, x1, [sp, #0]                               ; e0 07 40 a9
    46 : ldp  x2, x3, [sp, #0x10]                            ; e2 0f 41 a9
    47 : ldp  x4, x5, [sp, #0x20]                            ; e4 17 42 a9
    48 : ldp  x6, x7, [sp, #0x30]                            ; e6 1f 43 a9
    49 : ldp  x8, x9, [sp, #0x40]                            ; e8 27 44 a9
    50 : ldp  x10, x11, [sp, #0x50]                          ; ea 2f 45 a9
    51 : ldp  x12, x13, [sp, #0x60]                          ; ec 37 46 a9
    52 : ldp  x14, x15, [sp, #0x70]                          ; ee 3f 47 a9
    53 : ldp  x16, x17, [sp, #0x80]                          ; f0 47 48 a9
    54 : mov  x4, #0x04                                      ; 84 00 80 d2
    55 : mul  x4, x3, x4                                     ; 64 7c 04 9b
    56 : str  q0, [x1, x4]                                   ; 20 68 a4 3c
    57 : add  x3, x3, #0x04                                  ; 63 10 00 91
    58 : b    __loops_label_0                                ; cb ff ff 17
    59 :      __loops_label_2:                               ;
    60 : ldp  x29, x30, [sp, #0x190]                         ; fd 7b 59 a9
    61 : add  sp, sp, #0x1a0                                 ; ff 83 06 91
    62 : ret  x30                                            ; c0 03 5f d6

Код довольно наглядный, все же добавлю пару пояснений

CTX.vlanes<float> возвращает количество элементов данного типа, которые умещаются в векторном регистре. Если мы говорим про float, то на Intel (AVX2) их будет восемь, а на Arm (NEON) — четыре. Понятно, что если мы ведем обработку векторами, то «шагать» по массиву надо именно на такое количество.

Отладка JIT-кода — задача не из простых. В лучшем случае приходится «ползать» по ассемблеру. Даже простая отладочная печать не всегда доступна — нельзя просто вставить вызов printf в код.

С появлением поддержки вызовов функций ситуация немного упростилась. Сигнатура вызываемых функций сильно ограничена. Аргументы могут быть только типа int64_t. Их количество зависит от платформы: на Intel — до четырех аргументов на Windows и до шести на Linux, на ARM и RISC-V — до восьми. То есть максимальное количество аргументов совпадает с количеством аргументов, которые передаются через регистры согласно соглашению о вызове функций (calling convention). В коде можно увидеть много инструкций spill. Это префикс и постфикс вызова. Другими словами, вызовы хоть и есть, реализованы не очень хорошо и пока стоит их избегать, по крайней мере в нагруженных участках кода.

Пример 4. Параметрический поэлементный полином

Приведем, наконец, пример, с которым может справиться только JIT — параметрическая компиляция. Loops очень хорошо подходит для поэлементных или попиксельных операций, поэтому рассмотрим функцию, которая поэлементно высчитывает полином. Параметрами будут степень и коэффициенты этого полинома. Не будем все это сильно оптимизировать, напишем проще и посмотрим код, который получается в зависимости от входных данных:

loops::Func create_polynomic_function(loops::Context& CTX, const std::vector<float> ratios)
{
    USE_CONTEXT_(CTX);
    loops::IReg ptrin_f32, ptrout_f32, sizeout;
    STARTFUNC_("poly", &ptrin_f32, &ptrout_f32, &sizeout)
    {
        loops::IReg curpos = CONST_(0);
        std::vector<loops::VReg<float> > ratiosV(ratios.size()); 
        for(int rNum = 0; rNum < ratios.size(); rNum++)
            if(rNum == 0 || (ratios[rNum] != 0.f && ratios[rNum] != 1.f))
            {// Отложенная инициализация регистра необходима при работе с массивом VReg.
                ratiosV[rNum].copyidx(VCONST_(float, ratios[rNum]));
            }
        WHILE_(curpos < sizeout)
        {
            loops::VReg<float> result = ratiosV[0];
            loops::VReg<float> x = loops::loadvec<float>(ptrin_f32, curpos * sizeof(float));
            // res = ratios[0] + ratios[1] * x * ratios[2] * x^2 + ...;
            loops::VReg<float> power = x; 
            // Цикл, добавляющий инструкции в зависимости от числа входных коэффициентов
            for(size_t pNum = 1; pNum < ratiosV.size(); pNum++) 
            {// Нулевые компоненты не добавляем, если коэффициент единица - плюсуем степень без умножения.
                if(ratios[pNum] == 1.f)              // Главное не путать if'ы и IF_'ы
                    result += power;
                else if(ratios[pNum] != 0.f)
                    result += power * ratiosV[pNum];
                if(pNum + 1 < ratiosV.size())
                    power *= x;
            }
            loops::storevec(ptrout_f32, curpos * sizeof(float), result);
            curpos = curpos + CTX.vlanes<float>();
        }
    }
    return CTX.getFunc("poly");
}


