applying gpu and posix thread technologies in massive remote sensing image data processing by: group...

Applying GPU and POSIX Thread Technologies in Massive Remote Sensing Image Data Processing

By: Group 17 King Mongkut's Institute of Technology Ladkrabang

High Resolution graphics not Smoother?

1. Buy better Graphics Card

2. Install newest Driver

<< This Paper >>3. Better GPU & CPU Management!!

Why

This Paper?

GPU?POSIX?

- IEEE Thread Standard- Thread API for Many OS

- Pthread

= GPU + Thread Technique

Apply GPU & POSIX Thread

What

Overview

1.Solve?2.Technique3.Process4.Real App

1.Solve?

1.Solve?Problem: Remote sensing is Massive!

High Resolution = Slow Processing = Can’t Real Time

Goal: Processing image faster

2.Technique

2.Technique (1/4)

1. CUDA

2. Block and Tile

3. Dual-parallel Processing

2.Technique (2/4)1.CUDA

(Compute Unified Device Architecture)

Is: Parallel Architecture for using GPU

Use by: NVIDIA GPUs since 2007 e.g. GeForce GT 420*

2.Technique (3/4)2. Block and Tile

What is Block and Tile

● Image data share to block of I/O

● If block larger than gpu , have problem

2.Technique (4/4)3. Dual-parallel Processing

POSIX threads are applied to perform the I/O step and the GPU processing step simultaneously.

3.Process

2.Process(1/2)Main

Init of CUDA

Two Thread are set Up

Process of I/O Thread

And GPU Process

pthread_join

Return

3.Process (2/2)

4.Real App

EXPERIMENT

Database PostgreSQL

4.Real App

CPU 1 ( AMD Athon 3000+ , 1.81GHz ) CPU 2 ( Intel Core i3 530 , 2.93 GHz ) GPU ( Nvidia Geforce GT240 , 512M )

1 2 3

EXPERIMENT

4.Real App1 2 3

4

56

Image1

Image2

Image3

Image4

Origital Image

ProcessesProcessed Image

Input

Process

Output

4.Real AppOriginal image Processed image

4.Real App

IMAGE 1

IMAGE 2

IMAGE 4

IMAGE 3

4.Real AppImage123821 * 21758

Image212000 * 10961

Image36000 * 5480

Image43000 * 2740

- PostgreSQL is a powerful- Object-Relational database system- More Than MySQL

- CUDA technology directly to process the image blocks stored in database rapidly with SQL statements

4.Real AppDatabase PostgreSQL

4.Real App

1.Use2.Download3.Install

Database PostgreSQL

4.Real App1.Use PostgreSQL

PostPIC PostGIS

SELECT *FROM images WHERE where date(the_img) > '2009-01-01'::date and size(the_img) > 1600;

- Support geographic objects to PostgreSQL

- Simple Features Specification for SQL

- Types and Functions

- Download : http://www.postgresql.org/download

4.Real App2. Download

4.Real App3.1 Install PostgreSQL

1 2 3

4 5 6

4.Real App3.1 Install PostgreSQL

7 8 9

10 11 12

4.Real App 3.2 Install PostGIS

16 17 18

13 14 15

4.Real App3.2 Install PostGIS

19 20

4.Real App

PostgreSQL 9.3

PostGIS 2.1

3.3 Install Complete

Conclusion!

Conclusion1.Solve?= Processing

faster

3.Process= Flowchart

2.Technique= Dual-Process

4.Real App= Speed up

PostPIC PostGIS

PostgreSQL

Conclusion

CPU GPU

Block and Tile

Applying GPU and POSIX Thread Technologies in Massive Remote Sensing Image Data Processing

By: Group 17By: Group 17

THANK YOU !!

Applying GPU and POSIX Thread Technologies in Massive Remote Sensing Image Data Processing

Extended!!

1. Technique (Extended)

2. Process (Extended)

1. Technique X

1. Technique X

1.CUDA X

2.Block and Tile X

3.Dual-parallel Processing X

1. Technique X1.CUDA (Extended)

1. Technique X

1.1 Architecture

Confix GPU

API

1.CUDA (Extended)

1. Technique X

1. Language integration-Level = e.g. C, C++, …

2. Device-Level = e.g. Assembly

1.1 Architecture1.CUDA (Extended)

-> API

1. Technique X

1.2 Parallel

CPU GPU

1.CUDA (Extended)

1. Technique X

- A resource management program in OS

-> CPU

- Allow copy backhelp other compute

1.2 Parallel

1.CUDA (Extended)

1. Technique X

Partition of imageData in GPU

Partition for a kernel

Processing data in a Block

-> GPU1.2 Parallel

1.CUDA (Extended)

1. Technique X2. Block and Tile

(Extended)

1. Technique X2. Block and Tile

(Extended)

• The block is the I/O unit between sensing image data and the system memory

1. Technique XBlock and Tile(Cont)

• We tested the I/O performance with different block sizes

• Results show that the I/O performance of sensing image data declines

• The tile is used to transfer data between the system memory and GPU memory

• Block , the I/O unit between image and memory

• Tile , the I/O unit between memory and GPU memory

1. Technique XBlock and Tile(Cont)

• The block in system memory is partitioned into multiple tiles in this approach

• The tile is used to transfer data between the system memory and GPU memory

1. Technique X2. Dual-parallel

Processing (Extended)

3. Dual-parallel Processing (Extended)

1. Technique X

• Using buffer pool technology.

• I/O step and the GPU processing step are independent from each other.

• Simple and easy to implement if buffer size is equal to block size.

1.Technique X

• Used for the I/O task between the image data and the system memory

• Responsible for delivering the buffers from the buffer pool to the GPU memory and processing

3. Dual-parallel Processing (Extended)

1.Technique X3. Dual-parallel Processing (Extended)

1.Technique X

• From the micro perspective, image data are processed by hundreds of execution units simultaneously in GPU

• From the macro perspective, the I/O operation and the processing operation are performed simultaneously by POSIX threads.

3. Dual-parallel Processing (Extended)

1.Technique X3. Dual-parallel Processing (Extended)

2.Process X

2.Process(1/3)• Begins with function main after that Initialization of CUDA

environment• Group of buffers are set up• Two POSIX threads are created• One is used to input and output the remote sensing image data• Two is used to responsible for processing the buffers read by

the first thread• For example

• Ready_to_read• The I/O thread should read a block from image data

to current buffer• Ready_to_process

• Buffer is ready to be process by GPU• Ready_to_process

• The I/O thread should write a block to image data

2.Process(2/3)• Two global variables are declared to record the execution state

of each thread• For example

• Is_IO_Over• is true, it means all the work of I/O thread is finished

• Is_Process_Over• is true, it means all the work of responsible for

processing the buffers is finished• “pthread_join” would be called to terminate that thread• Two threads are finished then all the remote sensing image

data are completely processed and the program will be end by calling “return”