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AcousticsGPU: GPU-ускоренное моделирование акустики помещений

AcousticsGPU — это библиотека для высокопроизводительного моделирования распространения звука в помещениях с использованием GPU-ускорения на базе PyTorch. Поддерживает два метода: трассировку лучей (Ray Tracing) и метод зеркальных источников (Image Source), а также учитывает частотные характеристики материалов.


Основные возможности

Возможность Описание
GPU-ускорение Ускорение в 10–100+ раз по сравнению с CPU за счёт использования PyTorch и CUDA
10 частотных полос Октавные полосы от 31.5 Гц до 16 кГц (31.5, 63, 125, 250, 500, 1000, 2000, 4000, 8000, 16000 Гц)
8+ реальных материалов Коэффициенты поглощения из таблицы компании Фронт Акустик
Два метода моделирования Ray Tracing — для поздней реверберации, Image Source — для точных ранних отражений
Визуализация Энергетические карты для каждой частоты, частотные характеристики в точках микрофонов, гистограммы распределения энергии

Установка

Стандартная установка

pip install acoustics-gpu

Установка зависимостей

pip install torch numpy matplotlib scipy

Системные требования

Для работы с GPU ускорением: видеокарта с поддержкой CUDA

Необходимые библиотеки

Для работы с AcousticsGPU необходимы следующие зависимости:

Библиотека Версия Назначение
PyTorch ≥ 2.0.0 GPU-вычисления, тензорные операции
NumPy ≥ 1.24.0 Численные расчёты
Matplotlib ≥ 3.7.0 Визуализация (энергетические карты, графики)
SciPy ≥ 1.10.0 Аппроксимация и дополнительные вычисления

Результаты производительности

Тестирование на конфигурации: Intel Xeon E3-1230 (4 ядра, 8 потоков) vs NVIDIA GTX 1080 (8 GB).

Метод CPU (с) GPU (с) Ускорение
Ray Tracing (1000 лучей) 12.51 0.85 14.7×
Ray Tracing (10000 лучей) 117.53 0.90 130.6×
Ray Tracing (100000 лучей) 1368.85 2.15 636.7×
Image Source (порядок 50) 14.01 8.965 1.56×

Ссылки

Таблица коэффициентов звукопоглощения

Репозиторий проекта


Проект в стадии разработки

Metadata

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