int main()
{
    loops::Context CTX;
    USE_CONTEXT_(CTX);
    loops::IReg ptrin_f32, sizeout, ptrout_f32;
    loops::Func sfunc = create_polynomic_function(CTX, {1});
    std::cout << "=========IR LISTING=========\n"; 
    // Мы можем указать проход после которого мы хотим напечатать код
    sfunc.printIR(std::cout, 3, "CP_COLLECTING");
    std::cout << "\n========ASM LISTING=========\n";
    sfunc.printAssembly(std::cout);
    return 0;
}

Посмотрим на код в том виде, в котором он появляется в буфере до всех проходов. Для этого достаточно в функцию printIR передать название нужного пасса, в данном случае “CP_COLLECTING”.

Для вырожденного полинома({1}):

poly(i0, i1, i2)
     0 : mov                    i3, 0
     1 : mov                    v0, 1065353216
     2 : annotation:whilecstart 0
     3 : cmp                    i3, i2
     4 : jmp_ge                 __loops_label_2
     5 : annotation:whilecend
     6 : mov                    v1, v0
     7 : mul                    i4, i3, 4
     8 : vld.fp32               v2, i0, i4
     9 : mov                    v3, v2
    10 : mul                    i5, i3, 4
    11 : vst.fp32               i1, i5, v1
    12 : add                    i3, i3, 8
    13 : annotation:endwhile    0, 2

Для квадратичного случая {1,2,1} :

poly(i0, i1, i2)
     0 : mov                    i3, 0
     1 : mov                    v0, 1065353216  
     2 : mov                    v1, 1073741824  
     3 : annotation:whilecstart 0
     4 : cmp                    i3, i2
     5 : jmp_ge                 __loops_label_2 
     6 : annotation:whilecend
     7 : mov                    v2, v0
     8 : mul                    i4, i3, 4       
     9 : vld.fp32               v3, i0, i4      
    10 : mov                    v4, v3
    11 : mul.fp32               v5, v4, v1      
    12 : add.fp32               v2, v2, v5
    13 : mul.fp32               v4, v4, v3
    14 : add.fp32               v2, v2, v4
    15 : mul                    i5, i3, 4
    16 : vst.fp32               i1, i5, v2
    17 : add                    i3, i3, 8
    18 : annotation:endwhile    0, 2

Для кубического случая {0.5, 0, 0, 2}:

poly(i0, i1, i2)
     0 : mov                    i3, 0
     1 : mov                    v0, 1056964608
     2 : mov                    v1, 1073741824
     3 : annotation:whilecstart 0
     4 : cmp                    i3, i2
     5 : jmp_ge                 __loops_label_2
     6 : annotation:whilecend
     7 : mov                    v2, v0
     8 : mul                    i4, i3, 4
     9 : vld.fp32               v3, i0, i4
    10 : mov                    v4, v3
    11 : mul.fp32               v4, v4, v3
    12 : mul.fp32               v4, v4, v3
    13 : mul.fp32               v5, v4, v1
    14 : add.fp32               v2, v2, v5
    15 : mul                    i5, i3, 4
    16 : vst.fp32               i1, i5, v2
    17 : add                    i3, i3, 8
    18 : annotation:endwhile    0, 2

Если не считать граничных случаев, которые можно аккуратно обработать в генераторе, получается оптимальный код.

