Experiences Accelerating MATLAB Systems Biology Applications Heart Wall Tracking Lukasz Szafaryn,...

Experiences Accelerating MATLAB Systems Biology

Applications

Heart Wall Tracking

Lukasz Szafaryn, Kevin SkadronUniversity of Virginia

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Outline

• MATLAB• Optimizations to MATLAB• GPU Acceleration with CUDA• Applications (Heart Wall Tracking and Myocyte Simulation)

– Problem– Algorithm– Optimization and performance– Lessons

• Conclusions • Future Research

MATLAB

• Convenient but inefficient programming language of choice for scientists- Interpreted language- Most of the existing code and libraries are single-threaded

• MATLAB Parallel Toolbox - understanding of parallel programming

• Jacket and GPUmat - large parallelism to justify overhead

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MATLAB contd.

• Interpreted language optimized by JIT compiler – 2x slower than C

• MATLAB Embedded Compiler has limited support – 1.2-1.4x slower than C

• MEX Interface to link C code- translating to C - many functions written from scratch- no support for convenient OpenMP standard, need to use

thread libraries

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Acceleration

1. Translation: - convert MATLAB to C

2. Parallelization:– C for multi-core CPU– CUDA for GPU

Experimental Setup

– CPU: 3.2 GHz quad-core Intel Core 2 Extreme– GPU: NVIDIA GeForce GTX 280 (PCIe 2.0)– MS Windows, MS C Compiler

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Allocate GPU memory

Transfer inputs

Launch kernel

Return to CPU

Transfer results

Free GPU memory

C Program

CUDA Kernel

CPU GPU

Acceleration with GPU (CUDA)

Entire Heart Wall Application

read first frame from input file, display

image

[0.533 s] [0.28 %]

crop image, display image

[0.089 s] [0.05 %]

SRAD, display image

[3.224 s] [1.72 %]

detect edges, display image

[0.448 s] [0.24 %]

morphological transformation, display image

[0.275 s] [0.15 %]

dilate image, display image

[0.285 s] [0.15 %]

inner and outer ellipse parameter

setup

[0.001 s] [0.00 %]

create ellipse sample points

[0.726 s] [0.39 %]

track movement of sample points in all

frames

[129.276 s] [68.99 %]

display movement of sample points

through frames

[36.63 s] [19.55 %]

save outputs into file

[0.035 s] [0.02 %]

Hough Search, display images

[15.872 s] [8.47 %]

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Heart Wall TrackingDescription

• Speed and shape of contractions provides important information about body’s response to stimulus

• Measured by tracking inner and outer heart walls through multiple frames

Input OutputTracking

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Heart Wall TrackingAlgorithm

• Processing 20 inner and 30 outer heart wall points, total 50 points (TLP)

• Processing of each point - sequence of operations on the surrounding area and template (DLP)

Update templates

Read next frame

Track inner point

Track outer point

Save point locations

20

30

10 # of frames / 10

…

time

task-level parallelism (TLP)

1 2 3 4 50

data-level parallelism (DLP)

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Heart Wall TrackingPerformance

• Times reported for processing of 300 frames (10s of ultrasound recording)

1.21x

2.71x

10.86x

2.37x

3.39x

5.94x 6.29x

33.20x 36.89x 41.23x

Porting Tradeoffs

Convenience Performance

Developing modular code

• replacing each MATLAB function with equivalent parallelized GPU function

• common routines can be possibly reused in other applications

Hiding specific aspects of GPU programming inside each module

• each module sets up its own GPU execution parameters

• each module performs its own I/O with GPU transparently

Restructuring and combining code

•writing code based on algorithm tasks rather than MATLAB statements

•overlap parallel (often unrelated) tasks by executing them in the same kernel call

Exposing specific aspects of GPU programming

•doing more work inside each GPU kernel call to fully exploit parallelism and avoid overhead

•performing I/O with GPU manually to eliminate redundant data transfers

multiple

add,sub, mul, div

---------- SYNC ----------

Reduction

---------- SYNC ----------

multiple

add,sub, mul, div

---------- SYNC ----------

convolution

multiple

add,sub, mul, div

---------- SYNC ----------

Reduction

---------- SYNC ----------

multiple

add,sub, mul, div

---------- SYNC ----------

convolution

multiple

add,sub, mul, div

---------- SYNC ----------

Reduction

---------- SYNC ----------

multiple

add,sub, mul, div

---------- SYNC ----------

convolution

multiple

add,sub, mul, div

---------- SYNC ----------

Reduction

---------- SYNC ----------

multiple

add,sub, mul, div

---------- SYNC ----------

convolution

multiple

add,sub, mul, div

---------- SYNC ----------

Reduction

---------- SYNC ----------

multiple

add,sub, mul, div

---------- SYNC ----------

convolution

multiple

add,sub, mul, div

---------- SYNC ----------

Reduction

---------- SYNC ----------

multiple

add,sub, mul, div

---------- SYNC ----------

convolution

multiple

add,sub, mul, div

---------- SYNC ----------

Reduction

---------- SYNC ----------

multiple

add,sub, mul, div

---------- SYNC ----------

convolution

tim eSMs

outer heart points

(30)

inner heart points

(20)

…

…

1 2 3 30…

global SYNC

Frame 1

Frame 2

…

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Heart Wall TrackingLessons

• Typical MATLAB code written by a scientist has room for optimization – 1.3x

• Conversion to C requires significant coding effort• Selective offloading results in multiple CPU-GPU data

transfer overheads• Iterative codes require merging kernels and reusing

variables to avoid overhead• CUDA libraries cannot be used as a part of GPU code• Good performance - significant changes to the structure of

code, difficult for a scientist to understand

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Conclusions

• Limited availability of C libraries necessitates time consuming coding

• Many systems biology applications (even those with limited parallelism) benefit from GPU

• GPU overheads are significant (should be eliminated in new CPU-GPU architectures)

• Real-time processing feasible in near future• Ultimately, acceleration of applications should be

automated!

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Future Research

• Automatic acceleration with the use of compiler - via use of architecture-specific libraries- via compiling for target architecture

• Merging of workloads- based on resource needs- based on dependency

• Acceleration with alternative architectures- well suited for fine-grained parallelism

- esp. FPGA

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Acknowledgements

• Funding provided by:– NSF grant IIS-0612049– SRC grant 1607.001

• Equipment donated by NVIDIA

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Software

Source code will be soon available at:http://lava.cs.virginia.edu/wiki/rodinia

Questions

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Backup

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