A Framework for Applying GPU Parallel Computing to

an Agricultural Model Kei TANAKA

Agricultural Research Center (ARC) National Agriculture and Food Research

Organization (NARO), Japan

Contents

• Background and Objectives

• GPGPU

• Aparapi

• Comparison of the execution time of DVI calculation of SIMRIW

– Normal, Multithreading, GPU

• Future works

• Summary

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Background • Tool for Predicting the Possibility of

Rice Cultivation (2009)

• For the calculation of cultivation possibility data

– 15,000 points (1 degree grid)

– 8 cultivars 6 conditions (air-temp., CO2) 17,520 times/points 365 days of transplanting dates

– Core i7 860

– 7 days by normal, 2 days by multithreading

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Objectives

• Shortening of the computing time to generate the cultivating possibility data

• Number or target crops increase Points (-> 0.1 degree grid) for more detailed Parameter set simulation

• Concurrent Programming

– Multithreading by CPU -> GPGPU

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GPGPU

• General-Purpose Computing on Graphics Processing Units (GPGPU)

• Single Instruction Multiple Data (SIMD) – Executing the same processing at a time for different

data by one instruction

– Suitable fields: physics, fluid calculation, climatic simulation, image and speech processing

• Calculation of cultivation possibility data – A crop model has a lot of conditional jump instructions

– A lot of same calculations with different meteorological data and parameter values for a crop growth model

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Development Environment for GPGPU

• Compute Unified Device Architecture (CUDA)

– for NVIDIA GPU

• ATI Stream SDK

– for AMD GPU

• Open Computing Language (OpenCL)

– Framework that can be executed on different type GPU

– Supported by many companies

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GPGPU Programming with Java

• Development in Java

– Java Agricultural Model Framework (JAMF)

– Crop models implemented with JAMF

• Java Libraries to use OpenCL – Aparapi, Java bindings for OpenCL (JOCL), JavaCL, jOpenCL

• Aparapi

– All codes including the kernel function (parallel computation part) can be written in Java

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Aparapi (code)

• The parallel computation part is described in the run method of the Kernel class, and it will be executed by the execute method (as well as Thread class)

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final int[] square = new int[size]; final int[] in = new int[size]; // populating in[0..size] omitted

for (int i=0; i<size; i++){ square[i] = in[i] * in[i]; }

new Kernel(){ @Override public void run(){ int i = getGlobalID(); square[i] = in[i] * in[i]; } }.execute(size);

Aparapi (execution)

• Executable in various environments by the same code (write once, run anywhere) – When the following conditions are satisfied -> GPU

mode Otherwise -> Java Thread Pool (JTP) mode

– Installed the compatible GPU and OpenCL driver

– Convertible from the Java parallel code to the OpenCL code

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Java code javac

Java bytecode

java

OpenCL code execute

GPU mode

JTP mode

Comparison of the Execution Time DVI Calculation of SIMRIW

• GeForce GTS 250 （2009）

• Test Program: DVI calculation of SIMRIW

– Comparison among Normal, JTP, GPU mode

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10

100

1000

10000

100 1000 10000 100000 1000000

Exe

cu

tio

n tim

e（m

s）

The number of the crop model execution（times）

Comparison of the Execution Time (DVI calculation of SIMRIW)

Normal

JTP

GPU

GPU+Conversion

4.5 times faster

28 times faster

0.75s

3.35s

20.0s

Future Works

• Effect of GPGPU when the entire SIMRIW and other crop models are calculated

• Effect when latest GPU (GTX670) is installed

• Applying the code tuning for GPGPU

– In this presentation, only Aparapi was applied to Java code

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Summary

• Aparapi was adopted to develop GPGPU program using only Java

• GPGPU was effective for the crop growth model calculation with the fairly large frequency – 1 million execution conversion time incl. – Compared with Normal mode 28 times – Compared with JTP mode 4.5 times

• Because the cost of GPU for 3D game is hundreds US$, it is excellent in cost-effectiveness

• A further performance improvement can be expected by the latest GPU, and code tuning

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