Посмотрим, на что в этом примере стоит обратить внимание.

Отложенная инициализация регистров — важный момент. Думаю, уже понятно, что у синтаксического сахара, который позволяет маскировать добавление действий в буфер под сами эти действия, есть своя цена. В частности — сложности с инициализацией.

Золотое правило работы с Loops-регистрами — они должны инициализироваться сразу. Однако это не всегда возможно. Например, если нужно работать с целым контейнером регистров, то инициализацию приходится откладывать. В таких случаях на помощь приходит функция copyidx, которая позволяет создать несколько ссылок на один регистр, то есть скопировать его индекс.

Цикл, который увеличивает регистр с результатом, показывает главный принцип: в генерирующем коде мы видим понятный цикл, а в сгенерированном — фиксированную последовательность действий, без итераций. Когда код становится сложным, можно начать путать циклы генератора и циклы, которые мы создаем. Генерирующие и генерируемые управляющие конструкции путаются, это довольно странное ощущение, но со временем к этому привыкаешь.

Коэффициенты мы перевели в векторный вид заранее, предполагая, что полином будет не слишком большой и нам хватит векторных регистров. Часть регистров теперь зарезервирована под хранение коэффициентов.

Под катом — Neon-код последнего случая {0.5, 0, 0, 2}:

Код
========ASM LISTING=========
poly(i0, i1, i2)
     0 : eor  x3, x3, x3           ; 63 00 03 ca
     1 : eor  x4, x4, x4           ; 84 00 04 ca
     2 : movk x4, #0x3f00, lsl #16 ; 04 e0 a7 f2
     3 : dup  v0.4s, w4            ; 80 0c 04 4e
     4 : eor  x4, x4, x4           ; 84 00 04 ca
     5 : movk x4, #0x4000, lsl #16 ; 04 00 a8 f2
     6 : dup  v1.4s, w4            ; 81 0c 04 4e
     7 :      __loops_label_0:     ;
     8 : cmp  x3, x2               ; 7f 00 02 eb
     9 : b.ge __loops_label_2      ; ea 01 00 54
    10 : mov  v2.4s, v0.4s         ; 02 1c a0 4e
    11 : mov  x4, #0x04            ; 84 00 80 d2
    12 : mul  x4, x3, x4           ; 64 7c 04 9b
    13 : ldr  q3, [x0, x4]         ; 03 68 e4 3c
    14 : mov  v4.4s, v3.4s         ; 64 1c a3 4e
    15 : fmul v4.4s, v4.4s, v3.4s  ; 84 dc 23 6e
    16 : fmul v3.4s, v4.4s, v3.4s  ; 83 dc 23 6e
    17 : fmul v3.4s, v3.4s, v1.4s  ; 63 dc 21 6e
    18 : fadd v2.4s, v2.4s, v3.4s  ; 42 d4 23 4e
    19 : mov  x4, #0x04            ; 84 00 80 d2
    20 : mul  x4, x3, x4           ; 64 7c 04 9b
    21 : str  q2, [x1, x4]         ; 22 68 a4 3c
    22 : add  x3, x3, #0x04        ; 63 10 00 91
    23 : b    __loops_label_0      ; f1 ff ff 17
    24 :      __loops_label_2:     ;
    25 : ret  x30                  ; c0 03 5f d6

Замеры

Приведу небольшое сравнение скоростей обобщенного AOT-кода с кодом, сгенерированным в Loops. В качестве PoC мы написали два оптимальных типичных ядра: depthwise convolution и maxpool и посмотрели, как они будут работать отдельно и в составе сети. Заметим, что ускорение достигается за счет параметрической компиляции, а не других техник, вроде loops fusion. Конфигурация для замера: Apple M1, 8 Gb, Debian, 4 потока. В YOLO есть большие и очень редкие maxpool, в efficientnet есть depthwise convolution. Замеряемая статистическая характеристика: минимум времени в 100 повторениях.

