Implementation and Optimization of SIFT on a OpenCL GPU 6.869-6.338 Final Project 5/5/2010 Guy-Richard Kayombya.

Implementation and Optimization of SIFT on a OpenCL GPU

6.869-6.338 Final Project5/5/2010

Guy-Richard Kayombya

Overview

Motivation Quick Intro to OpenCL Implementation Results

Motivation

Learn OpenCL Adapt the SIFT algorithm to yet another

parallel architecture Maybe achieve some speedup

Quick Intro to OpenCL

New Standard from Khronos for HeterogeneousParallel Computing (v1.0 Released Dec 2008) Initiated by Apple

Open and royalty free Cross-Vendor and Cross-Platform Make use of all available processing entities

CPUs, GPUs and other Processors Scales from Embedded to HPC solutions

Quick Intro to OpenCL(2)

One Host, Multiple Devices

Each Device has multiple Compute Units

Each Compute Unit has multiple Processing Elements

E.g: GT200 has 30 Compute units/Streaming processors and 8 Processing Elements/Scalar SIMD processors = 240 Processing elements

Quick Intro to OpenCL(3)

NDRange = size of the problem to solve 1D or 2D

Work-Group = block of work-items Work-Item ~ lightweight thread

Quick Intro to OpenCL(4)

Global : per device Local : per Work-

Group Private : Per Work-

Item

Quick Intro to OpenCL(4)

__kernel void vec_inc ( __global float *a, __global const float b){ int gid = get_global_id(0); a[gid] = a[gid] + b;}

Implementation

Abstraction Layer (85 %) Gaussian/DoG Pyramids (100 % semi-

optimized) Keypoint Detection (95 % - Naive) Keypoint Refinement (90 % - Naive) Orientation Assignment (10 %) Descriptor generation(0 %)

Abstraction Layer

Problem : Host device code is cumbersome Requires dozens of repetitive lines to setup device

contexts kernels,buffers,etc... Solution: OpenCL wrapper

Simplifies creation and management of hybrid Host/Client buffers and execution of kernels

Facilitates transition from serial to parallel execution Host/Client Synchronization

Memory management issues still need to be fixed

Gaussian Pyramid

Separable convolution 2 1D filters

Indirect filtering to reduce kernel size sigma_diff = sqrt(sig_dst^2 – sigma_src^2)

Use convolutionSeperable() provided by Nvidia for efficient 2D seperable convolution on the GPU

Keypoint detection

Each pixels is processed by one work item independently No state sharing Worst case 26 comparisons / per work Item

Keypoint Refinement

Each Keypoint is processed independently by one work item

Kernel is a slightly modified version of the keypoint refinement Matlab Mex module by Vedaldi

Preliminary Results (Time)All the measurements are performed on an input image of size 512x512.

Gaussian Filtering (sigma 4.1):• Vedaldi Matlab CPU = 0.19s – 100 %• Naïve C++ CPU = 0.33s – 57%• GPU = 0.0094s – 2000 %• GPU with data transfer = 0.0133s – 1400 %

Extrema Detection (octave 0 of pyramid):• Vedaldi Matlab CPU = 0.179 s – 100 %• GPU = 0.035725s – 500 %

Keypoint Refinement (octave 0 of pyramid):• Vedaldi Matlab CPU = 0.004 s – 100 %• GPU = 0.0689s – 6 %

Preliminary Results(performance)

Refined Keypoints for octave 0

Blue: Matlab implementation

Red: OpenCL Green: Common 85% Correspondence