Simulation of Microwave Induced Thermoacoustic Imaging Model

using GPU

Nilangshu BidyantaRamaprasad KulkarniECE 562 Term Project

Project Overview

• Introduction

Figure: Schematic model (a) side view (the longer transverse dimension of the waveguide lies on the x-axis.) and (b) top view. Figure taken from reference [1].

Project Overview

• Motivation• Hypothesis• Project goals

Methodology

• Implementation of PSTD paper [Ref 1]

• Existing Matlab code, wrote C++ code using CUDA

Methodology• Code analysis: A simplified version of the PSTD implementation can

be written as follows:for(i=0; i<2000; i++){

Bx[i] = a*(Bx[i-1] + Bx[i-2] + Bx[i-3]) – b*myFunc(Cx[i-1], Cx[i-2], Cx[i-3]);

Bx[i-3] = Bx[i-2]; Bx[i-2] = Bx[i-1]; Bx[i-1] = Bx[i];..

Cx[i] = a*(Cx[i-1] + Cx[i-2] + Cx[i-3]) – b*myFunc(Bx[i-1], Bx[i-2], Bx[i-3]);Cx[i-3] = Cx[i-2]; Cx[i-2] = Cx[i-1]; Cx[i-1] = Cx[i];

.

.

}

• In each iteration, the set of four equations for Bx is repeated for By and Bz and similarly for Cy and Cz. Also, the function ‘myFunc’ has FFT and IFFT computations.

Results and Discussions

• Observed speedup w.r.t Matlab code– 11.84x speedup for GPU vs Matlab (2 core)– 2.68x speedup for GPU vs Matlab (6 core)

• Due to significant amount of data transfers, the GPU speedup vs Mutlicore machine is not significant.

• Matlab was run on a 6-core Intel i7 machine and 2-core Intel Core 2 Duo machine runningWindows 7, 64-bit OS.

• GPU code was run on a 4-core Intel Xeon machine running Linux 64-bit OS.

Results and Discussions

0 20 40 60 80 100 120 140 160 180 2001

10

100

1000

10000

100000

0

5

10

15

20

25

Matrix Size vs. Computation Time

2 Core

6 core

GPU time

GPU vs 2 core

GPU vs 6 core

Matrix Size

Com

puta

tion

Tim

e (m

s)

Results and Discussions

0 1000000 2000000 3000000 4000000 5000000 6000000 7000000 8000000 90000000

2000

4000

6000

8000

10000

12000

14000

16000

0

5

10

15

20

25

Matrix Size vs. Computation Time

2 core

6 core

GPU time

GPU vs 2 core

GPU vs 6 core

# of Matrix Elements

Com

puta

tion

time

(ms)

Results and Discussions

• Limitation for further speedup is – Loop-carried dependency– Data intensive rather than computation intensive– Memory requirement increases exponentially– For 320x320x320 matrix, out of 3000ms for each

iteration, around 2000ms is taken for data transfer– Data transfer time is significant compared to

computation time.

Lessons learnt

• Access to entire code, potential for further speedup

• Serial computation on CPU can be improved by parallelizing the code to utilize multiple cores.

References• Wang, X.; Bauer, D. R.; Witte, R.; Xin, H., "Microwave-Induced

Thermoacoustic Imaging Model for Potential Breast Cancer Detection," IEEE Transactions on Biomedical Engineering, vol.59, no.10, pp.2782-2791, Oct. 2012. doi: 10.1109/TBME.2012.2210218.

• NVIDIA Corp. 2007. NVIDIA CUDA Compute Unified device architecture programming guide 1.1. Technical report, NVIDA, Santa Clara, CA

THANK YOU

QUESTIONS