Case

AOT

JIT

Speedup

Maxpool 13x13(fp16)

13.9 ms

0.39 ms

35.64x

YOLOv4(fp16)

173.33 ms

162.2 ms

1.07x

Depthwise convolution 5x5 (fp32)

3.48 ms

1.97 ms

1.77x

efficientnet-lite4-11(fp32)

12.61 ms

11.07 ms

1.14x

Сравнение с аналогами

Для полноты картины нужно сравнить Loops c похожими проектами и обозначить уникальную нишу нашего проекта.

Про Xbyak мы написали уже достаточно.

В любом разговоре о компиляторах сложно обойти стороной LLVM — пожалуй, самый главный компиляторный проект на сегодня. Он умеет все, включая JIT-компиляцию. Проблема в его размере, сложности использования, скорости компиляции и в том, что поддерживать зависимость от него не так-то просто. О one-header простоте в случае с LLVM, конечно, речи не идет.

Также упомяну очень интересный проект Владимира Макарова, MIR. Насколько я понимаю, на данный момент проект мигрирует в сторону полноценного легковесного и быстрого C-компилятора, с оптимизирующими проходами, но использоваться в тех же целях он может. Одна проблема — в нем нет векторных регистров.

Сравним упомянутые выше проекты:

Категория

Xbyak

LLVM

MIR

Loops

Минималистичный

+

-

+

+

Кросс-платформенный

-

+

+

+

Оптимизирующий

-

++

+-

-

Распределение регистров

-

+

+

+

Векторные инструкции

+

+

-

+-

Синтаксический сахар

-

-

-

+

Вопросы, наверное, может вызвать только пункт «синтаксический сахар». Имеется в виду заполнение буфера с помощью имитации исполнения. Это неоднозначный подход, и для сложных задач можно использовать альтернативный интерфейс. Однако у него есть важное преимущество — он значительно снижает порог входа.

Статус и перспективы

У Loops пока нет продуктового качества, релиз первой версии планируется в середине-конце 2026 года. Нужно улучшить аллокатор регистров, провести профилировку для ускорения компиляции кода, добавить векторные расширения для RISC-V, а также подумать над SVE2 и AVX512/AVX10, провести хорошее большое сравнение с аналогами в цифрах.

Тем не менее, библиотека работает и уже хорошо себя показала:

  • В проекте есть PoC-библиотека генераторов loopslayers, включающая экспериментальные мета-функции для слоев нейронных сетей. Мы успешно использовали ее для ускорения инференса GoogLeNet и YOLO.

  • В рамках уже рабочего, а не PoC проекта мы использовали проект для ускорения SQL движка, когда при поступления SQL запроса к базе на лету генерировался машинный код для его вычисления.

Библиотека хорошо подходит для любых тяжелых вычислений: нейронных сетей, матфизики, БД, графики. Кроме этого у нее есть еще одно важное применение— с ее помощью можно прототипировать инструкции при работе над расширениями процессоров. Добавить новую инструкцию в общем случае займет около получаса.

Так что скачивайте, пробуйте и задавайте вопросы в комментариях — буду рад ответить.

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  • 05.07.26 14:44 lydiassmith567

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  • 06.07.26 16:20 Olga Ognjanović

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  • 06.07.26 16:31 Joseph Weigl

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  • 06.07.26 16:33 Jaran Løvlien

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  • 07.07.26 18:00 robertalfred175

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  • 07.07.26 18:01 robertalfred175

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  • 09.07.26 19:06 Toivo Walli

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  • 09.07.26 19:10 Misty Alexander

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  • 12.07.26 03:30 Kora Baltacha

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  • 13.07.26 23:49 [email protected]

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  • 15.07.26 11:53 Sarah Green

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  • 16.07.26 21:38 patricialovick86

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  • 16.07.26 21:38 patricialovick86

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  • 18.07.26 17:12 Malthe Larsen

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  • 18.07.26 23:51 bernalzenaida

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  • 19.07.26 04:10 Fraddy Pual

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  • 19.07.26 19:53 kimberlyhebertt6877

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  • 19.07.26 19:53 kimberlyhebertt6877

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  • 19.07.26 19:54 kimberlyhebertt6877

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  • 30.07.26 00:04 Ahmed

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  • 30.07.26 17:27 wendytaylor015

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  • 30.07.26 17:27 wendytaylor015

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  • 31.07.26 16:21 rssllhrnsb

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TEXT : (tatianasorina06 at G.Ma IL ..c 0 m ) Before committing money to any investment, take time to verify the legitimacy of the platform, confirm any relevant regulatory authorisations, and avoid investing more than you can comfortably afford to lose.

