ROOM ACOUSTICS SIMULATION USING GRAPHICS PROCESSING UNIT

GROUP ID:16PROJECT GUIDE: Prof. Sumeet Harale

Presented By:Chirag Batra

Outlines• What is Room Acoustics• First Order Reflection and Diffusion• Problem Statement• Goals and Objectives• Purpose of Room Acoustics• Introduction to CUDA• Why Room Acoustics Simulation with CUDA• Introduction to GPU• Difference between CPU and GPU• Methods To Implement Room Acoustics Simulation• TLM(Transmission Line Matrix)• Conclusion

What is

Room Acoustic

• The way to describe how sound propagates in a room• It will react differently to different frequencies of sound when it

strikes to different objects

Figure(1):3d Acoustics in a closed room

First Order Reflection• The first locations where sound reflects between

our ears and the speakers

Diffusion• net movement of molecules• from a region of high concentration to a region

of low concentration

Room Acoustic SimulationFor what purpose?

• In video game…• So that we can have the acoustic spatial perception

• For hearing the simulated sound given the room map

• Used in Aerospace laboratory to calculate noise produced by Aero plane

Problem Statement

• To Show The Simulation of a Sound Wave propagating in a room and to implement TLM for Room Acoustic Simulation.

Goals • To explain & implement how sound wave propagation is modelled in a room using

GPU

Objectives • To Learn OPENGL • To learn CUDA Programming • To Understand Acoustics Simulation

Introduction To CUDA

• CUDA is a parallel computing platform and application programming interface (API) model created by NVIDIA.• Allows software developers to use a CUDA-enabled GPU for general purpose

processing – known as GPGPU.

Room Acoustic Simulation why with CUDA

• Simulation of propagation because the pressure of each spatial point can be parallel computed•DCT(Discrete cosine transform) implementation

based on cuFFT(CUDA Fast Fourier Transform)

What is GPU?

• GPU-accelerated computing is the use of a GPU together with a CPU to accelerate scientific, analytics, engineering, consumer, and enterprise applications. • Used in power energy-efficient datacenters in government labs, universities,

enterprises, and small-and-medium businesses around the world.

Figure(5):GPU Acceleration

Difference between CPU and GPU • CPU is the brain of the computer the GPU is only meant to complement it.• GPUs are specialized and cannot replace the function of a CPU.• CPUs can perform the functions of a GPU but at a much slower speed.• GPUs can rival CPUs in transistor count.• GPUs can also work in tandem just like the CPU’s multi core capability.

Implementation of Room Acoustics Simulation

Transmission Line Matrix

• Space and time discretizing method• Computation of electromagnetic fields• Based on the analogy between the electromagnetic field and a

mesh of transmission lines• Allows the computation of complex three-dimensional

electromagnetic structures• Is a numerical method used to simulate wave propagation

Transmission Line Matrix Implementation

• Based on Huygens Principle, “Each point on a wave front acts as a source of secondary wavelets At a later time, the envelope of the leading edges of the wavelets forms the new wave front”• Implementation of the grid of nodes , wave propagation was made in C++ and OpenGL• Application is divided into several classes• Each node is represented on the screen as a two by two pixel square that has a color

that depends on the amount of pressure currently present in the node• Each node, two vectors, represent the flow of pressure between the nodes.• One incoming pressure one outgoing• The program uses the TLM methods to distribute all the pressure to the neighboring

nodes.

Transmission Line Matrix Implementation

• A sinusoidal source , at a node in the room, adding pressure to the incoming vector of this node at each time instant.

• Without a source of some kind there is no pressure to be scattered between the nodes.

• Virtual Microphones used for pressure detection

Kernel Code• Function callable from Host and executed on CUDA device• Simultaneously by threads in parallel• In this project we have used “kernel.cuh” to process CUDA • To call a kernel involves specifying the name of the kernel plus an execution

configuration• Thread allocated by kernel is equal to number of nodes in a block

Working of TLM

A source is added

Scattering of node

Simulation and result

Room

Source

Simulation

GPU vs CPUPressure on Nodes CPU(time in seconds if

number of nodes simulated are same)

GPU(time in seconds if number of nodes simulated are same)

0.454649 2 0.1

0.309246 4 0.2

0.0334211 6 0.3

0.49468 8 0.4

0.305479 10 0.5

Result

Comparison of CPU and GPU Performance

Software Requirements• Visual Studio 13.0• C/C++ Toolkit• OpenGL Libraries• CUDA Toolkit

Conclusion

• The programmability, processing and execution speed of GPU is faster as compared to CPU.• GPU is not only used for graphics related problems but also for

General Purpose problems(GPGPU) like Room Acoustics Simulation.• TLM is the efficient method used for Room Acoustics Simulation.

THANK YOU