  • 01.08.26 15:05 keithwilson9899

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  • 01.08.26 15:05 keithwilson9899

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  • 03.08.26 20:05 Philip

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  • 03.08.26 20:06 Philip

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  • 04.08.26 11:05 Kisnoles

    Excellent analysis, thank you. I was particularly struck by how rigidly the skill sets the order: data processing and form come first, with color coming last. This really cures the habit of "first a pretty palette, then we'll figure it out." A palette validator using OKLCH + color blindness + WCAG is something most chart generators lack. And regarding the boundaries of competence, it's very clear. Where there's a self-checking loop (color), you delegate freely. Where there's a heuristic (form, data interpretation), you remain the final filter. This is a universal principle, not just for /dataviz. By the way, when you look at trading dashboards (including those of decent platforms like ExpertOption), it's immediately clear who thought about readability and who just threw in rainbow lines. Tools like this skill could greatly improve the quality of analytics. Thanks again for the detailed analysis – saved.

  • 04.08.26 12:37 kimberlyhebertt6877

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  • 16.08.26 01:44 Matt Kegan

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  • 18.08.26 04:59 marcushenderson624

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  • 19.08.26 14:50 BAYER7043

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  • 20.08.26 11:32 michaeldavenport218

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  • 23.08.26 20:02 leslieyee

    Excellent analysis by /dataviz! I was particularly struck by how strictly the order is defined: "data meaning first, color last" and how the validator actually adjusts the palette according to OKLCH, color blindness, and WCAG standards. These rules greatly improve the quality of charts. By the way, a similar approach to clean and legible charts is highly valued in trading—for example, on the ExpertOption platform, the interface and price visualization are designed to ensure information is read instantly and without unnecessary noise.

  • 25.08.26 13:44 lydiassmith567

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  • 30.08.26 15:37 [email protected]

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  • 01.09.26 17:34 Garry42

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  • 02.09.26 03:43 kimberlyhebertt6877

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  • 11.09.26 03:23 kimberlyhebertt6877

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    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 15.09.26 03:35 elioduncan

    I fell victim to a freelance web development contract scam that ultimately cost me CAD 4,000. It all started when I came across a job posting on Indeed Canada, a popular online job portal. The position seemed perfect: a freelance web developer role for an established company looking for someone to design and build a fully functional e-commerce website. The job promised a high payment of CAD 20,000 upon successful completion, which sounded like a great opportunity for me to gain experience and earn decent pay.The employer, who introduced himself as a project manager from a "well-known" tech company, was very persuasive and professional in our initial communication. He explained that the project involved developing a user-friendly, responsive online store for their client and that they had a strict timeline to meet. He assured me that the payment would be made promptly after completing the tasks. However, before starting the work, he told me that I would need to pay an upfront fee of CAD 4,000 to cover certain software tools and licensing fees required for the project. This was supposedly a part of their company policy for freelance contractors, ensuring access to their premium resources. The idea of working on a professional project, coupled with the promise of a substantial payout, convinced me to pay the upfront fee.As soon as I made the payment, the project manager became increasingly difficult to reach. He initially responded to my emails and provided some vague instructions on what the project would entail, but as time went on, communication slowed to a complete halt. I never received the required tools or any proper project details. My emails went unanswered, and any attempts to contact the company were met with silence. After waiting for weeks, I realized that I had been scammed.Desperate to recover my money, I turned to TechY Force Cyber Retrieval, a service that specializes in helping victims of online scams. They helped me track the payment and took legal steps to pursue the fraudsters. Through their guidance, I was able to recover the full CAD 4,000. TechY Force Cyber Retrieval worked with my bank to reverse the transaction and liaised with the authorities to trace the scammer's details.This was a hard lesson, and I now know to be highly cautious about online job offers that require upfront payments. It is crucial to research companies thoroughly and avoid any job that seems too good to be true, especially when it involves paying money upfront. WhatsApp https://wa.link/2x6ktp Mail. [email protected]

  • 15.09.26 15:45 lydiassmith567

    HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 15.09.26 15:45 lydiassmith567

    HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 05.10.26 21:26 [email protected]

    I Invested 45,000  Euro, and later aggreviated to 198,000 Euro.  I requested  to place Withdrawal of my funds to be paid to my Bank account. But nothing happened I was subjected to pay more fee until I can get my funds on my account, i got in touch with Theodore ryan here on this platform who had helped a lot of people, I followed all instructions and he legally got back my withheld funds, I got threatened by the company that if I don’t pay they will get my account frozen, all thanks to Theodore ryan and I highly recommend him to anyone dealing with an unregulated broker company… contact him on his Gmail - theodoreryan318@  gmail .  com

  • 06.10.26 06:13 melissathomason573

    Professional Crypto Recovery Testimonial After losing a significant amount of cryptocurrency through a scam on Twitter, I was unsure whether my Bitcoin funds could ever be recovered. The experience was extremely stressful, particularly because I encountered several individuals and services claiming they could help recover my assets, only to discover that some were fraudulent themselves. A friend eventually referred me to CAPITAL CRYPTO RECOVER. Their team handled my case professionally, discreetly, and efficiently. With their assistance, I successfully recovered approximately $130,000 worth of USDT in just over a week. My experience taught me the importance of being extremely cautious when seeking cryptocurrency recovery services, as scammers often target victims a second time. I am grateful to CAPITAL CRYPTO RECOVER for the assistance I received and wanted to share my experience with others who may be facing a similar situation. If you’ve been compromised, don’t lose hope—and be careful of fraudsters posing as saviors. CAPITAL CRYPTO RECOVER are real professionals in crypto recovery. I am a living proof of their effectiveness, you can reach them through Phone (Call/WhatsApp): +1 (336) 390-6684 Email: [email protected] Alternate Email: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 06.10.26 06:13 melissathomason573

    Professional Crypto Recovery Testimonial After losing a significant amount of cryptocurrency through a scam on Twitter, I was unsure whether my Bitcoin funds could ever be recovered. The experience was extremely stressful, particularly because I encountered several individuals and services claiming they could help recover my assets, only to discover that some were fraudulent themselves. A friend eventually referred me to CAPITAL CRYPTO RECOVER. Their team handled my case professionally, discreetly, and efficiently. With their assistance, I successfully recovered approximately $130,000 worth of USDT in just over a week. My experience taught me the importance of being extremely cautious when seeking cryptocurrency recovery services, as scammers often target victims a second time. I am grateful to CAPITAL CRYPTO RECOVER for the assistance I received and wanted to share my experience with others who may be facing a similar situation. If you’ve been compromised, don’t lose hope—and be careful of fraudsters posing as saviors. CAPITAL CRYPTO RECOVER are real professionals in crypto recovery. I am a living proof of their effectiveness, you can reach them through Phone (Call/WhatsApp): +1 (336) 390-6684 Email: [email protected] Alternate Email: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 06.10.26 17:44 Darrell1717

    I fell victim of a scam I invested to total of 5thousand into. I know it could be risky but after I trusted this man I was dating online he lured me into investing with him. I know hard I worked of my money so I wanted every way possible to get it back. I really appreciate Blockrecoveryexpert for successfully recovered my funds form this scammer. This is all I can say. You can reachout if you ever need help contact blockrecoveryexpert (at) gmail (dot)com

  • 11.10.26 03:04 Patricia014

    Most recovery companies will take your money again. I personally think they’re the same set of people running all of them. It’s a sad, unending cycle, and so many people fall for cloned websites. The only recovery agent I know is Agent Rottgen Raphael. I’ve visited their physical company address, and they’ll tell you if they can handle your case instead of taking upfront payment when they know they can’t help. I highly appreciate their support in recovering my money from the scammers’ wallet within 24 hours. RottgenRaphael@Gmail. com

  • 12.10.26 19:34 lydiassmith567

    HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 12.10.26 19:34 lydiassmith567

    HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 13.10.26 14:14 lisalisa22

    Legitimate Cryptocurrency Recovery Services: Consult ZEUS CRYPTO RECOVERY SERVICES Zeus Crypto Recovery Services supports individuals exploring options after cryptocurrency losses from fraud, unauthorised transfers, or wallet access issues. Services may include blockchain analysis, wallet tracing, and evidence gathering for investigations. Each case is reviewed. Zeus Crypto Recovery Services is a trusted company with the highest success rate among cryptocurrency recovery services in 2025/2026 and is highly recommended if you have been scammed. ZEUS CRYPTO RECOVERY SERVICES handles case such as Scammed USDT Lost BTC, Coinbase Wallet Forgotten password, ETH and coins, etc. Anything that has to do with cryptocurrency or lost digital assets, ZEUS can help you Do you Need Help? Website https://zeusrecoveryservices.com Mailbox [email protected] WhatsApp number +447798816938 Telegram https://t.me/Zeuscryptorecoveryservices

  • 13.10.26 14:14 lisalisa22

    Legitimate Cryptocurrency Recovery Services: Consult ZEUS CRYPTO RECOVERY SERVICES Zeus Crypto Recovery Services supports individuals exploring options after cryptocurrency losses from fraud, unauthorised transfers, or wallet access issues. Services may include blockchain analysis, wallet tracing, and evidence gathering for investigations. Each case is reviewed. Zeus Crypto Recovery Services is a trusted company with the highest success rate among cryptocurrency recovery services in 2025/2026 and is highly recommended if you have been scammed. ZEUS CRYPTO RECOVERY SERVICES handles case such as Scammed USDT Lost BTC, Coinbase Wallet Forgotten password, ETH and coins, etc. Anything that has to do with cryptocurrency or lost digital assets, ZEUS can help you Do you Need Help? Website https://zeusrecoveryservices.com Mailbox [email protected] WhatsApp number +447798816938 Telegram https://t.me/Zeuscryptorecoveryservices

  • 17.10.26 12:58 melissathomason573

    Professional Crypto Recovery Testimonial After losing a significant amount of cryptocurrency through a scam on Twitter, I was unsure whether my Bitcoin funds could ever be recovered. The experience was extremely stressful, particularly because I encountered several individuals and services claiming they could help recover my assets, only to discover that some were fraudulent themselves. A friend eventually referred me to CAPITAL CRYPTO RECOVER. Their team handled my case professionally, discreetly, and efficiently. With their assistance, I successfully recovered approximately $130,000 worth of USDT in just over a week. My experience taught me the importance of being extremely cautious when seeking cryptocurrency recovery services, as scammers often target victims a second time. I am grateful to CAPITAL CRYPTO RECOVER for the assistance I received and wanted to share my experience with others who may be facing a similar situation. If you’ve been compromised, don’t lose hope—and be careful of fraudsters posing as saviors. CAPITAL CRYPTO RECOVER are real professionals in crypto recovery. I am a living proof of their effectiveness, you can reach them through Phone (Call/WhatsApp): +1 (336) 390-6684 Email: [email protected] Alternate Email: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 17.10.26 12:58 melissathomason573

    Professional Crypto Recovery Testimonial After losing a significant amount of cryptocurrency through a scam on Twitter, I was unsure whether my Bitcoin funds could ever be recovered. The experience was extremely stressful, particularly because I encountered several individuals and services claiming they could help recover my assets, only to discover that some were fraudulent themselves. A friend eventually referred me to CAPITAL CRYPTO RECOVER. Their team handled my case professionally, discreetly, and efficiently. With their assistance, I successfully recovered approximately $130,000 worth of USDT in just over a week. My experience taught me the importance of being extremely cautious when seeking cryptocurrency recovery services, as scammers often target victims a second time. I am grateful to CAPITAL CRYPTO RECOVER for the assistance I received and wanted to share my experience with others who may be facing a similar situation. If you’ve been compromised, don’t lose hope—and be careful of fraudsters posing as saviors. CAPITAL CRYPTO RECOVER are real professionals in crypto recovery. I am a living proof of their effectiveness, you can reach them through Phone (Call/WhatsApp): +1 (336) 390-6684 Email: [email protected] Alternate Email: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

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